261.21 Neocortical Layer 4 is a Pluripotent Function LinearizerOleg V. Favorov and Olcay Kursun

Department of Biomedical Engineering, University of North Carolina School of Medicine, Chapel Hill, NC 27599

INTRODUCTION

In Machine Learning/Pattern Recognition, a highly

effective kernel-based strategy for dealing with nonlinear

problems is to transform the input space into a new

“feature” space, in which the problem becomes linear and

more readily solvable with efficient linear techniques. We

propose that a similar “problem-linearization” strategy

might be used by neocortex. A mathematically abstract

elaboration of such a problem-linearization strategy

produces a computational system that closely resembles

the real cortical layer 4 in its structural and functional

properties. We demonstrate this close match between

theoretical and real cortical properties on layer 4 of the cat

primary visual cortex.

Problem-linearization

strategy of

transforming the input

space into a “feature”

space

Each cortical area computes

certain nonlinear functions F

over its afferent inputs.

Each cortical area might benefit

from a problem-linearization

strategy in learning its functions

HYPOTHESIS: Layer 4 implements a function-

linearization strategy for the upper layers 2/3

Layer 4 transform must be “blind,”

without feedback from layers 2/3.

Layer 4 transform must be

“pluripotent” – optimized to

make linear as broad a repertoire

of potential functions as possible.

IMPLEMENTATION OF FUNCTION-

LINEARIZATION STRATEGY UNDER NEURAL

CONSTRAINTS

Anti-Hebbian lateral connections are needed to drive

neighboring Layer 4 neurons to diversify their preferred

directions in the stimulus space by modifying their Hebbian

afferent connections.

BIOLOGICAL INTERPRETATION OF THE MODEL

Experimental evidence:

Egger V, Feldmeyer D, Sakmann B (1999)

Coincidence detection and changes of

synaptic efficacy in spiny stellate neurons in

rat barrel cortex. Nature Neuroscience 2:

1098-1105.

TEST OF FUNCTION-LINEARIZATION HYPOTHESIS ON VISUAL INPUTS (comparison with Layer 4 of cat V1)

Natural images were used to develop plastic connections in

the model.

THALAMIC (LGN) MODEL

LAYER 4 MODEL

PLURIPOTENCY TEST

Pluripotency of L4 transform – capacity to represent linearly

any arbitrary nonlinear function of the afferent input patterns.

LGN connections

of all L4 cells

EMERGENT LGN CONNECTIONS AND RECEPTIVE FIELDS

End-stopped RFs:

15% (25%)

Number of RF

subfields:

1-4 (1-5)

Av. number of RF

subfields:

2.7 (2.45-2.65)

RF aspect ratio:

3.8 (4.3-4.5)

ORIENTATION TUNING (OT)

- simple-cell type of response to gratings

- HWHH of OT: 15o

(16o)

- OT is contrast-invariant

- average optimal spatial frequency: 1o

(0.86o)

- OT is tighter for finer gratings

Equation

offers a close mathematical representation of the structure and function

of local layer 4 domains

CONCLUSION

Hebbian Feed- Recurrent Anti-Hebbian

thalamic forward inhibition recurrent

input inhibition excitation

OT of output is close to OT of LGN input -