Continuous Neural Field Modeling of Primary Visual Cortex

Important unresolved questions in visual neuroscience concern the neural circuitry by which the receptive fields of cortical cells gain their space-time structure subservient to different functional properties. How do simple cell receptive fields gain their Gabor-like profiles? How do direction selectivity and spatiotemporally oriented receptive fields originate? How do cortical complex cells gain their nonlinear response properties? Following a ``reverse-engineering'' approach to seek possible architectural answers to these questions, we proposed a neural field model of simple cell functionality. The model, based on the superposition of geniculate (i.e., feed-forward) and intracortical (i.e., feedback) contributions, suggests to relate the origin of highly-structured Gabor-like receptive fields to the propagation properties of cortical activation triggered by the specific spatial organization of the recurrent lateral inhibition scheme adopted. Direction selectivity and space-time oriented receptive fields emerge from the same architectural model when recurrent inhibitory interactions occur through spatially asymmetric schemes. It is worthy to note that when inhibition is mediated by gating circuits controlled by additional feed-forward signals, the resulting visual operators present a non-linear behavior. The analysis of the resulting operators through bi-impulsive stimulation evidenced 2nd-order Volterra kernels that closely resemble non-linear subunits of cortical complex cells. In conceiving these models and carrying on their analysis, major emphasis is on the extrapolation of computational principles than on the details of neurobiological facts. For that reason, this study should be considered not as a realistic biological model of primary visual cortex, but as an attempt to advance some average structural/functional principles of intracortical connectivity and to relate them to the underlying single-cell properties.

Back to index

Representation properties of cortical orientation maps

The efficiency of cortical representation of orientation can be evaluated in terms of the completeness of the representation and of its redundancy necessary to preserve information from intrinsic noise. The analysis of the efficiency of the resulting arrangements of orientation selective cells can be pursued by investigating how information about any small locus in visual space affects the activity of nearby cells on the basis of the overlap of their receptive fields. The way in which neighboring receptive fields overlap depends on their shape and size, and on their relative disposition as dictated by the orientation map. In order to have the same representation and computation capabilities across the visual space, it is necessary to have an uniform allocation of cells of different orientation preference for each point (cf. the uniform coverage criterium of Nick Swindale). According to a simple linear model of cortical cells, we conceived an information-theoretic framework to evaluate coverage uniformity of several experimental orientation maps by studying the amount and the isotropy of information transmitted horizontally on the maps. At each location of the cortical plane the directional mutual information is evaluated, as a measure of coverage uniformity. The resulting coverage uniformity map is defined by the degree of isotropy of the mutual information. The efficiency of an orientation map has been related to a corresponding coverage uniformity map characterized by uniformly low values. This criterium applied to pinwheel orientation maps (experimental data from the literature) reveals optimal coverage uniformity, compared to other orientation arrangements, except at vortices or fractures. These violations of uniformity are extremely localized and can be selectively reduced since singular point are characterized by low orientation tuning strength. In perspective, the same criterium can be used (1) to verify the optimal relationship between the average size of the cortical point image and the average hypercolumn width; (2) to define a self-organizing rule to obtain realistically looking artificial orientation maps.

Back to index
last updated: 03, November 99 by