Model of neural circuit comparing static and adaptive synapses

Replacing static synapses with the adaptive ones can affect the behaviour of neuronal network. Several network setups containing synapses modelled by alpha-functions, called here static synapses, are compared with corresponding setups containing more complex, dynamic synapses. The dynamic synapses have four state variables and the time constants are of different orders of magnitude. Response of the network to modelled stimulations was studied together with effects of neuronal interconnectivity, the axonal delays and the proportion of excitatory and inhibitory neurons on the network output. Dependency of synaptic strength on synaptic activity was also studied. We found that dynamic synapses enable network to exhibit broader spectrum of responses to given input and they make the network more sensitive to changes of network parameters. As a step towards memory modelling, retention of input sequences in the network with static and dynamic synapses was studied. The network with dynamic synapses was found to be more flexible in reducing the interference between adjacent inputs in comparison to the network containing static synapses.