Nonlinear Dynamics of the Primary Visual Cortex

McLaughlin

0211655

The investigator and colleagues have been developing a

biologically constrained large-scale computational model of the

'front-end' of the cortical visual system -- the primary visual

cortex (V1). To date, their work has focused upon local

properties of individual cells within the large-scale network --

properties such as orientation selectivity and simple vs complex

cellular dynamics. In this project, they scale up to a more

global model of V1, reaching scales large enough to study some

elementary optical illusions of psychology and psychophysics.

This involves several square millimeters of lateral cortical

area, together with a multi-layered architecture -- with emphasis

given to cortical dynamics. First, a 'coarse-grained mixed

representation' is derived mathematically and tested numerically

-- a representation that combines spatially coarse-grained

(local) mean firing rates, representing local background cortical

operating points, with an idealized representation of a

sub-network of individual point neurons embedded within this

background and retaining the detailed firing patterns of

individual neurons. These two components interact with each

other -- with the coarse-grained local operating points

influencing the responses of the individual neurons, and vice

versa. Second, the global mixed representation is used to study

specific dynamical phenomena in visual cortical processing: (i)

the layer-specific 'dynamics of orientation selectivity' (as

measured by reverse time correlation methods); and later (ii)

'bistability in figure-ground assignments' (as detected in

psychology and psychophysics experiments). In both cases, the

phenomena involve extensive lateral regions of V1, its layered

structure, and (likely in the case of figure-ground assignment)

other cortical regions. And in both cases the work involves

close interaction with the experimental work of neural scientist

Robert Shapley.

Today, through new biological experiments combined with the

power of modern scientific computation, scientists and applied

mathematicians are making significant strides toward

understanding the human brain. Visual perception and the cortical

processing of visual information provide important starting

points. By focusing upon the 'front end' of the cortical visual

system, McLaughlin and his colleagues develop computational

models of the visual cortex that are strongly constrained by

biological experiments. In this project the investigators

develop computationally efficient numerical methods that permit

scale-up of the models to global representations of the primary

visual cortex -- reaching cortical scales large enough to study

some elementary optical illusions of psychology. This work

requires the direct interaction of applied mathematicians,

computational scientists, and neural scientists -- and involves

theoretical, computational, and experimental components.