A goal-directed spatial navigation model using forward trajectory planning based on grid cells

A goal-directed spatial navigation
model using forward trajectory
planning based on grid cells
Erdem and Hasselmo
2012
European Journal of
Neuroscience

 People can navigate to specific place using compass and the
map under the grid
Navigation Task

Navigation Cells
 Rat has the navigation cells that represent the place, grid,
border and direction in the environment

Morris Water Maze Test
 The Morris water maze shows a spatial learning task of a rat [1]
 Rats show impairments in finding the spatial location of a hidden platform in the Morris water-
maze after lesions of the hippocampus [2], postsubiculum [3] or entorhinal cortex [4]
[1] Kipnis et al., 2012, Nature Reviews [2] Steele and Morris, 1999 [3] Taube at al., 1992 [4] Steffenach et al., 2005
Hippocampal lesion Postsubicular lesion Entorhinal cortex lesion

Goal-directed choice
 Prefrontal cortex to represent task space (i.e. goals and values of cues and
action options)
[5] Verschure et al., 2014
[5]

Spatial Information Processing Cells’ Network
 Medial entorhinal cortex (mEC) layer II grid cells receive the
input from postsubiculum and mEC layer V
PoS
[6] Witter et al., 2013, Philos Trans R Soc Lond B Bil Sci. [7] Fransen et al., 2006, Neuron
[6]
Hippocampal networkmEC neuronal circuitsEC layer V pyramidal cell
2 s
[7]

Persistent Spiking Cell
 Persistent spiking cells receive depolarizing input from head
direction cells with preferred direction
 Persistent spiking cells generate grid pattern of grid cell
[8] Hasselmo, 2008
[8]

Method
 Simulation environment
▫ All simulations are coded ad performed using MATLAB
▫ Persistent spiking cells’ frequency: 7 Hz
▫ Three head direction cells with preferred directions 0, 120 and 240°

Method
 Head direction cell  persistent spiking cell  grid cell  place cell
<Head direction cell’s tuning kernel>
<Head direction cell signal>
Error term (noise)
Main orientation
<Phase interference model>
Output of persistent spiking cells
Head direction cell activity
Spiking output of grid cell

Method
 Different scaling factors
and identical initial phase
cause grid cell firing
 The firing field is
formatted by summation
of amplitude

Method
 Head direction cell  persistent spiking cell  grid cell  place cell
<Place cell signal>
Recency cell Topology cell
<Recency cell signal>
Wu
<Topology cell layer weight>
<Reward diffusion>
Diffusion decay

Method
 When the virtual rat is introduced to a never-before experienced environment, a
new place cell is recruited receiving its synaptic inputs from a new population of
grid cells
 The virtual rat recruits new place cells either deterministically or in a pseudo-
random fashion

Method
 Forward linear look-ahead trajectory probes
▫ This model does not require storage of fixed
route vector between place cells and goal
location
▫ Instead, the virtual rat can pick any place cells
as a goal location, and decide on its next
movement direction based on the topology of
the place cell map

Aims
 How grid cells emerge using head direction cell inputs, and
place cells emerge using grid cell inputs.
 How the place cell map can abstractly represent an
environment’s topology using PFC cortical columns as its main
components.
 How a temporal recency effect can be used to connect PFC
cortical columns by Hebbian updates.

Result
 Morris water-maze goal-directed navigation in ideal condition
(i.e. no noise, no obstacles)

Result
 Morris water-maze goal-directed test with zero mean Gaussian noise

Result
 Quantitative analysis of noisy head direction cell and grid cell
signal effects on the navigation performance
Kolmogorov-Smirnov test (K-S test) is
nonparametric test that based on parameterized
families

Result
 The Tolman shortcut experiments

Result
 Hairpin shortcut experiments

Conclusion
 Grid firing pattern of grid cell could be formed by persistent
spiking neurons
 Phase resetting will be one of factor that process the spatial
information
 Navigation task is influenced by noise fraction

Discussion
 Another new model concerns the interaction of trajectory
planning with barriers in the environment
 Unrealistic method
▫ Recruited pseudo-random place cell

A goal-directed spatial navigation model using forward trajectory planning based on grid cells

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