Model Of Primary Visual Cortex Research Articles

Spatially periodic modulation of cortical patterns by long-range horizontal connections

We analyze spontaneous pattern formation in a continuum model of primary visual cortex that incorporates spatially periodic inhomogeneities in the distribution of long-range horizontal connections. These inhomogeneities reflect the underlying crystalline-like structure of cortex, as exemplified by the distribution of cytochrome oxidase blobs. We first solve the linear eigenvalue problem for a primary Turing instability, and show how the resulting activity pattern can lock to the underlying cortical lattice when certain resonance conditions are satisfied. This result is then extended to the weakly nonlinear regime by performing a multiple scale perturbation expansion of the nonlocal integro-differential equation that determines cortical activity. The resulting amplitude equation describes the effects of long-wavelength modulations of a primary roll pattern in the presence of periodic inhomogeneities, and is shown to exhibit a commensurate–incommensurate transition analogous to that found in convective fluid systems with external periodic forcing. Extensions of our theory to models of cortical development are briefly discussed.

Combined Hebbian development of geniculocortical and lateral connectivity in a model of primary visual cortex.

We present a network model of visual map development in layer 4 of primary visual cortex. Our model comprises excitatory and inhibitory spiking neurons. The input to the network consists of correlated spike trains to mimick the activity of neurons in the lateral geniculate nucleus (LGN). An activity-driven Hebbian learning mechanism governs the development of both the network's lateral connectivity and feedforward projections from LGN to cortex. Plasticity of inhibitory synapses has been included into the model so as to control overall cortical activity. Even without feedforward input, Hebbian modification of the excitatory lateral connections can lead to the development of an intracortical orientation map. We have found that such an intracortical map can guide the development of feedforward connections from LGN to cortical simple cells so that the structure of the final feedforward orientation map is predetermined by the intracortical map. In a scenario in which left- and right-eye geniculocortical inputs develop sequentially one after the other, the resulting maps are therefore very similar, provided the intracortical connectivity remains unaltered. This may explain the outcome of so-called reverse lid-suture experiments, where animals are reared so that both eyes never receive input at the same time, but the orientation maps measured separately for the two eyes are nevertheless nearly identical.

A local circuit approach to understanding integration of long-range inputs in primary visual cortex.

Integration of inputs by cortical neurons provides the basis for the complex information processing performed in the cerebral cortex. Here, we have examined how primary visual cortical neurons integrate classical and nonclassical receptive field inputs. The effect of nonclassical receptive field stimuli and, correspondingly, of long-range intracortical inputs is known to be context-dependent: the same long-range stimulus can either facilitate or suppress responses, depending on the level of local activation. By constructing a large-scale model of primary visual cortex, we demonstrate that this effect can be understood in terms of the local cortical circuitry. Each receptive field position contributes both excitatory and inhibitory inputs; however, the inhibitory inputs have greater influence when overall receptive field drive is greater. This mechanism also explains contrast-dependent modulations within the classical receptive field, which similarly switch between excitatory and inhibitory. In order to simplify analysis and to explain the fundamental mechanisms of the model, self-contained modules that capture nonlinear local circuit interactions are constructed. This work supports the notion that receptive field integration is the result of local processing within small groups of neurons rather than in single neurons.