The video contained 300 frames and each frame was presented to the model for 40 ms of simulation time. Each image was originally 256 × 256 pixels. Because our cortical model is made up of single columns, however, the input size was reduced to selleck 20 × 20 pixels (see Fig. 2B) to approximate the visual space that would drive neurons in a receptive field of a V1 cortical column. This was an assumed approximation given the 100 deg2 receptive field and 36 × 36 (64 × 64 pixel) input from the Goard and Dan experiment.
In the 256 × 256 pixel image, RF1 received input from pixels (121–140) × (121–140) and RF2 received input from pixels (141–160) × (121–140). Figure 3 shows the architecture of RF1 and RF2. It has been shown that retinal neurons remove linear correlations by ‘whitening’ images before they reach the cortex (Simoncelli & Olshausen, 2001). To simulate this, all the images were whitened and normalised before being presented to the network (Fig. 2B). Whitening was achieved by applying a Gaussian filter to the Fourier-transformed image (see http://redwood.berkeley.edu/bruno/npb261b/). This flattens the power spectrum of the image Akt inhibitor and is essentially equivalent to convolving the image with an on-center off-surround filter, as is observed in retinal
ganglion cells and the lateral geniculate nucleus (LGN). As we were not interested in modeling orientation selectivity development, we assumed that the simulated V1 columns, RF1 and RF2, were selective to vertical edges. Therefore, the images were convolved with a vertical Gabor filter after whitening.
The Gabor filter was constructed by modulating a Gabor kernel with a sinusoidal wave as shown in Eqn. (1), where σx and σy determine the spatial extent of the Gaussian in x and y and f specifies the preferred spatial wavelength GABA Receptor (Dayan & Abbott, 2001). Excitatory Poisson spike generators converted the images into spike trains in the input layer. (1) To develop our model, we used a publicly available simulator, which has been shown to simulate large-scale spiking neural networks efficiently and flexibly (Richert et al., 2011). The model contained a TRN, LGN, BF, two prefrontal cortex areas (providing top-down attention) and two, four-layered cortical microcircuits (Fig. 3). The cortical microcircuit architecture was adapted from Wagatsuma et al. (2011), which was able to account for experimental observations of attentional effects on visual neuronal responses and showed that top-down signals enhanced responses in layers 2/3 and 5. All connections that occur between layers in a microcircuit are shown in Fig. 3. Within each layer, there are excitatory–excitatory, excitatory–inhibitory, inhibitory-excitatory and inhibitory–inhibitory connections (data not shown). Connection probabilities in our cortical model were the same as used in Wagatsuma et al. (2011) and are given in Table 1. All subcortical and top-down connection probabilities were set to 0.
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