Abstract
Although many computational models have been proposed to explain orientation maps in primary visual cortex (V1), it is not yet known how similar clusters of color-selective neurons in macaque V1/V2 are connected and develop. In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. Each color input is decomposed into a red, green, and blue representation and transmitted to the visual cortex via a simulated optic nerve in a luminance channel and red–green and blue–yellow opponent color channels. Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. Each neuron in the V1 output layer makes synaptic connections to neighboring neurons and receives the three types of signals in the different channels from the corresponding photoreceptor position. Synaptic weights are randomized and learned using spike-timing-dependent plasticity (STDP). After training with natural images, the neurons display heightened sensitivity to specific colors. Information-theoretic analysis reveals mutual information between particular stimuli and responses, and that the information reaches a maximum with fewer neurons in the higher layers, indicating that estimations of the input colors can be done using the output of fewer cells in the later stages of cortical processing. In addition, cells with similar color receptive fields form clusters. Analysis of spiking activity reveals increased firing synchrony between neurons when particular color inputs are presented or removed (ON-cell/OFF-cell).
Highlights
It has long been known that many neurons in primary visual cortex (V1) are tuned to exhibit preference to particular simple oriented line segments, forming orientation maps that capture the preferred orientation of neurons across the cortical surfaces (Hubel and Wiesel, 1962)
The model is composed of nine layers of neurons which are organized into five hierarchical areas: photoreceptor layers (R, G, B), lateral geniculate nucleus (LGN) layers (L, C1, C2), V1 layer 4 (L4), V1 layer 2/3 (L2/3), and V1 layer 5 (L5)
We have incorporated anatomically accurate projections of signals between layers and the biologically plausible learning of synaptic weights based on Spike-Timing Dependent Plasticity (STDP) using Hodgkin–Huxley models of neuronal dynamics
Summary
It has long been known that many neurons in primary visual cortex (V1) are tuned to exhibit preference to particular simple oriented line segments, forming orientation maps that capture the preferred orientation of neurons across the cortical surfaces (Hubel and Wiesel, 1962). While several computational studies have been conducted to explain the emergence of the orientation map (Somers et al, 1995; Choe and Miikkulainen, 1998; Paik and Ringach, 2011), only a few have been done over such patchy distribution of color selectivity within an area of V1/V2. This model reproduces receptive fields of neurons inside and outside CO blobs, and the results showed that neurons outside the blobs are selective for orientation while neurons inside the blobs are selective for color. The spatial organization of a large number of color-selective areas was not studied in their model. We investigate the emergence of the spatial organization of color preference maps by developing a hierarchical neural network model that reflects anatomically faithful processing pathways and projections
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