| Hi-index | 0.00 |
This paper presents the finding of the research we conducted to evaluate the variability of signal release probability at Hebb's presynaptic neuron under different firing frequencies in a dynamic stochastic neural network. A modeled neuron consisted of thousands of artificial units, called `transmitters' or `receptors' which formed dynamic stochastic synaptic connections between neurons. These artificial units were two-state stochastic computational units that updated their states according to the signal arriving time and their local excitation. An experiment was conducted with three stages by updating the firing frequency of Hebbian neuron at each stage. According to our results, synaptic redistribution has improved the signal transmission for the first few signals in the signal train by continuously increasing and decreasing the number of postsynaptic `active-receptors' and presynaptic `active-transmitters' within a short time period. In long-run, at low-firing frequency, it has increased the steady state efficacy of the synaptic connection between the Hebbian presynaptic and the postsynaptic neuron in terms of the signal release probability of `active-transmitters' in the presynaptic neuron as observed in biology. This `low-firing' frequency of the presynaptic neuron has been identified by the network by comparing it with the ongoing frequency oscillation of the network.