capita selecta 27.03.2012 mcnaughton

8/2/2019 Capita Selecta 27.03.2012 McNaughton

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Brainbow micrograph by Dr. T. Weissman

Hippocampus

Association

Cortex

Primary

Cortex

Squire et al.

1989

Doughnuts in the brain:

periodic boundary

conditions on the brains

spatial coordinate

system

Dentate Gyrus

CA1

CA3

Neocortex

neocortex


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Dentate Gyrus

CA1

CA3

Neocortex

Bruce L. McNaughton Ph.D.

AHFMR Polaris research Chair,

Dept. Neuroscience

The University of Lethbridge

Lethbridge, Alberta, Canada

Visiting Professor KUL, NERF

[email protected]
http://www.nia.nih.gov/


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Dentate Gyrus

CA1

CA3

Neocortex Carol Barnes

Bill Skaggs

Alexei SamsonovichMatt Wilson

Jim Knierim

Kati Gothard

Min Jung

David Redish

Alex TerrazasFrancesco Battaglia

Drew Maurer

Zaneta Navratilova

May-Britt Moser

Edvard Moser

Jill LeutgebStefan Leutgeb

Francesca Sargolini

Ole Jensen

Laura Colgin


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Rat brain

150,000,000 neurons

15,000,000,000 synapses

~1.9 GB RAMProcessor speed ~1.5-15 G logic

operations per second

Power consumption ~0.05W

Volume 2 cc Weight ~ 2 g

iPad

0.5 GB DDR2 RAM

Processor speed 1 GHz

Power consumption 1.5W

Volume ~ .3 cc Weight ~0.7g
http://compare-processors.com/wp-content/uploads/2011/10/apple-a5.jpg


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5 x 104 neurons

3 x 108 synapsesper mm3

Vias ~ 100 nm

Fully 3-D interconnects


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through-wafer direct die-to-die

copper area-array interconnections

Gate pitch ~ 100 nm

Connectivity very lowcompared to brain, and

mostly limited to 2-D
http://flipchips.com/wp-content/uploads/2011/11/tut71fig1.jpg


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Figure adapted from Pinel, 2000

The Hippocampusreceives highly

processed inputs from a

variety of cortical and

subcortical areas.

Hippocampal outputs

are widespread

throughout the cortexand subcortical regions.

Hippocampus is part

of cortex

Hippocampus is essential for new

memory formation


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Felleman & Van Essen '91

Squire et al., 1989

The cortex is arranged as a hierarchical set ofvertically

and horizontally interacting modules. The Hippocampus

is the TOP of the hierarchy

CONNECTIONS OF THEVISUAL SYSTEM

hippocampus

retina

hippocampus


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In 1957, an epileptic

patient (H.M.) underwent abilateral temporal lobectomy

(Scoville & Milner, 1957).

This procedure had an

immediate and devastatingimpact on H.M.s memory

capabilities, although his

cognitive capabilities were

relatively intact (e.g. IQ).

Hippocampus is essential for

new memory formation


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Retrograde amnesia Anterograde amnesia

Time of hippocampal damage

Time

Hippocampal dysfunction leaves old knowledge

relatively intact, but disrupts recent memory and new

learning: why?

Consolidated memory Not yet consolidated memory

Present

Recall efficiency as a function of the time (before or after

hippocampal damage) when the item was experienced


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WHAT IS MEMORY?

Associative memory is the

ability to retrieve the whole of an

experience from one or more ofits parts

Seeing the glass fall is typically

followed immediately by a


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WHAT IS MEMORY?

Associative memory is the

ability to retrieve the whole of an

experience from one or more ofits parts

Seeing the glass fall is typically

followed immediately by a

CRASH


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WHAT IS MEMORY?

Associative memory is the ability to retrieve the whole of

an experience from one or more of its parts

Hearing the crash Seeing a glass fall

Associativesynaptic

links form

During learning, the

near simultaneous

occurrence of two

sensory inputs resultsin the formation of

associative synaptic

links among the brain

cells that represent

the events


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WHAT IS MEMORY?



Remembering the crash Seeing a glass fall

Activationspreads via

associative

synaptic

links

During recall, the

occurrence of part of

a stored experience

causes retrieval of therest of the experience

via associative

synaptic links that

were formed during

learning. We call thispattern completion


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WHAT IS MEMORY?




Activation

spreads via

associative

synaptic

links

During recall, the


a stored experience


via associative

synaptic links that

were formed during



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WHAT IS MEMORY?




Activation

spreads via

associative

synaptic

links

During recall, the


a stored experience


via associative

synaptic links that

were formed during



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WHAT IS MEMORY?



Hearing the crash Remembering a glass fall

Activation

spreads via

associative

synaptic

links

Recall can be

bidirectional


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First some basics: Brain

cells (neurons) and how

they communicate with

each other.


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First some basics:Brain

cells (neurons) and how

they communicate with

each other.

The human cerebral

cortex contains

more than 10 billion

neurons

dendrite

cell

body(soma)

axon

synapse

Neuronj

Neuron i

Wij

NEURONS and SYNAPSES

S


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synapses

Each cell

sends and

recievesabout

10,000

Usually about 300 -

400activesynapses are

needed to excite a

cortical neuronS

Wij Wij


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Classification based on neurotransmitter


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Guerra et al 2011Classification based on morphology


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Figure 3. Profiles of gene expression and electrical behaviour.

Toledo-Rodriguez M et al. Cereb. Cortex 2004;14:1310-1327

2004 by Oxford University Press

Classification

based on gene

expression and

biophysics

Th l ti l dCl ifi i b d i i


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Thalamo-cortical and

cortico-thalamic circuit

http://www.med.yale.edu/neurobio/mccormick/mccormicknew/Index.html

Classification based on connectivity


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Cerebellar cortexbasic circuit

Synaptic circuits of the striatum


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Synaptic circuits of the striatum

Annu. Rev. Neurosci. 2011. 34:44166

Gerfen & Surmeier Annu. Rev. Neurosci. 2011. 34:44166


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Symbolic neurons and neural networks

-

+


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AD.O. Hebb (1949)

provided the key concept

underlying the moderntheory of neural

associative memory

Cells that have fired together form

associative groups (assemblies)

linked by mutually strengthened

synapses, such that reactivation ofsome members of the group leads

to complete retrieval of the active

group

cells that fire together wire together'


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Networks with modifiable recurrent connections

could perform autoassociative memory (Marr,1971).

The mutual connections among neurons

participating in a given cell assembly enable the

retrieval of a complete pattern from any pattern that

is a unique fragment of the original (pattern

completion) or that resembles the original closely

enough (error correction). [from McNaughton &

Morris TINS, 1986]

David MarrA Theory for Cerebral

Neocortex, 1970

Simple Memory: A

Theory for Archicortex,

1971


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neural energy landscape model of pattern recognition

``Neural networks and physical systems with emergent collective

computational abilities'', Proc. Natl. Acad. Sci. USA 79, 2554 (1982)

J.J. Hopfield

Hopfield Net


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Content Addressable Memory(aka autoassociative memory)

The current standard models assume complete

(all-to-all) connectivity

Th bl i ' k D h h!


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The problem: it won't work. Do the math!

Cortical connectivity is much too sparse.

The typical corticalneuron (j) recieves

about 10,000 synapses

from other neurons (i);

but there are about10,000,000,000

neurons.

On average, each

cell recieves input

from onlyone in a

million other cells.

i

j

The synapticweight matrix

Wij

1010

1

1

1010Hypothetical Connection Map of Cortex

di i i i d l hi hi l k


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Indirect association in a modular, hierarchical network

d l ( ll ld ) hi hi l i l


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Hippocampus

Association

Cortex

Primary

Cortex

Hippocampus

Association

Cortex

Primary

Cortex

Squire et al., 1989

Modular (small-world), hierarchical, reciprocal

structure may provide a solution to the sparse

connectivity problem


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Indirect Association:During the initial encoding of the memory, output of the top module may become associated

with the patterns in each lower module. This provides anindex code for each memory.

Recreating the index pattern evokes the corresponding patterns in the lower levels, thus

completing the retrieval of the whole memory

Retrieval cued

by external

event

Spontaneous

retrieval

Compound event

encoded over

different modules


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Why does damage to the hippocampus impair

new learning?

Because there is no index module to enable

indirect association


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The cortex is highly modular,

meaning that there is are

groups of cells that are more

densly interconnected with

each other than with cells in

other modules.

Modules contain a fewthousand neurons.


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Top-down projections terminate in NMDAR

rich superficial layer of neocortex

CA3

CA1

Subiculum

Dentate

Gyrus

Entorhinal

Cortex


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Firing Rate Map

Listening to a single hippocampal neuron.Neurons in the rat hippocampus are very selective. The

traditional question had been: "what do they encode?".


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Firing rate maps for 80 simultaneously recorded hippocampal neurons while rat ran

in a square arena. (High rate cells are interneurons) (Wilson & McNaughton, 1993)

How the hippocampus creates top-down

spatiotemporal links for neocortical memory

Firing Rate Map


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How is the spatial code updated as the animal

changes its location? What sets the scale?

Firing Rate Map


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Is the code 'purely' spatial? What kinds of

information are actually stored in hippocampus?

Firing Rate Map

Multi neuron recording enables us to read out the


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Multi neuron recording enables us to read out the

contents of brains codes. For example, it allows

us to compute in real-time where the rat is and

where he is planning to go, just from its brain

activity.

Blue: Actual path of rat in a box

Red: Path predicted from firing rate maps


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"Path integration" in rodents. Without vision or any other external cue, rats can make a

tortuous outward journey from 'home' and then return on a direct route. To do this they

must keep track of changes in direction and distance travelled. (from Mittlestaedt &

Mittelstaedt, 1980)

Head direction cells located at input to hippocampal formation and


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30

210

60

240

90

270

120

300

150

330

180 0

10 Hz

5 Hz

Single HD cell tuning function

in polar coordinates

within it

Ranck, 1984; Taube et al., 1990;


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information about distance traversed.


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Raw

EEG

7 Hz

150 Hz

Spikes

(2 sec)

The distance moved by

the rat can be accurately

gauged (by an externalobserver) by integrating

the theta power over

time

Theta power increases linearly with running

speed(there is also a small change in frequency)

Terrazas & McNaughton '03Time

Position rat position

theta integral


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Under conditions of restraint, hippocampal

neurons (and head direction cells) become silent

Foster, Castro, McNaughton 1989. Science 244:1580-1582


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Muller et al., J. Neurosci. 1994

During random

foraging in two

dimensions, place

cell firing is

independent ofdirection.

N

S

W E

Average

The place code is updated by

path integration

The place code is maintained in darkness


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Once initial position is established in light, firing locations persist in

dark. (Each cluster of colored dots represents spikes from different

neurons) ALSO, once initial position is established in dark, firing

locations persist in light!

The place code is maintained in darkness

External cues do not specify firing fields, but can (sometimes)


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(Fuhs, VanRhoades, Casale, McNaughton & Touretzky, J.Neurophysiol. 2005, 94, 2603-2616; Skaggs & McNaughton

1995)

In the Opposite orientation condition (O), the

path integrator overrides the effects of the visual

landmarks; but in the Same orientation condition

(S), the cues reset the path integrator.

realign the path-integrator

The size of place fields is essentially independent of the


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The size of place fields is essentially independent of the

rate at which external inputs change

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Bin Number along Track

BinNumberalongTrack

Overlap of Population Vectors along Track Bin Number

10 20 30 40 50 60 70 80 90

10

20

30

40

50

60

70

80

90

180 cm track

CUE-RICH SECTION CUE-POOR SECTION

Decorrelation distance is

independent of cue density

Battaglia et al., J. Neurosci., 2004, 24(19):4541-4550


path integration

Rich

Poor

Knierim, Kudrimoti, McNaughton.


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, , g

J. Neurophysiol. 1998

Place fields

before and after

fast or slow

rotations of the

apparatus with

rat


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In uniform cylinder there is

random drift of the directional

coordinate.

Head direction cells and

place cells always co-rotate as an

integrated ensemble.


path integration


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Hippocampal topology:


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ppoca pa topo ogy:

hippocampus is like a folded

sheet of extended cortex. It

has two principal axesthe

longitudinal axis and thetransverse axis. Transverse

slices look very similar

anywhere along the

longitudinal axis, but there are

important gradients.

CA3

CA1Subiculum

Dentate

Gyrus

Entorhinal

Cortex

dorsal

ventralmiddle


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Right Hemisphere

Left Hemisphere


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Basic wiring diagram of hippocampal formation


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Daniel Amit1938-2007

1989 - attractor

neural networks.

1991continuous

attractor neural

networks (with M.

Tsodyks)

neural energy landscape for continuous functions

Theoretical distribution of


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Neurons (conceptually) ordered according to preferred value of parameter

Firing

rate

Theoretical distribution of

firing rates in a population of

parietal cortical neurons tuned

to different horizontalpositions of eye in the orbit

Left Right0

Population tuning function

(aka activity packet,bump,or hill )


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Neurons (conceptually) ordered according to preferred value

of parameter. Local excitatory connections. Global feedback

inhibition (not shown) limits net activity. Bumps become

stable states.

0

Continuous attractor

neural network in 1-D)


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The effect of local excitatory connections andglobal

inhibitory connections is a single bump of excitation

Continuous attractor neural networks in one and two

dimensions


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Quicktime movie


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Continuous attractor neural networks in one and two

dimensions

To perform angular or 2-D path integration, you have to

make the bump move consistently with the rats movement.

Attractor network model for head direction cells. Key elements


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30

210

60

240

90

270

120

300

150

330

180 0

10 Hz

5 Hz

are a ring attractor, and a set of cells conjunctive for direction

and angular velocity with offset return connections. This is the

so-called hidden layer of classical neural network theory.

(McNaughton et al., 1989; Skaggs et al., 1995)

conjunctive cells

head-direction cells

The identical concept in 2-D (Samsonovich & McNaughton, 1997)


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Intermediate layer contains cells that are conjunctive for

location, head direction (and are linearly sensitive to speed)

Linear

motion

signal

Head

direction

signalConjunctive cells


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There is a finite number of cells, so the map cant be infinite.

What happens when the rat gets to the edge of its map?

?


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Samsonovich and McNaughton 1996

i h h


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Samsonovich & McNaughton (1997)

implemented the map on a standard torus

(periodic boundary condition), which

predicts regularly repeating square grid

of place fields.

0 50 100 150 200

0

20

40

60

80

100

120

140

160

180

200

Microstructure of a


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Microstructure of a

spatial map in the

entorhinal cortexHafting, Fyhn, Molden,Moser & Moser1

Nature 2005

Place fields of layer II

medial entorhinal cortical

cells have a very regular

triangular (rhomboidal)

grid-like structure

a


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b

Rhomboidal grid could

arise from a simple

distortion of a

rectangular map withperiodic boundaries: a

twisted torus

0

50

100

150

200

020

40

60

80

00

20

40

60

80

00

0 50 100 150 200

0

20

40

60

80

100

120

140

160

180

200


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R

O

L

L

Sargolini, Fyhn, Hafting, McNaughton, Witter, Moser, and Moser (2006)


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In addition to grid cells in MEC, there are head

direction cells and conjunctive head direction x grid x

speed cells in deeper layers.These are exactly the cells required to implement path

integration according to the continuous attractor model.

EC is likely to be the path integrator.

head direction cell Conjunctive grid x head direction cell


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What sets the spatial scale ?


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One simple definition of scale is the average

size of the place fields.

Firing Rate Map


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Another definition of scale is the rate at which the

population activity changes as position changes.

Firing Rate Map

PVC ll #


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Population vector for a one dimensional space

1

2

3

.

.

.N

Cell #

PVPVC ll #


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Nearby vectors are correlated

1

2

3

.

.

.N

Cell #

PV PVC ll #


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Correlation decreases with distance

1

2

3

.

.

.N

Cell #

PV PVC ll #


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For bigger place fields, correlation decreases more

slowly with distance

1

2

3

.

.

.N

Cell #

Spatial scale can be


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0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Bin Number along Track

BinNumbera

longTrack

Overlap of Population Vectors along Track Bin Number

10 20 30 40 50 60 70 80 90

10

20

30

40

50

60

70

80

90

p

defined as the distance

over which successive

population vectorsbecome decorrelated.

One dimensional case:

Battaglia et al. 2004

-50 0 50-0.2

0

0.2

0.4

0.6

0.8

1

distance on track (cm)

pop.vectorc

orr.

Population vector correlation matrix

Place fields get bigger, and


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Jung, Wiener, McNaughton(1994). J. Neurosci. 14(12): 7347-7356.

Middle

Dorsal

dorsal

ventralmiddle

fewer cells are active at a

given location as recording

location move ventrally

dorsal


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dorsal

ventralmiddle

Spatial scale increases

systematically along the

dorso-ventral axis of the

hippocampal formation

Jung, Weiner, McNaughton 1994

Maurer, VanRhoades, Sutherland, Lipa, McNaughton, 2005

Hafting et al., 2005


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Grid scale also increases systematically along the septo-

temporal axis. Grid orientation is constant.

"Fourier Synthesis" of Place Fields

Combining grid fields at different spatial scales could lead to non


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Combining grid fields at different spatial scales could lead to non-

recurring place fields such as observed downstream of the

entorhinal cortex in hippocampus

(McNaughton, Battaglia,

Jensen, Moser and Moser.

Nature Rev. Neurosci. 2006);

Solstad et al. (2006)


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Reducing the gain of the motion signal would make the

bump move slowergrid scale would look bigger

Linear motionsignal

Head direction

signalConjunctive cells

00

20

40

60

80

00

20

40

60

80

00


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50

100

150

200

020

40

60

80

00

Bump speed sets the spatial scale

Terrazas, Krause, Gothard, McNaughton, Barnes (J. Neurosci 2005)


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Deletion of self-motion signals makes the hippocampal code

change more slowly with position: place fields get bigger

Place field expansion after degradation of movement signal is as

predicted by slower bump movement: overlap distribution preserved


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predicted by slower bump movement: overlap distribution preserved.

Like this

Not like this

Also, population sparsity is preserved, in-field peak rates decrease

Aprediction of the

toroidal attractor model

Grid Scale Quantization


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Speed signal

toroidal attractor model

(but not the interference

model):independent modules

from dorsal to ventral

MEC, so each module

can have a differentmovement speed gain.

One bump cant move

simultaneously at

different speeds.

This prediction

was recently confirmed

Dorsal MEC

small scale =high speed

Ventral MEC

large scale =

low speed

tetrodes were moved tangentially along layer II of MEC


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Thus, grid cells are organized into modules with similar spacing

and orientation, consistent with a network mechanism

Stensola, Stensola, Solstad,

Frland, Moser, Moser,

unpublished (slide courtesy

May-Britt Moser)

This showed discrete steps in grid spacing

Is the code 'purely' spatial? What kinds


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p y p

of information are actually stored in

hippocampus?

Classical view of activity

b i th


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Cells ordered by location parameter relative to

current rat location

Firingra

te

bump is a smooth

distribution with rate falling

off as a function of distance

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50

0.2

0.4

0.6

0.8

1

Independent Codes for

Spatial and Episodic


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In Samsonovich & McNaughton attractor map model, external

(cortical) inputs (V) become Hebbian associated with bump locations,

to enable correction of the PI by landmarks. This implies that external

inputs affect how much cells fire, but not where they fire!

Constant Place - Variable Cues

Constant Cues - Variable Place

Memory in Hippocampal

Neuronal EnsemblesLeutgeb, Leutgeb, Barnes,

Moser, McNaughton, Moser.Science, 2005

Constant Place - Variable Cues - RATE REMAPPING

Variations in cues affect how strongly cells fire, but not which cells can fire. The


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-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50

0.2

0.4

0.6

0.8

1

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50

0.2

0.4

0.6

0.8

1

potentially active population is determined by the path integrator.

Fluctuations in the

activity bump are

not all noise butreflect the influence

of external cues on

the cells encoding a

given locationCell # / location

1,2,3,n

1,2,3,n

Independent Codes forSpatial and Episodic


Neuronal EnsemblesLeutgeb, Leutgeb, Barnes, Moser,

McNaughton, Moser. Science,

2005

Constant Cues - Variable Place - GLOBAL REMAPPING

Variations in cues affect how strongly cells fire, but not which cells can fire. The


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potentially active population is determined by the path integrator.

Cell # / location

1,2,3,n

1,2,3,n

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50

0.2

0.4

0.6

0.8

1

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50

0.2

0.4

0.6

0.8

1

Fluctuations in the

activity bump are

not all noise but

reflect the influenceof external cues on

the cells encoding a

given location

Independent Codes forSpatial and Episodic


Neuronal EnsemblesLeutgeb, Leutgeb, Barnes, Moser,

McNaughton, Moser. Science,

2005

Inbound Outbound


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Independent codes for places and events

Pl 1


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Place 1 Place 2

r ~ 0

Constant Cue

Variable Place

Cue 1 Cue 2

r > 0

Constant Place

Variable Cue

The population vectors of

active neurons on two visits

to the same place spanstatisticallycorrelated

subspaces, even if the cues

are different.

The population vectors ofactive neurons in two

different places span

statistically independent

subspaces, even if the cues

are similar.

Place 1

Place 2

spatial context sensitive


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Hippocampal recipient layers are modality AND context sensitive

context insensitive

spatial context sensitive


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How could a toroidal synaptic matrix

be wired up by self-organization?

What if inhibition is not global?


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Range of excitatory connections

Range of inhibitory connections

Mexican hat function or DOG (difference of Gaussians)



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Network forms multiple bumps at closest packing density allowed by range of inhibition



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Network forms multiple bumps at closest packing density allowed by range of inhibition

0

20

40

60

80

00

The effect of local excitatory connections and localinhibitory

McNaughton, Battaglia, Jensen, Moser & Moser (Nat. Rev. Neurosci. 2006)


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This phenomenon was firstdescribed theoretically by

the mathematician Alan

Turing (but in a different

context).

Spontaneous symmetry

breaking in a

topographically

structureless Turing layerwith short range excitatory

connections and longer

range inhibitory

connections

y y

connections can be multiple bumps of excitation in one or two

dimensions

The mexican hat function: Short range

excitation & long range inhibition

Can a topographically structureless cortical sheet that gives rise



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to a grid of activity in brain space be used to organize

connections in a network that will give rise to

The mexican hat function: Short range

excitation & long range inhibitioncells that fire

topographically in a grid

like fashion inphysical

space? In other words, can

a toroidal synaptic matrixbe self-organized during

early development?

Turing layer



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Turing layer

Mexican hat connections

developing grid cell layer

random connections (Hebbian learning)

random top down

connections

(competitive learning)

Impose

random

drift of grid

phases

Output layer self-organizes as a



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Output layer self-organizes as a

toroidal synaptic matrix


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capita selecta 27.03.2012 mcnaughton

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