Observed Stent´s anti-Hebbian postulate on dynamic stochastic computational synapses

Unconstrained growth of synaptic connectivity and the lack of references to synaptic depression in Hebb´s postulate has diminished its value as a learning algorithm. While spike timing dependent plasticity and other synaptic scaling mechanisms have been studying the possibility of regulating synaptic activity on neuronal level, we studied the possibility of regulating the synaptic activity of Hebb´s neurons on dynamic stochastic computational synapses. The study was conducted on fully connected network with four artificial neurons where each neuron consisted of thousands of artificial stochastic synapses that are modeled with transmitters and receptors. The synapses updated their stochastic states dynamically according to the spike arrival time to that synapses. The activity of these synapses was regulated by a new stability promoting mechanism. Results support the following findings: (i) the synchronous activity between presynaptic (cell A) and postsynaptic (cell B) neuron increases the activity of A. (ii) Asynchronous activation of these two neurons decreases A´s activity if one of the following conditions are satisfied (a). if activity of the other presynaptic neurons of the postsynaptic neuron B is asynchronous with the A´s activity or (b) if B is in a depressed state when activity of presynaptic neuron A is increased. (iii) the introduced stability promoting mechanism exhibited similar to the Homeostatic synaptic plasticity process and encouraged the emergence of Hebb´s postulate and its anti-Hebbian mechanisms. Further, we demonstrated the metabolic changes that could occur inside Hebb´s neurons when such an activity takes place on a dynamic stochastic neural network.