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 θ), in a pop. of neurons. ► Response of one neuron in the pop. can be written as: ► The Fisher Information for this neuron is: ► For a population of neurons with independent noise: Orientation (deg) Activity 50 100 150 (Seung and Sompolinsky, 1993)

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 Orientation (deg) -45045 Activity spikes/s Orientation (deg) -45045 0 20 40 Activity spikes/s 0 20 40

11. Activity spikes/s Orientation (deg) Results - Sharpening ► Sharpening by adjusting feed-forward thalamocortical connections Activity spikes/s Orientation (deg) Log (variance) Log (mean)

12. Results - Sharpening ► Sharpening by adjusting recurrent lateral connections Orientation (deg) Activity spikes/s Orientation (deg) Activity spikes/s Log (variance) Log (mean)

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