Neural basis of Perceptual Learning Vikranth B Rao
Neural basis of Perceptual Learning Vikranth B. Rao University of Rochester, NY
Research Group Alexandre Pouget Jeff Beck Wei-ji Ma
Perceptual Learning in Orientation Discrimination ► Orientation learning. ► Perceptual learning. discrimination is subject to Learning (PL) is one such form of § 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: (Seung and Sompolinsky, 1993) The Fisher Information for this neuron is: ► For a population of neurons with independent noise: Activity ► 50 100 150 Orientation (deg)
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)
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
Activity spikes/s Sharpening – LNP Simulations 40 20 0 -45 0 45 Activity spikes/s Orientation (deg) 40 20 0 -45 0 45 Orientation (deg)
Results - Sharpening ► Sharpening Orientation (deg) Log (variance) Activity spikes/s connections by adjusting feed-forward thalamocortical Orientation (deg) Log (mean)
Results - Sharpening by adjusting recurrent lateral connections Orientation (deg) Log (variance) Activity spikes/s ► Sharpening Orientation (deg) Log (mean)
Comparing sharpening schemes
Future Work ► Exploring result of: changes in Fisher information as a § Amplification § Increasing the number of neurons ► Exploring other ways of increasing information ► Exploring Early 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
- Slides: 15