< Back to Article

High-Fidelity Coding with Correlated Neurons

Figure 8

Illustration of a proposed decoding mechanism and circuit.

A. The decoding mechanism is illustrated in the case of a two-pool model, in which denotes the spike count in Pool . The stimulus to be decoded elicits the distribution of activities represented by the yellow-red contour lines; other distributions, in blue-grey, flank it and result from different stimuli. Optimal decision boundaries (dashed lines), defined by simple inequalities, are implemented by the read-out circuit. B. The read-out circuit is a two-layer perceptron. In its first layer, excitatory and inhibitory inputs from both pools are non-linearly summed into two intermediary read-out neurons; the synaptic weights and thresholds (equivalently, baselines) are chosen such that the two intermediary neurons implement the inequalities and , respectively. Their two outputs are then summed non-linearly in turn, so that the ‘decoder neuron’ is active only if both inequalities are satisfied.

doi: https://doi.org/10.1371/journal.pcbi.1003970.g008