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Neural basis of Perceptual Learning

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Title: Neural basis of Perceptual Learning


1
Neural basis of Perceptual Learning
  • Vikranth B. Rao
  • University of Rochester
  • Rochester, NY

2
Research Group
Alexandre Pouget Jeff Beck Wei-ji Ma
3
Perceptual Learning in Orientation Discrimination
  • Orientation discrimination is subject to
    learning.
  • Perceptual Learning (PL) is one such form of
    learning.
  • Repeated exposure leads to decrease in
    discrimination thresholds (Gilbert 1994).

4
Central Question
  • Perceptual learning is a robust phenomenon in a
    wide variety of perceptual tasks.
  • When applied to orientation discrimination, how
    do we relate the learned improvement in
    behavioral performance, to changes in population
    activity due to learning at the network level?
  • This is the question we aim to answer.

5
Approach
  • We assume behavioral improvements are due to
    information increases in sensory representations.
  • (Paradiso 1998, Geisler 1989, Pouget and Thorpe
    1991, Seung and Sompolisky 1993, Lee et al. 1999,
    Schoups et al. 2001 Adini et al. 2002, Teich and
    Qian 2003).
  • By information, we mean Fisher Information
  • It clearly relates to discrimination thresholds
  • It can be directly computed from first and
    second-order statistics (mean and variance).
  • It can be computed for a population of neurons.

6
Fisher Information
  • By information, we mean the information about the
    stimulus feature (orientation ?), in a pop. of
    neurons.
  • Response of one neuron in the pop. can be written
    as
  • The Fisher Information for this neuron is

(Seung and Sompolinsky, 1993)
  • For a population of neurons with independent
    noise

7
Problems
  • We know that neurons are not independent.
  • Mechanisms which
  • Change tuning curves may also change the
    correlation structure
  • Change correlation structure may also change
    tuning curves
  • Change cross-correlations but not single-neuron
    statistics can increase information drastically
    (Series et. al. 2004)

8
Investigative Approach
  • We want to use networks of biologically plausible
    spiking neurons with realistic correlated noise
    to study the neural basis of PL.
  • Therefore, we consider
  • Two spiking neuron network models
  • Linear Non-Linear Poisson (LNP) neurons
    analytically tractable but less biologically
    realistic
  • Conductance-based integrate and fire (CBIF)
    neurons biologically very realistic but
    analytically intractable
  • Biologically plausible connectivity
  • Biologically plausible single-neuron statistics
    (near unit Fano factor)
  • Enough simulations to produce a reasonable lower
    bound on Fisher information

9
Exploring candidate mechanism(s) for PL
  • We want to investigate changes in Fisher
    Information as a result of the following
    manipulations to network dynamics
  • Sharpening
  • Via feed-forward connectivity
  • Via recurrent connectivity
  • Amplification
  • Via feed-forward connections
  • Via recurrent connections
  • Increasing the number of neurons
  • We use the analytically tractable LNP network to
    generate predictions and the CBIF network to
    confirm these predictions

10
Sharpening LNP Simulations
40
Activity spikes/s
20
0
-45
0
45
Orientation (deg)
40
Activity spikes/s
20
0
-45
0
45
Orientation (deg)
11
Results - Sharpening
  • Sharpening by adjusting feed-forward
    thalamocortical connections

12
Results - Sharpening
  • Sharpening by adjusting recurrent lateral
    connections

13
Comparing sharpening schemes
14
Future Work
  • Exploring changes in Fisher information as a
    result of
  • Amplification
  • Increasing the number of neurons
  • Exploring other ways of increasing information
  • Exploring Early versus Late theories of Visual
    Learning

15
Conclusion
  • We are interested in investigating the changes at
    the population level, that sub-serve the
    improvement in behavioral performance seen in PL.
  • We follow the prevalent view that improvement in
    behavioral performance is due to information
    increase in the population code.
  • Relaxing the independence assumption no longer
    allows us to relate changes at the single-cell
    level to changes at the population level, in
    terms of information throughput.
  • An exploration of the mechanism of sharpening at
    the population level, using networks of spiking
    neurons with realistic correlated noise, yields
    the following results
  • Sharpening through an increase in feed-forward
    connections leads to an increase in information
    throughput
  • Sharpening by changing the recurrent lateral
    connections leads to a decrease in information
    throughput
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