Psych 119F Study Guide Midterm #2
Psych 119F Study Guide Midterm #2 Psychology 119F
Popular in Neural Basis of Behavior
Popular in Psychlogy
This 187 page Study Guide was uploaded by Marissa Mayeda on Wednesday March 4, 2015. The Study Guide belongs to Psychology 119F at University of California - Los Angeles taught by Blair in Winter2015. Since its upload, it has received 290 views. For similar materials see Neural Basis of Behavior in Psychlogy at University of California - Los Angeles.
Reviews for Psych 119F Study Guide Midterm #2
Report this Material
What is Karma?
Karma is the currency of StudySoup.
You can buy or earn more Karma at anytime and redeem it for class notes, study guides, flashcards, and more!
Date Created: 03/04/15
Place cells A firing rate code for space Place cells appear to store a firing rate code for space in much the same way that head direction cells store a firing rate code for direction But DO they D Finite domain of spatial R Firing rate space locations that an animal f D gt R with n dimensions can occupy for n place cells 5 GOOOQQQOOOOOOQ 0 U39 I o I The Morris Water Maze Rats are placed in a tank of water in which a hidden platform is located Rat must learn to swim to the hidden platform to escape from the water BEFORE LEARNlNG AFTER LEARNING Hidden platform From Morris et al 1986 no 7 H II 39 mo E A ll Ki 39 v I Iquot I ha I IIquot II 3 i I u 39I II I 0 Q I It 4039 1 In 39 Q 3 I h 20 5 39n 39 g 1 39 I I I I I I I l I I I 39 l 9 I t uc vigai HIPPOCAMPAL LESION NORMAL Hippocampal lesions impair the water maze task 0 Over several days of training normal rats learn to find the hidden platform quickly from any location in the pool Rats with hippocampal lesions are impaired at learning to find the hidden platform Spatial learning in the water maze may require a form of quotdeclarative memory Probe Trials in the Water Maze HIPPOCAM PAL NORMAL LESION B 40 40 Closest way to test role of hippocampus Training for declara tive memory in roddents 30p Opposite Right Tim39e s 2039 When the platform is removed m normal rats searches mostly in the quadrant of the maze where the platform used to be located but hippocampal lesioned rats search 10 3 4 equallyin aquadrants they don t 5 3 3 A 3 o 3 know where to look 3 g Y Y A 2D attractor network The population of place cells can be a conceptualized as a sheet of neurons We can imagine a bump of activity that shifts across the sheet in correspondence with the rat s movements through the environment Could this sheet of place cells perform linear or translational path integration in much the same way that the ring attractor performs angular path integration If so then place cells may store a quotcognitive map that allows the rat to keep track of where it is within a familiar environment encode rat position on floor bump move across sheet according to rat l movingalong the floor I Ll fl l f f i t sigrfr iff f fq t 39i 39if39i f h Ef iu1 i iii1 4 E r L E k g LF E L 2 i ur m iiil i a arr 4 in r ii in Attractor states map spatial locations into firing rate space The peak shaped attractor state can sit at different locations on the sheet to encode different locations in space D Finite domain of spatial locations that an f R2 RN animal can occupy NEURAL CODE quot39 39quot H 39 39 39quot 39 f r39a r 209 62629 39 r v Aquot o o o 6 63 v fons e99 6 O 39o v39 6 39 7 0 6 c v v69 o5 quot99quotquotquot99 09 K4 9 9 0 9 so 0 360 o Hollow 39 plot firing rate of each neuron where each firing rate is a dimension R Firing rates of N place cells in the hippocampus FIRING RATE SPACE only points on this surface encode spatial locations Three problems with attractor models of place cells 1 The remapping problem Unlike head direction cells which maintain identical adjacency relations with one another at all times even during sleep place cells scramble their adjacency relationships with one another remap when the environment changes 2 The temporal coding problem Attractor models are good for storing and updating via path integration a firing rate code for space but superimposed upon their firing rate code place cells also store a temporal code phase precession 3 The unbounded domain problem Unlike directional heading azimuth which is strictly bounded to lie between O 360 spatial location in a 2D environment is essentially unbounded you can travel infinitely far in all directions Morph box experiments One approach that has been used to investigate how place cells respond to a changing environment is the morph box experiment Stage 1 The rat is exposed separately to two environments square and circle until they become familiar rat run around in square then circle box then morph square until it becomes a circle Stage 2 The square is gradually morphed into a circle or vice versa by gradually shifting the walls In CDC H J I MORPH BOX SHAPE I Each row at left shows data from a different place J 39 39 cell Each column shows the activity of all place cells in a specific box shape Some place cells are active in circle but not square At a certain point in the morph sequence place cells abruptly shift or remap their firing fields When this happens some cells that had not been active in the y a L u square suddenly become active acquire a place field i in in the circle Some place cells are active in square but not circle Conversely some cells tat had previously been active in the square fall silent in the circle Some place cells are active in both circle amp square Some place cells are active in both the square and the circle but when remapping occurs these cells shift their preferred firing locations respect to the walls AND WITH RESPECT TO ONE ANOTHER that is they scramble their adjacency relationships with one other see how they flip from what they do in the square to what they do in the circle Rate remapping versus global rema pin don t maintain adjacency relat onships ith each other change where they fire with respect to walls and to each other also may cause cell to change firing rate amprate remappin i a r The transl Ion from s ape 1 to shape 2 induces RATE REMAPPING All place cells continue to fire at the same locations but their peak firing rates may change cells scramble locations they prefer to fire global remapping or just remapping The transition from shape 2 to shape 3 induces GLOBAL REMAPPING Place cells change the locations at which they fire not only in relation to the environment but also in relation to one another adjacency relationships not the same in each environment might tell animal not only where it is in the environment room but what environment room is is in Encoding different environments The phenomenon of global remapping allows place cells to encode not only where the rat is in a given environment but also which of many environments the rat is currently navigating in For example cells 2 amp 3 fire at different locations in the square shapes 1 amp 2 but at the same location in the circle shape 3 So we can know which shape the rat is in square vs circle from whether cells 1 amp 2 fire at the same vs different times Global remapping might thus provide a basis for storing memories of distinct spatial environments eg your bedroom versus your office Attractor states map spatial locations cells in sheet scrambles and then put back together but Into fIrIng rate space d39acent Iat39onsh39 s are d39fferent a J J39 me39peal39gshape39d attractor state can srt at different locations on the sheet to encode different locations in space reorganize flnng rate space helps you understand that you are only in one space D Finite domain of spatial locations that an f R2 RN animal can occupy R Firing rates of N place cells in the hippocampus ENV39RON39V39ENTA FIRING RATE SPACE 39 I 3 at 3939T39 i r A r 333 9 w waive d9 O 64 0 g r p A 9 g 9 enode locations In a 390 9 0 w 9 0 v r ef gfgsgofofo 4 r let s us make patterns that make sense Remapping good for memory coding but bad for the 2D attractor network model Recall that the 2D attractor model assumes a center surround connectivity pattern whereby cells with adjacent firing fields excite one another and cells with distant firing fields inhibit one another How can this pattern of connectivity be maintained if the adjacency relationships among place cells are remapping between environments This creates a complication for trying to construct an attractor network model of place cells 1 L r I I ll I IL Adi l I I Ar l I I 1 1 L JIILIJI r i i l fi quot F ritifi tifirh r L W iF l L l39 L a L a A 1 r v 39 39 t L l i I p I L s 1 iiiii fati f 39g Ei9i j Fina Killinr 11g 2 if r 1 4 w fffrifftr i l 39 afifll 1 in HIV ii 1 litlll39f tl39i gt 1 391 ii 7 i r 439 5 F iquot 391 Sparse versus distributed codes How many place cells are active in each spatial environment Distributed code Each CA1 pyramidal cell shows a 40 chance of having a place field in any given environment OOOOOOOOOOOOOOOOOO ells in DG only fire in very few locations Intermediate code Each CA3 Sparse code Each DG pyramidal cell shows a 20 granule cell shows a 1 chance of having a place field chance of having a place field in any given environment in any given environment OOBOOOOOOOOOOOOOOO OOOOOOOOOOOOOOOOOO sparsity of code how many fire at any given time sparse code versus distributed code DG is sparse code CA3 is less sparse and CA1 is distributed not sparse at all Postmortem labeling of immediate early gene IEG RNA with flourescence insitu immediately kill mouse after it goes to new enviro and view its brain hybrldlzatlon new enviro see more red dots PG H IEGs such as cfos zif268 Arc and Homer la 1 i 39 i 39 i39 quot quoti r i quot Hla become activated within minutes after a silent neuron starts ring action potentials If animals are euthanized immediately after a behavioral episode the RNA or protein products of these IEGs can be labeled in postmortem tissue using FISH to identify neurons that were active during the premortem behavior This image shows very little Arc labeleing red in neuron layers of DG and CA3 blue from a rat that was taken from its home cage immediately before death Data from Chawla et al 2005 Hippocampus 15579586 This image shows Arc labeleing in single neurons place cells of DG and CA3 from a rat that explored a spatial environment immediately before death Data from Chawla et al 2005 Hippocampus 15579586 Contextual Fear Conditioning Learning to be afraid of a spatial environment Moris water maze test rat ability to know where it is in environ and where it needs to go 1is test examines rat s knowledge of WHAT enviro it is in this is a diff question I ggVUEE 6 CSUS a Auditory Fear Conditioning sound predicts shock Context Fear Conditioning enviro predicts shock Dorsal hippocampal Lesions Impair Contextual but not Auditory Fear Conditioning When the hippocampus is lesioned soon after CSUS pairings rats continue to freeze to the auditory CS but not to the context where training occurred By contrast unlesioned rats sham controls freeze to both the CS and context impairment in contextual fear response due to damage of hippocampus no place cells that scramble and remind you of the enviro where shock occurred A r lament Cmta mal F F mg 5 i1 WW Tm Fug Fmeing Emilia11a 39 r39 J quot 77 7171ii 1 D39nlJ 1 El a F r J a a a 5 a E E lEIL lilL1 E 3 4 5 E Hlniutsaaa Anagnostaras Maren and Fanselow 1999J Neurosci 1911061114 Hippocampal lesions cause temporally graded retrograde amnesia for context fear Just as humans with hippocampal damage show temporally graded retrograde amnesia left rats show less impairment of context fear if hippocampal lesions are iven lon after trainin rath than immediat I after trainin if you wait a few d ys before gestroy hippocar pus agility to recognize em rdl not as bad g 100 similar to HM no longer newly acquired memory CONTROL 39 SHAM U AMNESIC 39 I HIP LESION g 80 35 80 g labels here are Incorrect fle g o 0 2 60 LC 60 c H I E 39 If39f39 g 3939 I 2 40 3 40 5 e 3 u D o o o IR d 20 quot l 0 510 6 0 710 8390 0 O i 114 2391 2398 DECADE SURGERY DELAY DAYS Squire et al 1989 J Neurosci 980 Kim amp Fanselow 1992 Science 256675 Amnesic Korsakoff s patients show Hippocampallesioned rats show impaired impaired recent memory and intact recent memory and intact remote memory remote memory for public events for contextual fear conditioning Chlamydomonas Reinhardtii A green algae that swims with two agella In the presence of light it can grow well without nutrients Has an eyespot that detects bluecolored light F i i i i ii 39 r U 7 7 J 39 a F quot 7 D 7 imElililTizr39I iirw39ij 7 1quot jut l quot 17 l Proceeding of the National Academy of Sciences 100139405 2003 Charmelrhoosinez a irectly lightegate cationaselective memrane channel Geerg Hegel Tainjef Ezellesii Welfrem lluhni Surieel Kateri39gre39lli lhlene Adeisllwilii Peter Bertheld llj eris llig if Peter HEQEWHMMWF and Ernst Eamberg Mex Plenrltlnstitut f r Biephysiltj Merie urie Etresee iii eeeee Frankfurt Germany 1llnetir tiuit fiilr Eieclhernie Universit t Reenslbur UniversitEtsetresse 3H SEEM Hegehehurg Germany and Institut fiur Eiephyeikelisrhe Ehernie lehehnWelfeng Eeethe Univereitet Bi EEll ltl Ul l la eeeeee Frankfurt Germany I I39irrirriiurliteteze by Walther Siteetlreniue Ul iih EilTEity ef Eeliferhiej Sen Frencigte September eerie received fer retrietrlr April E l BILJ lighi Na channels similar to those associated with glutamate gzxgrIg4 receptors instead of bind glutamate open when blue lightquot y hits them Naquot 3 The algae s eyespot contains a sodium channel that depolarizes the algae cell when it is opened by a photon of blue light in much the same way that an ionotropic glutamate receptor depolarizes a neuron when it is opened by binding a glutamate molecule Optogenetics can we insert channelrhodopsin into a neuron If we did this would we then be able to excite or inhibit neurons with light instead of glutamate In a word yes YFPtagged channelrhodopsin 0 Created by Karl Deisseroth and colleagues Boyden et al Nat Neurosci 81263 2005 o Packaged in a virus usually a lentivirus or adenoassociated virus that can be injected into the brain where it will infect neurons 0 Infected neurons can ectopicallv express the channelrhodopsin A cellspecific promoter sequence virus only affect neurons can be used to limit expression to specific populations of neurons cellspeci c promoter targets desired population of neurons channelrhodopsin gene YFP gene YFPtagged channelrhodopsin Blue light opens the channel to excite the neuron Green light makes the channel glow so we can see which neurons contain the channel and which don t 1 blue light EIFIEFIE EhRE YFP emiEEinh l397 l if ll E iL l J 4 7 Eiii 7 ii 7 1 39 7 l iL i re 4 it 3 i l39 i f Kite e x at t we 17ligi iiig e tag with yfp to make it glow green when hit with light Na Millieeemd limeseale genetically targeted ptieel Cl ilIl quoti neural activity Edwe MI Heydmn 1 Femg Zlmmgli Emmet Eembergmi Gemg Negellm 3 Keri Deieeemtlhli i GREEN LIGHT CAUSES THE NEURON TO GIVE OFF A FLOURESCENT GLOW SO WE CAN SEE THAT IT CONTAINS THE CHANNELRHODOPSON PROTEIN Figure 1 Channelrhodopsin Blue Light K extracellular intracellular Na Blue Light Figure 2 Action Potentials Liu et al 2012 Nature 484381385 Hippocampus is infused with a viral vector containing ChRZEYFP driven by the TRE promoter sequence This promoter only drives gene expression when it is bound by a transcription factor called the tetracycline transactivator tTA The tTA protein is not normally present in neurons so ChR2 is not normally expressed even if a neuron get infected by the virus b Training Dentate gyrus Linking ChRZ expression to IEG activity The experiment is done with mutant mice in which the tTA protein is driven by a cfos promoter Since cfos is an IEG that gets activated when neurons start firing tTA becomes expressed in neurons that have recently started firing which will drive expression of ChR2 in these same neurons The tTA expression system can be turned off by giving the mice antibiotics doxicycline in their water Selective ChRZ expression in DH place cells that fire in a fearconditioned context Liu et al 2012 Nature 484381385 Green labeling shows neurons place cells infected with ChRZEYFP Rats are taken off Before training rats are given DOX during training blue light in context A DOX in context B so that is in the water so tTA amp tTA amp ChR2 will be ChR2 aren t expressed expressed in cfos positive neurons gt gt Doxycyoline No doxycycline Context A Context 8 Habituation FC l l l l l x l 5 days 2 days 1 day DH place cells Selective optical stimulation of place cells that were active in the conditioned context Liu et al 2012 Nature 484381385 After training rats are given Rats are taken Off blue light in context A DOX Before training rats are given DOX durlng tralnlng is in the water again but now blue light in context A DOX in COHteXt B SO that ChR2 is expressed in the is in the water SO tTA amp tTA amp ChR2 Will be neurons that were active ChR2 aren t expressed expressed 1 C39fOS during training positive neurons gt r gt Doxycycline No doxycycline Doxycycline Context A Context 3 Context A Habituation FC Testing lllllx llllll 5 days 2 days 1 day 5 days DH place cells nu Liu et al 2012 Nature 484381385 OPTOGENETICALLY TRIGGERED 20 Test 20 RECALL OF THE CONTEXTUAL FEAR Habitua on 15 MEMORY The graph at right shows that blue light never causes rats to freeze in context A during the habituation session prior to fear conditioning blue line However after fear conditioning in context B the blue light causes rats to freeze in context A Rats do not freeze in context A when the blue light is off 3 min 3 min 3 min 3 min P O gt Doxycycline No doxycycline Doxycycline DH place cells Context A Context 3 Context A Habituation FC Testing Hillx llHH 5 days 2 days 1 day 5 days Three problems with attractor models of place cells 1 The remapping problem Unlike head direction cells which maintain identical adjacency relations with one another at all times even during sleep place cells scramble their adjacency relationships with one another remap when the environment changes 2 The temporal coding problem Attractor models are good for storing and updating via path integration a firing rate code for space but superimposed upon their firing rate code place cells also store a temporal code phase precession 3 The unbounded domain problem Unlike directional heading azimuth which is strictly bounded to lie between O 360 spatial location in a 2D environment is essentially unbounded you can travel infinitely far in all directions Attractor states map spatial locations into firing rate space The peak shaped attractor state can sit at different locations on the sheet to encode different locations in space D Finite domain of spatial locations that an animal can occupy f R2 gt RN nea ne snowmen Just he re a 6 r lt o 699 5amp3 6 a 59969 1 o 1 3 9 v39 r r a u o o 4 o 9 A v 6 9 o o 9 i 909 pag oiog ozo 060 v 0 v v o 9 t Qfng 3123quot u scramble in new environment ENVIRONMENT B e w quot9quot x w r 00 oo 920302 o R Firing rates of N place cells in the hippocampus on sheet not actual ess of preferred firing spaces in worldspace FIRING RATE SPACE A points on one surface Sparse versus distributed codes How many place cells are active in each dslspatidacl env39 ent r n ibute ode nee tognow a out activity of not a Place cells re in 3 enVironmentS more of the cells to know what environment in guy9111quot ff j 47 f space39time trade off harder to read but it is easier to store quot s 39 l quot 39 07 3 o v 3 39 f r I 0 o o v 39 O quot 39V 3 39n 5 39 I 39 On 39 39 39 I u 39 o 39 5 39 g 9 o 39 o I n 39 Distributed code Each CA1 pyramidal cell shows a 40 chance of having a place field in any given environment OOOOOOOOOOOOOOOOOO se code less important to know about multiple ells firing more just about if one is firing Intermediate code Each CA3 Sparse code Each DG pyramidal cell shows a 20 granule cell shows a 1 chance of having a place field chance of having a place field in any given environment in any given environment OOOOOOOOOOOOOOOOOO OOOOOOOOOOOOOOOOOO Postmortem labeling of immediate early gene promoter make it only expressed IEG RNA with flourescence insitu when IEG On expressin DG place cells that I when animal off antibiotics hybrldlza uon only in certain environm no longer inhibit channels from bei09iimuleted bright n Dr J 39 I make memory of that enviro IEGs such as cfos zif268 Arc and Homer la Hla become activated within minutes after a silent neuron starts ring action potentials If animals are euthanized immediately after a behavioral episode the RNA or protein products of these IEGs can be labeled in postmortem tissue using FISH to identify neurons that were active during the premortem behavior This image shows very little Arc labeleing red in neuron layers of DG and CA3 blue from a rat that was taken from its home cage immediately before death Data from Chawla et al 2005 Hippocampus 15579586 This image shows Arc labeleing in single neurons place cells of DG and CA3 from a rat that explored a spatial environment immediately before death Data from Chawla et al 2005 Hippocampus 15579586 Millieeemd limeseale genetically targeted ptieel Cl ilIl quoti neural activity Edwe MI Heydmn 1 Femg Zlmmgli Emmet Eembergmi Gemg Negellm 3 Keri Deieeemtlhli i GREEN LIGHT CAUSES THE NEURON TO GIVE OFF A FLOURESCENT GLOW SO WE CAN SEE THAT IT CONTAINS THE CHANNELRHODOPSON PROTEIN Figure 1 Channelrhodopsin Blue Light K extracellular intracellular Na Blue Light Figure 2 Action Potentials artificially make mice brain think it s in a different environment Linking ChRZ expression to IEG activity virus infect DG cells Liu et al 2012 Nature 484381385 Text Hippocampus is infused with a Viral vector containing ChRZEYFP driven by the TRE promoter sequence This promoter only drives gene expression when it is bound by a transcription factor called the tetracycline transactivator tTA The tTA protein is not normally present in neurons so ChR2 is not normally expressed even if a neuron get infected by the Virus b Training Dentate gyrus The experiment is done with mutant mice in which the tTA protein is driven by a cfos promoter Since cfos is an IEG that gets activated when neurons start ring tTA becomes expressed in neurons that have recently started ring which will drive expression of ChR2 in these same neurons The tTA expression system can be turned off by giving the mice antibiotics doxicycline in their water Selective ChRZ expression in DH place cells that fire in a fearconditioned context Liu et al 2012 Nature 484381385 Green labeling shows neurons place cells infected with ChRZEYFP Rats are taken off Before training rats are given DOX during training blue light in context A DOX in context B so that is in the water so tTA amp tTA amp ChR2 will be ChR2 aren t expressed expressed in cfos positive neurons gt gt Doxycyoline No doxycyoline DOX is antibiotic Context A Context 8 Habituation FC i i i l l x H l a before off DOX 5 days 2 days 1 day quot0 Ilght should not cells firing in this fear enviro on Its own Cause express fear response DH place cells Selective optical stimulation of place cells that were active in the conditioned context Liu et al 2012 Nature 484381385 After training rats are given Rats are taken Off blue light in context A DOX Before training rats are given DOX durlng tralnlng is in the water again but now blue light in context A DOX in COHteXt B SO that ChR2 is expressed in the is in the water SO tTA amp tTA amp ChR2 Will be neurons that were active ChR2 aren t expressed expressed 1 C39fOS during training positive neurons gt u gt Doxycycline No doxycycline Doxycycline experimental context same as control 0313 tGXtA COnteXt B contalllligff antibiotics and Habituatlon fear F C Testing has been fear i l i l l conditioned Textl l l iCOIlitioned X 5 days 2 days 1 day 5 days DH place cells Liu et al 2012 Nature 484381385 OPTOGENETICALLY TRIGGERED 20 T t 20 RECALL OF THE CONTEXTUAL FEAR 3 Hisbitua on 15 MEMORY The graph at right shows that blue S 5 Exp 10 light never causes rats to freeze in context A n 12 during the habituation session prior to fear a 10 conditioning blue line However after fear 3 conditioning in context B the blue light causes 5 rats to freeze in context A Rats do not freeze in context A when the blue light is off light on for three minutes off for three minutes repeat 3mir391 3mg 3min 3min freeze with blue light since it remembers fear environment where it was s ocke b O gt rlnakeitremembtDox line No dox c Cline D x lin where it was during yCyC y y 0 ycyc e fear achiSitiOnContext A Context B Come A pmduce SameHabituation F C Testing behavrors as it actually int e ottr i l l l l l i l l envho X H artIfICIally activates days 2 days 1 day 5 days place cells DH place cells Three problems with attractor models of place cells 1 The remapping problem Unlike head direction cells which maintain identical adjacency relations with one another at all times even during sleep place cells scramble their adjacency relationships with one another remap when the environment changes 2 The temporal coding problem Attractor models are good for storing and updating via path integration a firing rate code for space but superimposed upon their firing rate code place cells also store a temporal code phase precession 3 The unbounded domain problem Unlike directional heading azimuth which is strictly bounded to lie between O 360 spatial location in a 2D environment is essentially unbounded you can travel infinitely far in all directions Theta rhythm and Sharp Waves Theta rhythm is a 68 Hz Sharp wave ripples SWRs oscillation that occurs during are synchronous bursts of voluntary movement such as activity that occur mainly When the rat is navigating during quiet wakefulness through its environment When theta is absent i ii quot llii i it Iquot39gt3939lquotlUHquot2331i quot 39lquot iquot quoti M M t 1 W W iwvvquot quot quot39quotquotquotlquotulirli l it39ll i iiquot n 39 i39V i i iiiliiquot39r film mage ehl movement 1 1 Hz to 10 kHz ill 39139 i 2 V 5quot V ai39 u i W331 o J v u J A l quot Ah 0 I1 2quot quotquot2 v gt go 39av 39 flu fvl OL A xJi 39 500 H to 10 kHz 3922 With lump 2 o l v I l 39 H n quot39 x quot39 quot 2 lawquot il39 39il 3 J r h quot v39 a t l39 a o gfu H Il i amp f placed In box 11 inch leTlIl 100 quot3400 Hz 4 U tilllw l quotiquotquoti39i inun 3quotI39H3939l4 quotliquot39ii H I 3 Will39u illquot W si39Ninthinquot 7 o n3 mminitiitil w lw P39Cked Up placed in water 39 Sillml climb om itob39OHz 100 ms I EEG signal recorded at a specific location is called the local field potential or LFP rhythm show firing of hippocampal place cells Phase Precession in Hippocampal Place Cells As the rat passes through the place field the place cell s spikes occur at progressively earlier phases of the EEG theta oscillation Z I Firing phase differs at A versus B even though firing rate is the same record place cell fir 0 very specific thing that v ays happens 3400 23930 160 1quot pl ge cell bursts a theta frequency as rat runs thr gh field of place cell it bursts r mically at theta frequency uto t ertain ace39 hence lacec place cell bursts p at theta frequency 5 l and the LFP phase 4 l of the spike bursts dependsupon and thus encodes Place Field I where the rat is in l the field 180 270 A B Firing rate alone does not distinguish between points A and B because the firing rate is the same at both locations 00 360 bursts are coming at earlier time of spikes clumped in little bursts bursts that happen in phase eaCh me phase PreCGSSIOH middle of place field are greater Graphing phase precession The phase precession phenomenon can be seen in a scatterplot graph where each point corresponds to an action potential with position on the xaxis and LFP phase on the yaxis i I a Place field 340 210 160 100 30 E 1800 I I I I I m U E 2 LL 270 00 360 Place Field 0 50 100 150 200 M Position Cm Place cell spikes burst at theta frequency LFP theta Phase Compressed replay within a theta cycle As a consequence of phase precession place cells fire in an orderly sequence on each theta cycle On each cycle the first cells to fire are those with fields centered behind the rat then cells centered at the rat s current location then cells centered in front of the rat see example cells that fire behind rat in front of rat and where rat is now early stages of theta cells we just left behind 39 340 210 160 340 100 210 etc 100 210 30 160 100 30 180 270 Cell1 39 i Cell2 Phase codes position in fieldcentered coordinates When the rat reverses direction place fields are traversed in the opposite order In this case the spike phases also reverse their order Consequently each place cell s spike phase encodes the distance to that cell s field center along the current direction of travel see example shift in valley show that not just firing rate but also time code that tells where rate is 340 210 160 340 100 210 30 160 340 100 210 30 160 100 30 180 270 00 360 Cell 2 Cell 1 alwavs field entering will fire39 early on t att w tdircthLln rat ist veling w a ce sare lrlng e youw ere elsony time code change Rate or time code Both The phenomenon of phase precession seems to indicate that place cells can simultaneously store two codes for space Rate code firing rate of the neuron depends upon where the rat is located with respect to the boundaries of the allocentric environment Time code spike phase of each place cell depends upon the distance to the cell s field center along the rat s current direction of travel 2D attractor network The attractor network model we have discussed does not on its own offer any clear explanation for the phase precession phenomenon The model can be modified in various ways to account for phase precession but these modifications involve adding new neurons or connections to the model and experiments must be done to investigate whether such features actually exist L r Am m 1 IILIJI a t is r 39 r39 iquot 39 ffff a L L a F It 439quot Ii 113 I39 I 39 i i hr J i39 39 E i 39 iit ifi tii a i m iiiiii r i39 191111 if are L r fi tr l 39 fi fff39ff39i 39 lm In A Three problems with attractor models of place cells 1 The remapping problem Unlike head direction cells which maintain identical adjacency relations with one another at all times even during sleep place cells scramble their adjacency relationships with one another remap when the environment changes 2 The temporal coding problem Attractor models are good for storing and updating via path integration a firing rate code for space but superimposed upon their firing rate code place cells also store a temporal code phase precession 3 The unbounded domain problem Unlike directional heading azimuth which is strictly bounded to lie between O 360 spatial location in a 2D environment is essentially unbounded you can travel infinitely far in all directions Finite versus infinite world state variables Allocentric azimuth that is 39 Allocentric position or distance is directional heading is a finite an infinite world state variable world state variable because there because there is an unlimited set is a fixed and limited set of of locations that one can visit by directions one can face traveling farther and farther in any Consequently a finite number of g39Ven d39rECt39O head direction cells can store all 39 Consequently a infinite number of possible directional headings place cells would be needed to store all possible locations that an animal can visit How does the brain handle this problem Allocentric Azimuth Allocentric Position Adult neurogenesis in dentate gyrus Old neurons for old places and new neurons for new places A Allocentric Position This image was generated in the Lab of FH Gage at the Salk Institute For many years it was believed that mammals were born with all the neurons they would ever have and that no new neurons were generated a process called quotneurogenesisquot in adulthood It is now known that adult neurogenesis does occur in two specific regions of the adult brain the olfactory bulb and the dentate gyrus The microscope image at left shows mature neurons stained in red and newborn neurons cell bodies axons and dendrites stained in green Perhaps new place cells are born in the dentate gyrus to store new memories of novel places that we visit Vaybebut we can do much better than adding new place memories one at a time Cyclical versus linear position or distance This odometer is like a clock with six hands that measures distance on six different spatial scales Adding just one new dial to the odometer multiplies the number of distances we can represent by a factor of ten Could the brain contain systems for measuring distance that work like this Hacking the ring attractor What if we make some modifications to this circuit such as feeding velocity inputs from the utricle instead of from the semicircular canals A EI39anxr I 7 UTRICLE tr RY o m o l EXCITATO RY RING INHIBITORY RING continuous attractor network Samsonovich amp McNaughton 1997 a dv Fuhs amp Touretzky 2006 360 Burak amp Fiete 2009 A Navratilova et al 2012 i 270 39a lVIhatre Gorchetchnikov amp Grossberg 2012 8 625 v 2 180 no cu 5 V 90 8 0 Angular frequency of the bump 0 varies linearly with velocity v at a slope determined by the spatial frequency d1 M of the periodic length interval 2014 Nobel Prize for Physiology amp Medicine Jehm Edve rd MewBr tt Keefe Meser Meser Grid cells in entorhinal cortex CA1 Schaeffer Pyram39dal cells Subiculum Collaterals I Pyramidal Cells Mossy Temporo ammonal Medial P CA31 I Fibers Dentate pathway Entorhinal yrcaerrlll a Granule Cells Cortex Perforant Path spatial pegged 7 gt spatialfrequency d1I I rx I I I I I I I I I I I m lullhi 3939II39I39 IIIIlIIII H E I I I I I i I I quotI I ii I I data from HaftingI Fyhn Bonnevie Moser amp Moser 2008 E I Supplementary Figure 5 FE I I I I 1 4 I 9 E II I I I I I I II 5 quot I I E II I II IIJI I I I I I II I II IIIIIIIII II III II39I ill I I I Iquot I III I II 39 39 I IIIIIIIII I I 39III39 39I II E I III I II II III IIIII39 I III I I39I I I I I 39 39 I II 39I39 39 III II I39IIIIIIIII 39 I39IIII 39Ill 39 II39I I I 39 I EI II39I I 4 I IIII39II II39 IIll I I39Iquot I EH 11 II39 I39 II III I I III I I I III I i I III I I I I I I D I I I39 I I I I I I Position x 70sz Different grid39fells have different vertex spacings 2 e I II I 0 degrees s V distance s MxN neurons 0 degrees s V distance s MxN neurons Ring attractor model of grid cells 1 The remapping problem Like head direction cells and unlike place cells grid cells with the same vertex spacing appear to maintain identical adjacency relations with one another in all environments 2 The temporal coding problem Like place cells grid cells also burst rhythmically at the theta frequency and exhibit phase precession against the local LFPthe attractor model still does not inherently account for this 3 The unbounded domain problem By using several rings each composed from grid cells with a different spacing just like the different wheels of the odometer we can uniquely encode a VAST number of locations enough to cover the surface of the earth many time over Lap number Firing rate Hz Theta phase deg Phase precession in grid cells guruum or 1quot 392 30 0 Like place cells grid cells In entorhinal cortex also show 0 phase precession against the 2 locally recorded EEG theta 20 rhythm 0P1 i 72o 4 3 2 4 Phase precession IS more 80 2 1 a a 13 common In grid cells recorded r 39 quot5 240 g 39 from cortical layer than In is I E3535 quot g s otherlayers 0 6 Position cm From perception to memory erce tion ngh p p Icon1c memory 8 2 Shortterm memory 4 I 395 PS Intermedlateterm 45b a quot39 1 C1 5 Longterm memory Low Neural Codes Mapping states of the world onto states of the brain A neural code may be formalized as a function f that maps a domain D of quotworld states onto a range R of quotbrain states States of the world become states of the brain works like a function D values on x axis f 3 D a R R values on y axis NEURAL STATES OF THE WORLD y STATES or THE BRAIN A quottime code for azimuth Because of their phaselocking behavior the interspike interval between a left versus right VCN neurons in mammals or nucleus magnocellularis neurons in birds provides a time code for the azimuth of a sound source azimuth of sour goruErcglialnlgl e fggfnq h can represseln eTCFiQgrgmgg gr ode remember sound reaches right VCN or NM rst if sound comes from the right side interval shift between ring of left or right serves as a functin of here the sound is l coming from 0 Negative Positive LEft azrmuth azrmuth or 900 U 90o RghtVCN AZIMUTH ANGLE orNoM E Sound E Cycle 39 Period 39 25 ms azimuth is called 51 set of points that lie on a circle interspike interval where one spike coming from left side and other is coming from right side 4 mafp39ptlh haviime ith dnto interspike interval A pair of monoaural phase locking neurons one from each brain hemisphere can encode the azimuth of a sound source in their interspike interval D values on x axis f f1 gtR7 1 points on circle NEURAL STATES or 200 THE WORLD a 100 e 1 0 2 100 1 E 200 O O 0 4 6 1 gt 90 45 0 45 90 90 45 0 45 90 AZIMUTH degrees AZIMUTH degrees R values on y axis real number line STATES OF THE BRAIN Converting the time code into a rate code MAMMALS VCN neurons send bilateral excitatory direct and inhibitory indirect via NTB projections to the medial superior olive M50 M50 neurons convert the time code for azimuth into a ring rate code for azimuth Left Ear Right Ear Right NTB same state of world different state of the Experienced Range brain L V III l g I I R refers to real number g Best ITD Best ITD line i 5 refers to points on a quotquot circle Right 200 100 0 100 200 Left 39 Leading ITD microseconds Leading aZ39mUth Converted to ring rate Mapping azimuth onto the mean ring rate of neurons in left M50 The mean ring rate of neurons in the left M50 is an approximately linear function of azimuth so MSO maps azimuth onto the ring rate of a neural population D values on x axis f f1 gt1Rf1 R values on y axis points on circle real number line NEURAL CODE STATES OF 40 STATES OF THE WORLD gt E THE BRAIN 3 3O 3 30 E 20 E 20 E 10 1o 9 O I O 90 45 0 45 90 lt gt AZIMUTH degrees 90 45 0 45 90 AZIMUTH degrees Mapping azimuth onto the mean ring rate of neurons in left and right IVISO Together the left and right MSO map azimuth into a planar quot ring rate space where the axes are the mean ring rates in left and right M50 D values on x axis points on circle STATES OF THE WORLD 0 90 45 0 45 90 AZIMUTH degrees STl gtIR72 L r 00h 00 N O A O Left MSO Firing Rate Hz 0 NEURAL CODE AZIMUTH degrees A CDC 2H elea Buuu osw lu la 00 quotquotquotquotquotquotquot quot 1 2300 I 90 45 0 45 90 R values in ZD ring rate space Cartesian plane FIRING RATE SPACE M A O 00 O O Left MSO Firing Rate Hz N O O O 7 1o 39 20 3o 40 39 nght MSO Firing Rate Hz winstead fconverting azimuth one cnvertel int numbers left an riht space corresponds rin of on i n of not fre chne they nt if a when one number azimuth t nuber even tho have o n bers in ring rate reen line Firing rate code is really one dimensional Most of the points in the planar ring rate space do not correspond to azimuth angles Only a linear subset of the plane encodes actual azimuth angles D values on x axis f f1 gtch2 R values on a line in ZD points on circle ring rate space Cartesian plane NEURAL CODE FIRING RATE SPACE A n n a STATES OF 2E 40 40 3 1 only points on THE WORLD 4 z E 40 this line encode n 30 30 8 4 azimuth angles 27 o T m 30 E 20 20 23 27 quotquotquotquotquotquotquotquot 3 5 quotquotquotquotquotquotquot quot to IE 20 U 10 10 g g 0 quotE O O f E s L 90 45 0 45 90 v quot 3 O 0 10 20 3o 40 39 AZIMUTH degrees 90 45 0 45 90 AZIMUTH degrees nght MSO Firing Rate Hz Jeffress Model Right 200 NM Rightinput timecode Right NL 4 49 f I 41gt I r quot I I II I u II I II E ill I I I r I I II ll 1 l I III H r Output place code I I II I abcde LU Leftinpul E timecode m D Z Left 5 NM quotquot Left Leading BARN OWL NM neurons send bilateral excitatory projections to NL neurons which convert the time code for azimuth into a ring rate code for azimuth In this case NL contains a different population of neurons for each ITD that it encodes the quotplace code for azimuth Let us consider three of these populations that encode the left neuron E center neuron C and right neuron A azimuth positions Experienced Range I Neuron A 200 100 0 100 ITD microseconds Right Leading ots ofquot re nt neurnas each prefer ITD as m ural me delay a neuron re re Neuron most for aherte three p referred ring rates thus will have ree points on ring rate space A three dimensional ring rate space The three neurons or populations A C and E map azimuth angles onto a line in 3D ring rate space What if we consider all ve neurons AE this time take 1D azimuth and map onto 3D ring rate space however still just a one dimensional thing begin with a line and end result i if the brain encodes azimuth line D values on x axis line 3 neurons 3D space 39 ns e ne on how azimuths all fall on that f T1 gtCIRT3 pomts on curcle NEURAL CODE STATES OF THE WORLD 90 45 0 45 90 AZIMUTH degrees values on a line in 3D ring rate space FIRING RATE SPACE t E onlypointson this line encode azimuth angles A higher dimensional world space Recall that the locust s ocelli encode the pitch amp roll but not yaw angles of its ight position These two angles de ne a point on a torus D Flight position pitch amp roll only 39 Roll ocelli of locust encode whether it is pitching or rolling ignoring yaw here g 7 2 gt specify must give tWO numbers 2D world 2 numbers just give a plane assumer numbers on line not a circle if on a circle get a torus donuU This symbol is used to represent a space consisting of two angular coordinates which is a torus different points on this donut shaped map each point refer to different pitch and roll Ocelli map flight positions onto ring rates The three ocelli arranged in a triangle map ight positions into a 3D ring rate space Would this work if the ocelli were in a different geometric arrangement D Flight position R Brain states that can pltch amp roll only g f2 R B represent lght posmons In D Flipped Left Pitch Back f OCELLI CMYES39 8 e eve ight 93 NO bottom ocellus Pltchmnm not lit and right NO YES 9amp0 and left are lit Left ocellus lit Q30 Questions map objects to ring rate space In our game of quottwo questions we can imagine a pair of neurons 11 and 12 that re more when the answer to their question is more likely to be yes D object chosen by Objects HRH R Brain states that can thinker represent objects A 3 l 1 Cupcake NEURAL CODE YES quot154 2 Thundercloud Q1 Can it be washed 3 Toothbrush 4 PiCkup TYUCk Q2 Is it bigger than a I I must guess state of world loaf 0f bread 41 questions you choose to ask de ne your NO quotquotquotquotquotquotquotquotquotquotquotquotquotquotquotquot code similar to placing of ocelli NO YES represent these states in brain From perception to memory erce tion ngh p p Icon1c memory 8 2 Shortterm memory 4 I 395 PS Intermedlateterm 45b a quot39 1 C1 5 Longterm memory Low Delayed saccade task 0 1 Fixation Head xed monkey stares at central point Recording electrodes monitors neurons in dorsolateral prefrontal cortex Diagram adapted from Chang M H et al J Neurosci 201213222042216 Delayed saccade task 0 1 Fixation Head xed monkey stares at central point 0 2 Target presentation A spot appears at one of eight locations surrounding the fixation point after a few seconds xation point disappears if you look where it used to be get a squirt of grape juice in your mouth Recording electrodes monitors neurons in dorsolateral prefrontal cortex Diagram adapted from Chang M H et al J Neurosci201213222042216 Delayed saccade task 0 1 Fixation Head xed monkey stares at central point 0 2 Target presentation A spot appears at one of eight locations surrounding the fixation point 0 3 Delay period Target disappears xation is maintained Recording electrodes monitors neurons in dorsolateral prefrontal cortex Diagram adapted from Chang M H et al J Neurosci201213222042216 Delayed saccade task 1 Fixation Headfixed monkey stares at central point 2 Target presentation A spot appears at one of eight locations surrounding the xation point 3 Delay period Target disappears xation is maintained 4 Saccade Fixation point vanished instructing the monkey to look at the former target to obtain reward Recording electrodes monitors neurons in dorsolateral prefrontal cortex Diagram adapted from Chang M H et al J Neurosci201213222042216 Targetspeci c delay period activity Data from Fuster and GoldmanRakic target target xation on off off I t ll l spikes Recording electrodes monitors neurons in dorsolateral prefrontal cortex Diagram adapted from Chang M H et al J Neurosci201213222042216 raphs f rin ependin n Cha position of ta ret in ret keeps rin off rin respcnds Ring attractor network Visual Input A ring of neurons in which Each neuron can be turned on each neuron is tuned to by an excitatory visual input prefer a different target which is activated by stimuli at angle For simplicity we or near its preferred target may imagine that adjacent angle But do neurons keep neurons in the ring prefer firing if this input turns off after adjacent target angles the target disappears think of bein arra ne circular rin formation magine ajacent refer anles C VitV Ump all neu the la ill re d bump Centersurround connectivity sual Input RECURRENT EXCITATION Neurons may excite themselves to stay active after their input turns off a connection scheme referred to as recurrent excitation which makes individual neurons bistable on or off Input RECIPROCAL INHIBITION Neurons may inhibit their neighbors to turn them off In combination with recurrent excitation this causes the network to have peakshaped attractor states al I uro rin nd inputs nes bistable either be r ff if efcite stay keep rin if ets excite neihbrs th eihbors prevent thin from rin 39 recircal inhibitini nei39h rs but The Peak is a stable Attractor State The peak shaped attractor states are quotlow energy states of the network Left on its own the network will always converge to one of these states bottom graph M shows unstable state I I thatwould not be oooooooooooooooooooo supported bythis system must be peak shaped I II OOOOQOOO Attractor states map target angles to ring rate space The peak shaped attractor state can sit at different locations on the ring s perimeter to encode different target positions D Ange to target g f1 ngV R Firing rates of N delay STATES OF neurons In PFC THE WORLD FIRING RATE SPACE NEURAL CODE A only points on this circle encode target angles LLLL n of are of the single angle angle single angle Io if eak run rin eventually f0 l Headdirection HD cells Each HD cell is tuned to fire persistently Whenever the rat faces in its preferred direction Ranck 1984 Taube et al 1992 Different HD cells have different preferred ring directions thus implementing a population vector code for head direction Firing Rate Hz N E S W N 09 909 1809 2709 3609 Head Direction HD tuning referenced to visual cues HD cells remember environments across repeated Visits by aligning their preferred direction with respect to landmark cues Cue card A Firing Rate Hz Firing Rate Hz W N E S W 0 50 1E80 2870 00 0 90 180 270 360 Head Direction Head Direction HD tuning does not require visual cues HD cells still re in their preferred direction when NO visual cues are available their activity is persistent rather than evoked Cue card Firing Rate Hz I 39 Lights Off I I I i m E a 00 E E W N E S W 6 33900 1E80 2870o 99600 0 90 180 270 360 Head Direction Head Direction Population Vector Code From the tuning curve of a single HD cell we can infer the pattern of activity over a population of HD cells Firing Rate max Head Direction The activity peak shifts through the population as the rat turns its head Firing Rate max Head Direction A Recurrent Attractor Network Center Surround Connectivity OOOOOOOOOOOOOOOOOOO Inhibitory Connections ooood 1 5ooooo Excitatory Connections Head Turning Causes a State Transition in the HD Network Stationary Bump Shifting Bump mut HUJIIIII A Recurrent Attractor Network Center Surround Connectivity OQQOOOOOOQQOOOOOOQQ mew H wwmm Neuron Inh39b39tory Neuron right turn Nonnections left turn ooood 1 coooo Excitatory Connections Egocentric versus allocentric reference frames Egocentric means quotself Allocentric means quotother centered so the origin of centered so the origin of an an egocentric coordinate allocentric coordinate system system is located on some is located outside the body in part of the animal s body the external world allocentric focused on external like a compass 0 deg com pass detect N COG Positiveelectromagnetic north azimutWhich is fixed point migratory birds have a magnetic sense allocentricVV Whereas barn owl s center is center of gaze egocentric azimuth 8 Negative azimuth Ijkl Egocentric Azimuth Allocentric Azimuth headcentered coordinates with earthcentered coordinates with 0 reference at center of gaze 0 reference at geomagnetic north HD video Headdirection HD cells Each HD cell is tuned to fire persistently whenever the rat faces in its preferred direction Ranck 1984 T aube et al 1992 The preferred direction is defined With respect to environment centered coordinates and remains the same at all locations Different HD cells have their own preferred firing directions which HD cell remain stable across repeated Visits to the same environment container in WhiCh 1 2 00 l 3600 arnnnaVs IV current Cue card estimate of vvhereits heading aHo cent caHyin afannhar placewith No E0 5 0 W0 N o S familiar 0 90 180 270 360 Head Direction 1809 features 2709 W E 909 Firing Rate Hz Control by visual landmark cues 39 When Visual landmarks that define the environmental reference frame are rotated HD cells rotate their preferred directions by the same amount familiar landmark cues cue card A matter test by changing them around rotate cue card and keep all the rest the same two cells follow rotation of cue card get fooled and think direction changed sense C9quot 1 2 ofdirection reorient N E S W N N E S W N 09 909 1809 2709 3609 09 909 1809 2709 3609 Head Direction Head Direction Firing Rate Hz HD tuning does not require visual cues HD cells still fire in their preferred direction when NO visual cues are available their activity is persistent rather than evoked Cue card If you were blindfolded in a x familiar enVIronrnent you I Lights Off I would still have a sense of direction thusin the dark 1 these HDcells still 1 N fire 5 E w a E a E Equot E E W N E S W W N E S W 09 90 1809 2709 3609 09 90 1309 2709 3609 9 9 Head Direction Head Direction A working memory for allocentric azimuth The population of HD cells functions as a container for storing a working memory of directional heading When the head is not turning a stationary activity bump stores the current head direction When the head turns the activity bump must shift HD signal represent something in a sense that doesn t exist in the world not sens stimulus abstract relatio rf hip between you a I your environment E because indivi ial neurons hav urve can infer this activitSB bump for all I er as rat turns itsgead activity 39 posits and withdrawalgofrom assu o nment Fir Head Direction Angular path integration The bump must shift at a rate that is exactly proportional to the angular velocity of the rat s head as rat turns its head activity bump shift deposits and withdrawals from assumptions of environment Firing Rate max Head Direction an ularvelocit of bum mu be equal t angulalro velocity of rat head head turning Speed Angular velocity measures the angular velocity is just speed and diI ECtiOIl Of rotation Spee j some hmg 395 tummg g Typically angular velocity is O 5 measured in degrees or radians 9039 i per second w The magnitude absolute value of 5 the angular velocity is simply the 3 frequency in Hz times 360 g degrees The sign of the angular velocity defines Whether the direction of rotation is clockwise or counterclockwise Again Sytestibular system senses angular Turn world Into mind state we are concerned with Yaw change in azimuth angle The re a re six 6 B Yaw Rotation around zaxis for head movement I l 3 translational 3 rotational A o 7 quotj a Translation along R011 394 H i y Rotation 39 b the X y amp Z axes around i Pitch x axis Rotation Rotation around the 33131201 x y amp z axes Semicircular canals Vestibular organs of the inner ear Moving around in plane of yaw cause fluid to move around in semicircular canals moving hair cells in ampulla give sense of rotation The utricle amp saccule detect translational movement amp linear acceleration Utricle senses the horizontal B Yaw Rotation plane x amp y axes around zaxis Saccule sense the vertical plane 2 axis The ampullae of the semicircular canals detect rotational movements amp angular acceleration V Utricle y Saccule Roll i Rotatlon around Pitch xaxis Rotation around yaxis Ampulla Ampullae of the semicircular canals At the base of each semicircular canal is a bulbous enlargement called the ampulla which contains an organ similar to the macula of the otolith A layer of hair cells embedded in support cells extend their stereocilia into a gelatinous glob called the cupula When the head rotates in the plane of the semicircular canal fluid circulates in the canal and exerts pressure on the cupula This bends the hair cells in one direction or the other depending upon the direction of the head rotation Cupula displacement Angular acceleration Cupula Q l l l l l I 39 N l I I l u 39l r l l 39I y l r l l Semicircular l canals Semicircular Hair cells Endolymph Ampulla canal flow Vestibuloocular reflex VOR Rotate eyes at exact velocity in opposite of angular velocity of head The vestibuloocular reflex keeps the eyes fixed on a target while the head moves around Sensory neurons from the vestibular nerve project directly to motoneurons in the cranial nerve motor nuclei 1 Detection of rotation 2 Inhibition of 2 Excitation of extraocuiar 3quot extraocuiar muscles LJr muscles on on one quot the other side side 3 Compensating eye movement If httpuploadwikimediaorgwikipediacommonsthumb558Simpev 39 estibuoocuarrefexPNGSOOpXSimpevestibuoocuarrefexPNG Three ring angular path integrator All three rings are composed from HD cells with adjacent cells in each ring having adjacent preferred directions Height of bump is firing rate of cell right below that spot INHIBITORY RING Can use this idea to see where Rat thinks it is EXCITATORY RING INHIBITORY RING Two rings are composed of inhibitory neurons One ring is composed of excitatory neurons All three rings contain their own activity bumps The activity bumps in all three rings remain aligned with one another as they circulate around the rings together The angular velocity of bump rotation is identical to the angular velocity of the animal s headbut how Connections among the rings The three rings end excitatory and inhibitory projections to one another that form a centersurround connection pattern with adiustable symmetry Excitatory cellls excites selves and tho Nearby that prefer similar directions also will excite the cells that prefer the same 039 INHIBITORY Irectlon as them in other ring These bumps still stay oncells still ac even without sensory input you always know you re facing some direction even if EXCITATORY blindfolded RING store these bumps Inhibitory cells CounterclockwiseRlNG direction HD cells in the top ring inhibit excitatory HD cells in the clockwise direction HD cells in the middle ring excite themselves their nearby neighbors and inhibitory HD cells in the top and bottom rings that share the same preferred direction HD cells in the bottom ring inhibit excitatory HD cells in the counter clockwise direction Symmetric vestibular inputs When the head is not turnino the top and bottom rings receive symmetrical input from tonically active vestibular neurons on the left and right sides which holdsthe activity bumps still in the rings 39Semicircular a Canal LEFT vestibular nucleus amp prepositus hypoglossi excite the top ring and inhibit the bottom ring RIGHT vestibular nucleus amp prepositus hypoglossi inhibit the top ring and excite the bottom ring Clockwise asymmetry When the head turns clockwise the top ring is inhibited and the bottom ring is excited so that inhibition decreases in the clockwise direction and increases in the counterclockwise direction This causes the bumps to circulate clockwise Semicircular Canal Se m ici rc u l a LEFT vestibular nucleus amp prepositus hypoglossi neurons DECREASE their firing rates RIGHT vestibular nucleus amp prepositus hypoglossi INCREASE their firing rates If rat turning vestibular neurons on side towards which head is moving increase firing Vestibular neu ns on side turning arvay fr m decrease firing ounterc 0C WISE asymmetry Lose symmetry bump shift to area of less inhibition When the head turns counterclockwise the top ring is excited and the bottom ring is inhibited so inhibition decreases in the counterclockwise direction and increases in the clockwise direction causing the bumps to circulate counterclockwise 4V 7 riff we Semicircular quot ix Ca n al a Ca nal I I tJt RIGHT vestibular nucleus amp prepositus hypoglossi DECREASE their firing rates LEFT vestibular nucleus amp prepositus hypoglossi neurons INCREASE their firing rates P05 is part of cortex each part of cortex communicates with thalamus Ascending HD pathway A 80 E V 60 is g4 40 E 2E 20 X LL I Vestibular O 39W N E S W System 09 909 1809 2702 3609 The HD signal was first discovered in the postsubiculum PoS by James Ranck in 1984 PoS receives strong inputs from the anterodorsal thalamus AD and HD cells were later found in AD AD gets inputs from the lateral mammillary nuclei LMN and HD cells were discovered there as well LMN gets input from the dorsal tegmental nucleus which contains HD cells and gets strong inputs from the vestibular system 80 60 4o 2039 Lesion 1 HD cell was recorded from AD thalamus for 15 minutes and the preferred firing direction was plotted from Blair et al 1999 2 The rat was then picked up and an electric current was injected bilaterally into LMN to destroy it 3 The same HD cell was recorded immediately after the LMN lesion It ceased to eXhibit any directional firing properties but fired constantly at about 10 Hz Hacking the ring attractor What if we make some modifications to this circuit such as feeding velocity inputs from the utricle instead of from the semicircular canals Semicircular canals sense rotational motion utricle sense linear motion 139 If V Iquot I l INHIBITORY UTRICLE RING EXCITATORY RING INHIBITORY RING Sensing head tilt and linear acceleration Hair cells in the utricle are depolarized by backward head tilts or by forward acceleration Hair cells in the utricle are hyperpolarized by forward head tilts or by backward acceleration Depolarization Hyperpolarization Sustained head tilt no linear acceleration Backward Forwa rd 9 Bright s No head tilt transient linear acceleration When the head is upright It Forward acceleration V Backward mam hair cells are at an a intermediate membrane potential and tonically release a moderate amount of neurotransmitter If linear velocity integrated bump would go around ring faster when moving forward like 0 h I i ow many miles you have on your car lg I I i I dometer in Car te s you Grid cell on a linear track ire at fixed distance intervals as if in a ring f integrate linear velocity get linear position F data from Hafting et al 2008 L Mali IlI IIIIIIIIIII ulwl 39IHI IIIH FIT I H mi El Elli E E LIL L 5i 9H r I j II I 39IIII I I 39I II 39 I quot39I II 3939 I 39i39 393 4i II I l ll39 II quot I IIIIIIIIII I I 39II39II39I I E I II 39 39Iquot 39L III IIIII39 I IquotII I 39I II39I IJII I L I II I I I Iquot II I I l39 1 IIII IIIIILII II I III II I39ll I III 39I II E E1 39II I In llquot I III III III II IIII quot II quot I II E 41 I IIIIIII I III III I ll 39II mi 1 II39I39 39IflII39 JIII I 39III39II Ii r III I 39 I 39I I I I I J I 39 quot 39 I I I I I I I D degrees s 360 270 180 Population code for linear rather than angular position Bump s angular velocity depends upon the rat s linear running speed Midterm 2 in class Wed Mar 11 Office hours after class on Monday March 9 Review session on Tuesday March 10 in Franz 2258A from 46 pm No office hours will be held after the exam on Wednesday or on Friday March 13 Written Final Exam Thursday March 19 spatial pegged 7 gt spatialfrequency d1I I r x I I I I I I 39 I I I I I I lullhi 39III39I39 I uIIIIIIIIIII H E I I I 1 I i I I 39 I 39 I I data from HaftingI Fyhn Bonnevie Moser amp Moser 2008 E I Supplementary Figure 5 FE I I I I I 4 I HE E II I I I I I II 5 quot I I E II I II IJ39 I I 39I I II 39 I I39IIquotI II 3939 III 39i39 I I I Iquot I III I II 39 39 I II39IIIIII I I 39III39 39I II E I II 39 II LI III IIIII39 I r II I I39I I I I 39 39 j I 39 39II I39I I I 39IIIII I III 39I I I I II I IIII I I II I I I I I I quotI IIII III I II I I E EI IIIII I IIIle II I 39II III I II I39 II III I I39 II EL I I IIII39II II39 III I II39I39III EH 11 II39 II Il39 III I JIII I I I I III I i I III I I I I I I D I I I39 I I I I I I I Position x Hacking the ring attractor What if we make some modifications to this circuit such as feeding velocity inputs from the utricle instead of from the semicircular canals linear speed move bump around the ring bump speed a und ring increase with rat running speed INHIBITORY UTRICLE RING EXCITATORY RING INHIBITORY RING like odometer in your car faster car moves faster odometer moves ampIl larger period smaller frequency Each grid cell bursts at a a Z dv frequency In Hz which IS equal to 360 the number of grid fields traversed A i 270 r per second Burst freq o 360 a 6 2 180 d 3 90 shallower for bigger spacing 8 0 lambda distance per second because makes 360 Angular frequency of the bump 0 varies linearly with velocity v at a slope determined by the spatial frequency d1 M of the periodic length interval Cyclical versus linear position or distance This odometer is like a clock with six hands that measures distance on six different spatial scales Adding just one new dial to the odometer multiplies the number of distances we can represent by a factor of ten Could the brain contain systems for measuring distance that work like this Different grid cells have different spatial periods or quotvertex spacings and thus A different spatial frequencies I firing rate IS the same as expected but pattern is diff fire bursts of spikes at 7Hz II 700 0 degrees s gridcells mid MEC grid cells 3 j dorsal MEC 0 0 V distance s M 72 7L3 Grid cells with different vertex spacings can be thought to reside in different ring attractors which are like different wheels on the odometer f grid cells dorsal MEC 0 degrees s M K2 7amp3 V distance s l midMEC I grid cells 360 27a 180 90 12 2 7zr dzv 0 M x2 x3 r 1 grid cells ventral MEC 1 The bump circulates m in rings with a smaller vertex spacing L which corresponds to a higher spatial frequency d and a steeper slope of the line relating v to a e 3 this is the correct diagram one from last lecture is incorrect Ring attractor model of grid cells 1 The remapping problem Grid cells with the same vertex spacing appear to maintain identical adjacency relations with one another in all environments so there is no remapping problem to worry about 2 The temporal coding problem Like place cells grid cells also burst rhythmically at the theta frequency and exhibit phase precession against the local LFPthe attractor model still does not inherently account for this 3 The unbounded domain problem By using several rings each composed from grid cells with a different spacing just like the different wheels of the odometer we can uniquely encode a VAST number of locations enough to cover the surface of the earth many time over so the unbounded domain problem is less of an issue each time rat passes through either one of grid fields see phase precession occurs over and over again Phase precession in grid cells bursts per second different firing rate because multiple spikes in burst burst at 7Hz firing rate and burst freq are not the same but both measure in Hz d gt T Ral wvn 1W 139 w 30 2O 1O 0 60 4o 201 3quot 0 720quot Lap number Firing rate Hz Theta phase deg 480 240 1 8 quot J I Position cm Like place cells grid cells in entorhinal cortex often show phase precession against the locally recorded EEG theta rhythm Phase precession is more common in grid cells recorded from cortical layer ll than in otherlayers The ring attractor model of grid cells does not provide us with an obvious explanation for phase precession Hacking the ring attractoragain What if we make some modifications to this circuit such as eliminating the bottom inhibitory ring How will the circuit behave now INHIBITORY RING excitatory cells also excite inhibitory cells get walls of inhibition EXCITATORY here only get wall on RING one side allow bump to only move in one direction INHIBITORY RING A ring oscillator CPG With a constantly circulating bump the ring attractor becomes a ring oscillator that could drive rhythmic behaviors like the locust wingbeat cycle ELEVATOR MUSCLES DEPRESSOR MUSCLES Theta cells Rate histogram of a theta cell Compare the firing rate maps of a theta cell place cell and grid cell All burst rhythmically at about 79 Hz but the theta cell lacks spatial tuning Theta cell Place cell Grid cell lll l ill l l i39lli l f il l tl lllilll tile H E j lllt jHL Ring oscillator model of theta CPG A ring oscillator circuit can easily simulate theta cells if the activity bump circulates at the theta frequency of about 8 Hz 0 Each cell in the ring bursts on its own phase of the theta cycle Theta cell spikes lllll II a III llll Illl II lllll Illll Ill Illl llll Theta Frequency Hz 9 N 5 0 9 00 9 03 I 0quot 3 Speed dependence of theta frequency From Jeewajee et al 2008 Hippocampus 1821175 10 20 30 40 Running Speed cms theta freq between 4 and 12 Hz but usually 69 Hz 0 A number of studies have shown that the frequency of theta rhythm in both EEG and singleunit recordings tends to increase slightly with the rat s running speed 0 How can the ring oscillator model account for this dependence of burst frequency on running speed Shift of yintercept The main difference between grid From Jeewajee et al 2008 Hippocampus 1821175 9392 360 Hillim E 270 gas a 180 E86 iii L 90 cells and theta cells is their y intercept 2nd 227 V is slope spatial frequency should say 390 not 2 AW P lIl cgt 10 20 30 40 Running Speed cms running speed zero bump speed zero Speedmodulated theta CPG frequency is some constant plus what it was before if lambda a Q 27rd v UTRICLE W36 1 1 3 o2m A m w tr 2 ollso no 3 8 o If we imagine that the theta ring oscillator receives an excitatory driving input that encodes LINEAR velocity then the theta frequency will increase with running speed from a nonzero baseline When the rat sits still two bumps MOVE in different theta rings but AT THE SAME SPEED or angular frequency so that that they are not moving with respect to EACH OTHER bumps moving at same frequency so not moving with respect to each other exists a reference frame in which bumps are stayin s glltvlv lglEJra tTisgtay ggp U Vl It depends upon what reference frame we measure their position in g I RING 2 J theta cells 875 With respect to the neurons in the rings the bumps are moving But with respect to one another they are not moving So there exists a reference frame in which the bumps are 0 M 7 2 7L3 0 M 7 2 7 3 still when the rat is still V distance s 00 N Um Theta Freq Hz When the rat moves the two bumps AT DIFFERENT SPEEDS or angular frequencies so that that now they are moving with respect to EACH OTHER when rat starts moving frequencies are different YVVWVWVYVWW If running speed modulates the bump frequency with DIFFERENT SLOPES IN EACH RING then the bump frequencies will only be equal when running speed is zero The faster the rat runs the greater the difference between the bumps speeds and thus the faster the move with respect to each other RING 1 theta cells 1 V distance s 0 M x2 x3 Beat interference Sinusoidal tones that differ in frequency or pitch by just one cycle per second 1 Hz are very hard to tell apart from each other 2ms 1996 ms 1Hz is very small difference in frequency 500 Hz 501 Beat interference l x 501500 1 Hz envelop NW N Wm M WWW quot I Oscillatory interference models of grid cells Burgess amp O Keefe 2005 Burgess et al 2005 Giocomo et al 2007 Hasselmo et al 2007 constructively interfere when peaks a iihlme5 5e 08 move in and out of phase beat freq is time for one bump to go 360 degb t period f I SecCycle 2 1 around with respect to other bump I I W WWI IlllI W W I I v f2f1 beat spacing cmcycle Converting a time code into a lace codesound familiar as rat walks across tracks peaks are when t ey are in phase time code for rat s position WVWWVYWW VWWVYVWWWWMYYWW RING 2 1 r theta cells 1 ell The two theta rings store a TIME CODE for the rat s position in very much the same way that the barn owl s left amp right nucleus magnocellularis store a time code for azimuth The grid cells convert the time code into a place code very much like the nucleus laminaris computes a place code for azimuth Do grid cells and place cells derive their locationspecific firing by detecting synchrony among theta cells grid cells come from theta cells grid cell fire when 2 theta oscillator 25 cms 20 cm cells fire in synchrony so happens Ia Grid cell data from Haftlng et al 2008 e i E L 4 5 5 5 g 2W quot 339 5 E E 5 1 I Ildlalil39 Il39lquot 39Ilquotquot3939H I39 Hi m Hun in m vvquot 39 V V l v 1 v 11 a i 5 i i 39 C9 place cell only fire if many theta oscillators fire in synchrony less common so sparse code Place cell data from Foster amp Wilson 2008 l O Firing Rate Hz 5 O l l Position Ascending theta pathways target entorhinal cortex where grid cells are found and hippocampus where place cells are found origin of theta rhythm p 7 b I 13 if drug injected into medial septum 39quot quot Entorhinal Cortex Hippooampus A ten quot Mi 39 f 39 quotquot 39 39 The I 39 a w MOIate histogram of a theta cell Vme f Elm E n i Hill Spatial aloecte39y eulocmrelalion D r 21l39zat1blz Spazid autocorrddticn T39Jjettory r quot Baseline Sub Medial Septum rr 1247 p gm 1 0 E 9 a a I 1 Rate 39neo g39mross 219 sampled W 412541 p 37 m LII z s 12H grooms 071 l t x r K f Inactivation 39r39 02 125 rd39icss 4222 36 Hour Recovery n 374 p 51 1 39r Liaix 910 24 Hour Recovery 039 1311731 ick7 391 2Dl 1 a tilz grdwcss 045 Reprinted from Brandon et al 2011 Theta inputs to grid and place cells can be quotturned of by infusing drugs into the medial septum This temporarily inactivates the projection from medial septum to entorhinal cortex When this is done grid cells in the entorhinal cortex are severely disrupted Brandon et al 2011 Koenig et al 2011 Inactivating medial septum reduces place cell firing rates but spares spatial tuning T E Iidt f inie 3 Ear HIFFEEEHWF UE lELlLE rat 3513 v53 ll39ll j a5lquot ti ratf w TITLE 39 I it t t u I a ME It l3 Before inactivation inact reCOVerV Reprinted from Koenig et al 2011 The grid cell model is supported by the data but the place cell model is not 25 cms 20 cm Grid cell data from Hafting et al 2008 a E L 4 z a 2 i E it I H i I H 93 a 5m 3 3 E E 1 I Il I39IIIJIIEIIII39L ll I I lllll llll 39llll39 llllquotlllll grid ail Place cell data from Foster amp Wilson 2008 0 A 20 8 V EN 8 2 9quot 5 10 gt 0quot A E 39quotVVVVVV39 39VVVV39V39V LL 0 u place POSItIO cell Place cells in hippocampus of crawling bats Each cell fires at one or two preferred locations Different cells have different preferred locations evenly distributed throughout the environment 270 cm 2 Place cell recording in flying bats Example of bat flight trajectory Bat flies in a room containing an artficial tree at the center He flies around the tree to receive rewards from baited branches Bat wears a headstage homing electrodes and wireless transmitters 3D place cells in bats Top view XY Side view YZ Front view XZ Location specificity is preserved in cross sections through all angles 3D path plots and firing rate maps show that this hippocampal neuron fires in a specific 3D location As in rats different place cells prefer to fire in different locations K 10 Hz J 6H2 I l l III L t 1 1 7 p I t A Bat 1 n 10 place cells Ten place cells recorded from the same bat had different preferred firing N locations Hippocampal theta is transient in bats d LFP theta rhythm d E I b f WANNA appears on y In re 339 1s 39 4 25Hz bouts lasting 1 second M ax e There IS no promInent Min theta peak in the LFP power spectrum during S39eep Behav sleep or behavior 481420 4814 20 Frequency Hz Grid cells in MEC of crawling bats Adjacent cells had similar spacing amp orientation but differing spatial phase Dorsoventral gradient of spacings Some grid cells were directionally modulated others not Firing rates were modulated by running speed 9 0quot C 0 I 1 39 0 U C 39 0 j 395 o o 0 ct Entorhinal theta is transient in bats c Entorhinal theta also occurs in brief bouts jk Grid cell firing is present even when theta is absent suggesting that theta is not required for grid cell firing Full writlam Maul It Full With ui Ellhm EEEEJHDI EEH lmut 4 ulnly gl Illt sessilenu HEJIIEZIIIL r r tan 1161 156 crnlyr Do place and grid cells without theta in bats disprove oscillatory interference models Possible explanations Theta is present in bats but for some reason is not being observed Oscillatory interference occurs at a different frequency in bats not theta frequency The oscillatory interference frequency is not constant in bats but instead changes with time 1 Hz 8 Hz 2 Hz etc Recording multiple HD cells lpos m ll 1 c ADn Peyrache et al 2015 recorded multiple HD m cells simultaneously 9 from the anterodorsal thalamus ADn and postsubiculum P08 ADn a 39 a l 3 Iz ggg 33 Mind Reading Population Decoding When many HD cells are simultaneously recorded it becomes possible to decode their spike trains to reconstruct the rat s directional heading in real time This is like reading the rat s mind to see what direction it THINKS it is facing take spike trains of neurons and run through algorithm to estimate what direction animal is facing based off of neurons that are firing 360 Actual HD 360 econstructed HD 1 AD J FA 4 quot x A PoS I 39 V J I II II I 39 II II I 39 F39Im39l39 39l 39 I II II I I f 39 9 quotFl l quot39 39quot 394 III I I III I39ll III quotphl I 1 lllllquotl H I quotIquot quotm d H J IIIh W39LI 3939 qrq 39 I 39 I I II in I I Id In 39 d III 39 1 I I I 39F I x quot39 IIII3939I 39139 I 1 q I39 quotNilquot I quot quotr39 rdm quotquotquot quot P39IIquot mun 39I 1 l l 10 8 P08 39 IllaJ 439 ADn A rat dreams of turning can see what rat is dreaming about Awake Sleep Sleep during REM brain activity similar to wakefulness I l I l V i can see animal thinking of turning itself around 360quot Actual HD stops changing during m sleep because the rat s head is 360quot Still Reconstructed HD during REM on m looks like wakingrat is to 39 quot 3939quot39quotquotquotquotquott3939i39 i quot3quotquot 11quot 1 iaxw39ir39t39iwquot 39quotquot39 39 1 s 39M39 dreaming CE 13quot rquot 1 WW 1 I Ists lgHEL39m 39g39ng Raster plots show spike activity u Hm g gt during wake versus sleepwhat 5 39 IIIlS39f39 lmll 39l quot39 is going on during the SWS lt Mquotquot39 quot quot39 ll139llli3939t ifi39T f 3939 II quotL39lL3939 quotquot214 State g compressed replay patterns of activity 105 t 39 f 39 similar to real life but very compressed i SW3 REM During Slow Wave Sleep SWS l39 39 I I I I 391 I 3939 Mlquot 39 1 whim the reconstructed HD signal CL 139 39 39 l quot I I ll H I quotpf Ih39 quotHquot IHIu39ll39uh39u39 appears to generate very rapid 1 39 H ml m39 head turns that are highly 360quot Tha39amic Signa39 compressed in time about 10x W M faster than normal head 0 I turning 1 39 39J39 I Will I I quot m I 39 u 39l 39 Imll F c 1 I quot39Tl39l39il39 m H 139 39llili39llll39l39ll39Wu 39II39I I n39I During Rapid Eye Movement 2 151a 39 39 I I 1 I i Itquotlluuu REM sleep the reconstructed l at I I dblll 39 n m um u 39n u3 head turns occur at normal 250 ms 25 s waking Speed 2014 Nobel Prize for Physiology amp Medicine John Edvard MayBritt O Keefe Moser Moser brain s GPS system helped to understand the neural circuits that underly long term memory storage Stages of Memory Formation working memory High ICOI11C memory 8 3 Shortterm memory 4quot I H I 2 Intermedlateterm Eb S CD 3 an Low Henry Molaison 19262008 suffered frmere I surgically rempp main strucLuIie f r 39rTTwh at F come from quot 39 The human hippocampus The hippocampus seahorse was given its name by Bolognese anatomist Giulio Cesare Aranzi circa 1564 I mm Brainc omzection com Iv Hamish Lamarg Lupin101quot Hippoeampus amp Memory Modern studies of the hippoeampal role in memory began With studies of the famous neurological patient HM Large portions of HM s medial temporal lobe were reseeted to treat his severe intractable epilepsy HM have amnesia as result Seoville and Milner 1957 studied HM s resulting memory loss MRI scan of quotHMquot NOTE THE RESULTS OF HIS BILRTERRL MEDIHL TEMPORHL LOBE RESEOTION 9ND THE REMOURL OF THE HIPPOORMPUS William Sooville Brenda Milner HM HM s Memory Impairment 0 HM exhibited severe anterograde amnesia for almost all declarative memories of facts and episodes that he encountered after his operation anterograde is after surgery 0 HM also exhibited temporallygraded retrograde amnesia he showed better memory for declarative information acquired long before surgery than recently before surgery declarative memory are things you can recall and convey to someone through language eg not how to ride a bike 0 Procedural learning e g motor skill learning was found to be normal in HM Animals models Do animals have declarative memory Without language they cannot declare anything And yet all mammals have a clearly identi able hippocampus With very similar anatomical structure to humans So What do animals use their hippocampus for Early attempts at nding hippocampal memory impairments in rodents were not successful but it gradually became clear that rodents use their hippocampus to store memories of familiar environments hard to nd model for declaratwe memory Chronic neurophysiological recordings To further investigate the role of the hippocampus in learning and memory researchers began implanting electrodes into the hippocampus of freely behaving rats Before describing What they found it is useful to rst review the circuit anatomy of the hippocampus and discuss some of the different methods that can be used to record neural activity in freely behaving animals The rodent hippocampus The two hemispheres of the rat hippocampus resemble a pair of bananas joined at their stalks In rats the hippocampus occupies a larger proportion of the total brain volume than in humans mainly because rats have a much smaller cerebral cortex mm Bra m Connection mm I39l quot murals 14mm k 39IJtI39JHI The rodent hippocampus ll Atranverse cross 5 k m through the hf hippocampus slicing Jl 3 a the banana reveals a highly organized a circuit commonly 3 391 referred to as the 39 9 jf trisynaptic loop w 11 C A l The circuit architecture of the hippocampus was rst sketched by the Spanish neuroanatomist Ramon y Caj al d V quotquot I f Juro s hv 1133 wquot l u ram 35 139 egv39di l s quot I quot 5 44 I y 3395 39 39 O 39 if muff v1quot quot r 394 to o 39 O 1 I a 390 quot quot 39 Q In transverse section the hippocampal circuit consists of two layers of cell bodies that form interlocking C s they look like a sweet roll One of the cell layers is called the dentate gyrus DG and the other is called Cornus Ammonu which mean s Ammon s Horn in Latin abbreviated CA Two major subdivisions of the CA region are CA3 and CA1 dentate gyrus has dentate granule cells o quot Pyramidal Cells bubiculum 2253 Pyramidal Ingmar 39 39 v 39 a 1 p 39 nn 39 39 0 r c 11 39 39v F S N ea 9 a 39 Medial quot1 f Granule 39 quot Entorhinal Pyramldal 397 Cells Cortex Cells 39 CA3 pp The principle cell populations of the hippocampal are excitatory glutamatergic neurons that send their axons to neurons in other parts of the hippocampal circuit basic hippocampus circuit is highly conserved across species must serve important role trisynaptic loop three synapses bolded arrows below CA1 Pyramidal Cells bubiculum Pyramidal Cells Schaeffer Collaterals Mossy 39 Dentate MGd al CA3 Fibers Granule Entorhinal P ramidal yceus Cells Cortex Perforant Path J The classical trisynaptic loop circuit bold arrows consists of 1 the perforant path projection from medial entorhinal cortex to the dentate gyrus granule cells 2 mossy fiber projection from dentate granule cells to CA3 pyramidal neurons 3 Schaffer collateral projections from CA3 to CA1 pyramidal cells Output from CA1 is relayed back to entorhinal cortex via Subiculum to close the low CA3 cells excite themselves looped blue arrow below Schaeffer Collaterals CA1 Pyramidal Cells Subiculum Pyramidal T Cells Mossy Temporoammonal Dentate Medial CA3 Fibers Granule pathway a Entorhinal P ramidal yCeuS Cells 39 Cortex 39quot J Perforant Path Additional pathways include 1 perforant path projection from entorhinal cortex to CA3 2 temporoammonal projection from entorhinal cortex to CA1 3 excitatory recurrent collaterals from CA3 neurons onto themselves Field EEG recordings A large lowimpedance electroencephalographic EEG electrode can record eld potentials generated by concerted electrical activity crowd noise from hundreds or thousands of neurons large wire record electrical activi field potentials are measuring electric fields around neurons crowd noise similar to crowd of neurons creating lots of activity to record Field EEG recordings Recording eld potentials is a little bit like recording the crowd noise at football game by hanging a microphone from a blimp t PJsr Hal 0quotl l 1 Jv39i quotla 39IUJSA 39I i MIN if 39 v uvih 1quotquot yi hl39li illt39 mov mEnt movement I i I I f i I I 2 wwh j or j Ii10x v Jl ctquot 79 quot1A u uh oiic quot39Iquot JIL 4n 22 mch jump I v t l O 395 quot3 39 3939 0 wt 39quot r l r 1 3 39 l Jquot3939 v I I I 39 4 l 39 quot 39rquotquot39vvil placed In box 11 incmump 39 r 39i Iquot 4 l lhww m r39w 39w Wwquot39MHu wa Jquotit V I W H39HlI39 39 3 quot Jvu39539leJ IquotHquot H39M vw WCKeU Up placed In water 39 318nm chmb Out i i 39 f 39 39 s I 39 39 I 0 a 39 p39 I i 0 s I 39A a r 39 no I 39 5 I 1Tquotk1 I quoth T ii 39 39 v o 39 I r I Slltlng sml teeth chatter head turn Sumng Still 1 it 1 quot35 Lii39r39lli39i quot H a M l 6 ivva i 39453993 quot quotfr quotMEL 3 VI sleep 1 350 mV I 7 alwwwlil tmmwLNJLW MW sleep penml last Rec EEG in recordings in freely behaving rats Vanderwolf 1969 placed an EEG recording electrode into the CA region of the hippocampus and recorded spontaneous activity in freely behaving rats He observed that there were several distinct patterns of crowd activity in the hippocampus and each type of activity accompanied a different kind of behavior Theta rhythm and Sharp Waves Theta rhythm is a 68 Hz oscillation that occurs during voluntary movement such as When the rat is navigating through its environment tend to occur when walking around not still lll l w39lc Wlalwml quotlwl mll l quot ju lw39 i 39 v39l lililuql qh 1 mwwmvictimllquot movement moveman I i I J J i r I o 39 2 Wrw 39rq R39 39 39J v k nquotlquotl quot3quot 139quot lquot39 0 0quotquot quot l39t 4 3922 inch lump I a v x 39WK v 11 inch jump IJII 0 quot i 39 3 quotJ1b39 39vquot 39 39 0quotquot I h placed In box ll PHH ali39r 39x quotdtu39H n39wquotMquot H Jil ltlili39il 4 NWVL r lit ul f u ll lw l39nquotquot39 i 39 u39h39llllimpllll ww placed m water 39 Climb Oul theta rhythm is synchronized oscillation one of the patterns of activity picked up 3 wun 39 Sharp wave ripples SWRs are synchronous bursts of activity that occur mainly during quiet wakefulness when theta is absent swrs occur during slow wave sleep when still or quietly awake 1 H4210 10kHz 500 H7 to 10 kHz 2 l 100 ID 400 HI 3 4 w 1 to 50 Hz 100 ms I Singleunit recording 39 Small highimpedance microwire electrodes can snuggle up next to individual neurons and record their action potentials i M 39l 9 at k U B l i ggmug a illiiw p llgi39l392 n lgl I Chronic SingleUnit Recording Neurons Firing Action Potentials P lace cells O Keefe amp Dostrovsky 1971 discovered that many hippocampal neurons are place cells that re only when the rat visits a speci location in the environment the place eld g This path plot shows an overhead view of a square boX The squiggly gray line shows the path that a rat followed as it wandered around in the box The black dots show all locations where action potentials were red by a single hippocampal place cell dark spots are showing high firing place field of cell This ring rate map shows the average ring rate of the same cell at each location in the box Darker colors indicate higher ring rates The cell s place eld is clearly visible as a dark area of high ring Spatial Population Vector Different place cells each have their own preferred ring location place eld so the set of all place cells can store a population vector code for the rat s spatial location shows where cells like to fire place cells don t have actual topographic organization of place cells artificially place in small boxes below Slmultaneous recordan of 30 dlfferent place cells from the I same rat shows that each has its own place field in the box 39 D quot I Q C 39I39 a tu a 0 39 39 Place cells are excitatory pyramidal neurons in layers CA1 and CA3 Each cell s place field tends to remain stable across repeated visits to the same environment This is appears to be form of longterm memory for spatial locations because the place cell recognizes when the animal visits a familiar location in space D16 U 5 1 D18 D19 D20 021 U N 00 Firing rate maps are often plotted as colored heat maps with hot colors indicating areas of high ring and cool color indicating areas of low ring Lever et al 2002 allowed rats to forage freely for food pellets in a circular arena the rats foraged for 20 minutes a day every day for two weeks The ring rate maps at left show the activity of a single place cell that was recorded from CA3 on consecutive days days 1623 39 Notice that the place cell res at exactly the same location every day this cell seems to store a kind of memory that may allow the rat to recognize this speci c familiar location cells have constant place preference this is a kind of memory recognize familiar places this is a form of declarative memory Lever et al 2002 Nature 41690 Even distribution of eld centers In a cylindrical chamber place cells can exhibit ring elds that are against walls in the middle of the oor etc During freeforaging place cells are randomly distributed throughout the environment goal locations are preferentially Preferred ring locations ARE NOT topographically organized IIIIIIIIIIIIIIIIIIII I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I m I I I I I I ll I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I m I IE I39 IIIIIlIIlIIII IIIIIllI I II I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I n I m m I m I In uence of visual landmarks When prominent familiar landmarks are moved place cell ring elds often move along with the landmarks 0 For example When a cue card is rotated in a circular arena place cells move with the cue card similar idea to manipulating head direction cells Removal of the cue card often does not disrupt place elds LIGHT DARK LIGHT Place cells can maintain their ring elds in complete darkness The Hippoeampus as a Cognitive Map John O Keefe amp Lynn Nadel 1978 Hypothesis The hippoeampus stores mental maps of spatial environments these are literally stored memories A 2D attractor network instead of having circle of neurons for activity bump have conceptualized sheet of place The populatmn Of place cells activity bump shifts through sheet in ceus can be a correspondence to rat movement through conceptualized as a sheetenvimnment of neurons We can imagine a bump of activity that shifts across the sheet in correspondence 39 39 39 39 with the rat s movements through the environment Could this sheet of place cells perform linear or translational path integration in much the same way that the ring attractor performs angular path integration
Are you sure you want to buy this material for
You're already Subscribed!
Looks like you've already subscribed to StudySoup, you won't need to purchase another subscription to get this material. To access this material simply click 'View Full Document'