Your Federal Quarterly Tax Payments are due April 15th Get Help Now >>

Neural basis of Perceptual Learning - PowerPoint by rt3463df

VIEWS: 8 PAGES: 15

									Neural basis of Perceptual
        Learning
         Vikranth B. Rao
      University of Rochester
          Rochester, NY
 Research Group


Alexandre Pouget

Jeff Beck

Wei-ji Ma
 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).
               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.
                         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.
                   Fisher Information
►   By information, we mean the information about the
    stimulus feature (orientation θ), in a pop. of neurons.
►   Response of one neuron in the pop. can be written as:
                          ri  fi    ni                (Seung and Sompolinsky, 1993)

►   The Fisher Information for this neuron is:
                                       f   2
                          I   
                                     ri  fi    ni
                                            2
              Activity




► For a population of neurons with independent noise:
                                     N             N
                                                          fi   2
                          I     I i    
                          50   100  150
                                     i 1
                         Orientation (deg)         i 1      i2
                          Problems
► We   know that neurons are not independent.
                         1 2
          I    f  Q 2  f'  tr  Q 1   Q   Q 1   Q   
                    N  fi
                    T

                   i 1   i

► 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)
            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
    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
        Sharpening – LNP Simulations

                       0.4                                                                            rmax




                                                                        Activity spikes/s
                      0.35                                                                  40

                                                                                            20
                       0.3
Information (deg-2)




                                                                                            0
                                                                                                 -45     0      45
                      0.25                                                                         Orientation (deg)



                                                                                                              I
                       0.2


                      0.15


                       0.1




                                                                        Activity spikes/s
                                                                                            40
                      0.05
                                                                                            20

                        0                                                                   0
                         19   20     21     22    23     24   25   26                            -45     0      45
                                                                                                   Orientation (deg)
                                   Tuning curve width (Deg)
                                        Results - Sharpening
                    ►   Sharpening by adjusting feed-forward thalamocortical
                        connections



                                                                      Activity spikes/s
                                                                                          20

                                                                                          10

                                                                                           0
                                                                                                -45     0     45
Activity spikes/s




                                                                                                                      Activity spikes/s
                                                                                                  Orientation (deg)
                                                Information (deg-2)
                                                     Log (variance)




                                                                      Activity spikes/s




                            Orientation (deg)                                                                                             Orientation (deg)
                                                                                          20

                                                                                          10

                                                                                           0
                                                                                                -45     0     45
                                                                                                  Orientation (deg)
                                                                                              Log curve
                                                                                          Tuning (mean) width (Deg)
                                        Results - Sharpening
                    ►   Sharpening by adjusting recurrent lateral connections



                                                                     Activity spikes/s
                                                                                          20

                                                                                          10

                                                                                           0




                                                                                                                     Activity spikes/s
Activity spikes/s




                                                                                               -45     0     45
                                                                                                 Orientation (deg)
                                               Information (deg-2)
                                                    Log (variance)




                                                                                                                                         Orientation (deg)
                           Orientation (deg)
                                                                     Activity spikes/s




                                                                                          20

                                                                                          10

                                                                                           0
                                                                                               -45     0     45
                                                                                                 Orientation (deg)
                                                                                         Tuning Log (mean)
                                                                                                 curve width (Deg)
        Comparing sharpening schemes




                                                                       Activity spikes/s
                       3                                                                   20

                      2.8                                                                  10

                      2.6                                                                  0
                                                                                                -45    0     45
Information (deg-2)




                      2.4                                                                        Orientation (deg)
                      2.2

                       2

                      1.8

                      1.6

                      1.4




                                                                       Activity spikes/s
                      1.2                                                                  20

                       1                                                                   10
                        20   22       24      26     28      30   32
                                                                                           0
                                  Tuning curve width (Deg)                                      -45    0     45
                                                                                                 Orientation (deg)
                Future Work
► Exploring   changes in Fisher information as a
 result of:
   Amplification
   Increasing the number of neurons

► Exploringother ways of increasing
 information
► ExploringEarly versus Late theories of
 Visual Learning
                           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

								
To top