Research Article

Robust Reservoir Generation by Correlation-Based Learning

Figure 1

Schematic of our network model. The model consists of two networks: the reservoir and readouts. The reservoir is a recurrent network composed of excitatory and inhibitory neurons. Excitatory neurons receive external inputs representing conditioned stimuli (CS, (4), (5)). Four types of connections, excitatory to excitatory, excitatory to inhibitory, inhibitory to excitatory, and inhibitory to inhibitory, are assumed. Connection weights between excitatory neurons are modifiable by CBL (see (3)), whereas the weights of other connections are fixed as in conventional RC models. On the other hand, a readout neuron receives inputs from excitatory reservoir neurons ( , ) and the input representing unconditioned stimuli (US, (12), (13)). The connection weights between excitatory reservoir neurons and the readout neuron are modifiable by supervised learning rule (see (11)).