Modeling of LTP-Related Phenomena Using an Artificial Firing Cell

We present a computational model of neuron, called firing cell (FC), that is a compromise between biological plausibility and computational efficiency aimed to simulate spiketrain processing in a living neuronal tissue. FC covers such phenomena as attenuation of receptors for external stimuli, delay and decay of postsynaptic potentials, modification of internal weights due to propagation of postsynaptic potentials through the dendrite, modification of properties of the analog memory for each input due to a pattern of long-time synaptic potentiation (LTP), output-spike generation when the sum of all inputs exceeds a threshold, and refraction. We showed that, depending on the phase of input signals, FC's output frequency demonstrate various types of behavior from regular to chaotic.