Computational modeling of place cells in the rat hippocampus
Download
1 / 32

Computational Modeling of Place Cells in the Rat Hippocampus - PowerPoint PPT Presentation


  • 150 Views
  • Uploaded on

Computational Modeling of Place Cells in the Rat Hippocampus . Nov. 15, 2001 Charles C. Kemp. Talk Overview. Give an introduction to place fields and the hippocampus Review two models both with navigation using place fields one with a model for generating place fields

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Computational Modeling of Place Cells in the Rat Hippocampus' - mika


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Talk overview l.jpg
Talk Overview

  • Give an introduction to place fields and the hippocampus

  • Review two models

    • both with navigation using place fields

    • one with a model for generating place fields

  • Critique these two models

  • Look toward the future of the field



Hippocampus place cells l.jpg
Hippocampus & Place Cells

  • Big Field

    • Journals, books

    • Research groups

  • Long History

    • 1800’s for hippocampus

    • 1971 for place fields

  • Few Answers



Importance of the hippocampus l.jpg
Importance of the Hippocampus

  • Episodic memory

    • HM

  • Spatial tasks

    • allocentric system

  • Navigation

    • taxi drivers

    • VR


What about rats l.jpg
What about Rats?

  • Hippocampal anatomy is very similar in rats and humans.

  • Rats show similar deficits from hippocampal lesions.

  • Monkeys have view cells.


Zooming into the rat brain l.jpg
Zooming into the Rat Brain

  • 2g

  • 150 million neurons

  • 500,000 pyramidal neurons in hippocampus





Review of two explicit computational models l.jpg
Review of Two Explicit Computational Models

Arleo & Gerstner (2000)

Foster, Morris & Dayan (2000)


Navigation with place fields l.jpg
Navigation with Place Fields

  • Both models of navigation

    • Create functions that associate actions with locations in the environment.

    • Train these functions while the simulated rat navigates in an environment looking for a reward.

  • Foster, Morris, and Dayan’s also learns coordinates for each location



Functions for behavior l.jpg
Functions For Behavior

  • Location -> Action

    • A(p)={a1(p), a2(p), ... an(p)}

    • P[A, p](i) = probability of action i

  • Location -> Value

    • c(p) ~

    • v(p) = Max[A(p)]

  • Location -> Metric Coordinate

    • {x(p},y(p)}


Learning the functions l.jpg
Learning the functions

  • Recursion Trick

  • Gradient Descent and Hebbian Learning


Arleo s model of place fields l.jpg

reset

Vision

place cells

Path Integration

place cells

Linear combination

CA1 Hippocampal

place cells

Arleo’s model of Place Fields


Feature vectors for snapshots l.jpg

Feature Vector Maker

Ii

fi

Filter Bank

Magnitude

Subsample

Feature Vectors for Snapshots

  • Collects four images at each position it visits

  • Converts all images to feature vectors prior to use.


Snapshot cells are combined to make sec cells l.jpg
Snapshot cells are combined to make sEC cells.

  • A radial basis function is put around each of the four feature vectors from a new location

  • the outputs from these 4 radial basis function are combined as a weighted average

  • the weight vector is adapted by a hebbian update rule


Weighted average of pi cells and sec cells makes place cells l.jpg
Weighted average of PI cells and sEC cells makes Place cells

  • PI cells:

    • use integration of wheel turns

    • represent as a set of radial basis functions

  • strongly responding PI cells and sEC cells are combined using sEC cell method



Static navigation can t learn from a single example l.jpg
Static Navigation can’t learn from a single example

  • Rat’s can

    • Water maze

      • 2 meter diameter

      • opaque water

      • hidden platform, 1.1 cm diameter

    • After 3 days of 4 trials a day

      • minimal latency after a single example


Single example learning without metric navigation l.jpg
Single example learning without metric navigation?

  • Topological

  • Cue, Action sequence

    • note distal cue from example

    • swim to center

    • look for cue

    • swim towards it until proper distance from the wall

Real paths

(steele&Morris 1999)

Simulated paths

(Foster et al 2000)


Self motion information l.jpg
Self-motion information

  • Save et al, Hippocampus 2000

    • olfactory information is more important

    • self-odor has been neglected

    • place cells go unstable

      • 39% (dark/cleaning)

      • 80% (light/cleaning)

    • few remain stable

      • 10% (dark/cleaning)

      • 0% (light/cleaning)

  • Both models assume

    • accurate self-motion information

    • stable place fields

  • Arleo & Gerstner assign too much importance to PI cells


More problems with rbf place fields l.jpg
More problems with RBF place fields

  • Wood et al, Nature 1999

    • smell cup

      • if matches last cup smell ignore

      • if it doesn’t match last cup smell dig for food


A better model for place cells l.jpg
A better model for place cells?

  • Hartley et al, Hippocampus 2000



Getting there faster l.jpg
Getting there faster.

  • quantify the input

    • robot

    • rat VR

    • model the environment

  • record the output

    • at least head position, body position, eye position

    • camera array to record the rat

  • observe the computations

    • improved multi-electrode arrays

      • chronic implantation

      • multi-region

      • larger number (1/2 million cells)

  • facilitate collaboration


Robots l.jpg
Robots

  • Are they a good model

    • better methods of quantifying the input exist

    • poor models of rat senses and actions

    • convenient, cool looking

  • Can they help this research?

    • indirectly, yes

      • elucidate issues

      • explore complex tasks

      • for example, Sebastian Thrun and Hans Moravec


Navigating the microstructure l.jpg
Navigating the Microstructure

  • compartmental models

  • statistical characterizations

  • 3D reconstruction and data sets

Ascoli et al. (1999)

Fiala & Harris (2001)


Conclusion l.jpg
Conclusion

  • Introduced place fields and the hippocampus

  • Reviewed two models

    • both with navigation using place fields

    • one with a model for generating place fields

  • Critiqued these two models

  • Tried to look toward the future of the field


Other points of interest l.jpg
Other Points of Interest

  • Abstract Neighborhoods

  • Generalized Snapshots

  • Searching through states

  • Beyond simple navigation


ad