Download Neural basis of Perceptual Learning

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
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:
ri  fi    ni
►
(Seung and Sompolinsky, 1993)
The Fisher Information for this neuron is:
I   
2

f  
Activity
ri  fi    ni
► For a population of neurons with independent noise:
2
N
N
I50 100 150I i    
1
Orientation i(deg)
i 1
2

fi  
 i2
Problems
► We
know that neurons are not independent.
2

fi1 
 Q 2  f'  tr  Q 1   Q   Q 1   Q   
I    f
N
T
i 1
► Mechanisms
i
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
rmax
Activity spikes/s
0.4
0.35
20
0
-45
0
45
0.25
Orientation (deg)
0.2
I
0.15
0.1
Activity spikes/s
Information (deg-2)
0.3
40
0.05
0
19
20
21
22
23
24
Tuning curve width (Deg)
25
26
40
20
0
-45
0
45
Orientation (deg)
Results - Sharpening
Sharpening by adjusting feed-forward thalamocortical
connections
20
10
0
Activity spikes/s
-45
0
45
Activity spikes/s
Orientation (deg)
Log (variance)
Information
(deg-2)
Orientation (deg)
Activity spikes/s
Activity spikes/s
►
Orientation (deg)
20
10
0
-45
0
45
Orientation (deg)
Log (mean)
Tuning
curve width (Deg)
Results - Sharpening
20
10
0
-45
0
45
Activity spikes/s
Orientation (deg)
Log (variance)
Information
(deg-2)
Orientation (deg)
Activity spikes/s
Activity spikes/s
Sharpening by adjusting recurrent lateral connections
Activity spikes/s
►
Orientation (deg)
20
10
0
-45
0
45
Orientation (deg)
(mean)
TuningLog
curve
width (Deg)
Activity spikes/s
Comparing sharpening schemes
3
2.8
10
0
-45
2.4
0
45
Orientation (deg)
2.2
2
1.8
1.6
1.4
1.2
1
20
22
24
26
28
Tuning curve width (Deg)
30
32
Activity spikes/s
Information (deg-2)
2.6
20
20
10
0
-45
0
45
Orientation (deg)
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