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13th November 2013

The hippocampus and spatial representation
Place cells
A rat explores around the box with distal cues for orientation looking for randomly scattered pieces of food
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e
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It
was discovered by O’keefe and Dostrovsky (1971)
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The firing range of the place cells can be called ‘place field’ and it is encoded in the hippocampus
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In epileptic patients, the
electrode recordings show properties of place cells; also in VR games, place cells-like cells have been found
in the hippocampus
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However, in narrow arm mazes, where the animal is running back and forth on linear tracts the
place cells fire differently depending on the direction of movement of the rat
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If all cues are removed, the place cells might
rotate slowly because the mouse doesn’t know the definitive location anymore
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(Rumelhart
and Zipser 1986)
Normalisation must occur to prevent the same connections increase in weight, as this will mean there will
always be the same output neuron winning
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Two ways to achieve this:
1
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Done by hand, change the value of each connection weight by dividing it by the total length of vector
of connection weights, so the total length of vector of connection weights will always be 1
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Alternative combined learning rule: wkj→ wkj+ ε(xjn wkj)
Sharp’s (1991) model of place cell firing
Sensory inputs  competitive learning  entorhinal cortex
with functions similar to place cells, but not as accurately
tuned  competitive learning  Place cells in hippocampus
(CA1 and CA3)
One set of neurons code for distances, one set cues for
directions
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The distance cues are more strongly tuned to the output neuron, the direction cues are broadly tuned
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The neurons in the entorhinal cortex will also become more sharply tuned
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The place cell firing is resistant to cue-removal
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The place cells also respond irrespective of direction
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Then Sharp restricted the movement by only allowing
the rat to run in certain directions, then particular neurons tend to only fire when the animal is facing a
particular direction
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A first time recording will show place cell firing in a new environment
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The first time recording, place cell will fire independently of
direction and learns to only fire in one direction
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2000
They recorded place cell firing in a reformable rectangular box
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The place cell tend to fire in the same corner even when the box is in
different configurations
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But
when the place cells that don’t fire in the corners are investigated, the actual distances from the walls to the
box plays important roles in telling the place cell where the rat is
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The place cells seem to have information about the distances of the boundaries of its
environment that is direction independent
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There are sharper tuning of BVCs for
shorter distances, more broad tuning for longer distances
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Place fields are modelled as the threshold sum of 2 or more BVC firing fields
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Place cell firing in new environmental layouts can
be predicted suing BVCs
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2009)
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It is good at capturing the sensory aspects, but it doesn’t take
learning into account
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There must be a role in
synaptic learning (see below)
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2000) and
robustness to cue-removal (Nakazawa et al
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The firing itself is not dependent on NMDA receptors, but
stability is important
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NMDA receptors
in CA3 is crucial for robustness of place cell firing to cue removal (Nakazawa et al
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13th November 2013
Also slow experience-dependent ‘remapping’ of the place cell representation occurs (Lever et al
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2006, Rivard et al 2004)
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Geometric determinants of the place fields of hippocampal neurons
O’keefe and Burgess 1996
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Place fields are modelled as the thresholded sum of two or more putative inputs of specific
functional form
Inputs have a Gaussian tuning curve to the distance to a wall in a given allocentric direction
Each of the inputs is tuned to respond maximally when there is a wall at specific distance along a
specific direction
The breadth of the tuning increase with the distance to which it is tuned
This model account for the consistency in the location of firing between envrinoments of different
size and aspect as well as various features of the shape of the fields, such as their elongation in
rectangular environments compared with square
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The receptive field of each BVC is a product of two Gaussians, one a function of distance, the other
of allocentric direction
The model explains the formation of place fields in terms of the interaction between the geometric
properties of the environment, and the geometric tunings of a set of inputs
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If learning occurs, it is not necessary to understand the shape and location of the fields in the
experiments dealt with above
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What constitutes a boundary?
The main determinant of BVC firing is the vector from the rat to the boundary regardless of the color,
material, or shape of the boundary
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Our evidence suggests that boundaries may be defined by both
sensory cues and limitations to movement
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3C) indicate that they are not only visual, whereas BVCs firing offset
from the boundary (Fig
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Thus, a boundary is an abstract concept that may reflect sensory properties of environment features such as
the sight or feel of a wall or an extended edge, as well as impediments to movement
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Subicular BVCs are impervious to environmental changes which cause PC remapping
The difference between environment a vs environment b produced strong remapping in CA1 PCs without
disrupting BVC firing
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However 14 of 17 BVCs were primarily insensitive
even to this change
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Relationship of subicular BVCs to the wider hippocampal formation
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Future questions:
Where does the sensory information driving the firing of subicular BVCs come from?
Does information from subicular BVCs reach hippocampal PCs, and if so, does it travel via the medial
entorhinal border cells?
Do the BVCs provide a complementary input to PCs to stabilize the path-integrative input from medial
entorhinal grid cells?

Long-term plasticity in hippocampal place-cell representation of environmental geometry
Lever, Willis, Caccuci, Burgess, O’Keefe 2002
That environmental experience leads CA1 place cells to discriminate between environments on the basis of
geometrical features, in the absence of differential reinforcement, and to maintain this discrimination for a
long time, is consistent with the cognitive map theory of hippocampal function
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We have captured hippocampal firing
patterns throughout the animals’ entire experience in two geometrically distinct enclosures, from initial
entry, through to discrimination, transfer and delay stages
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Paper notes
Computer simulation of hippocampal place cells
By Patricia Sharp 1991
The present model combines the idea that place fields result from “local views” with the idea that the
hippocampus contains Hebb-like synaptic mechanisms that store patterns of sensory input, in order to
model the activity of hippocampal place cells
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The input layer are neocortical cells that have sensory reponses to the elements

13th November 2013
of the patterns of stimuli available to the animal’s sensory receptors
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The model
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First layer has sensory cells, activated by particular environmental stimuli
Subsequent layers divided into winner-take-all clusters
Cells learn only on occasions on which they fire
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