Speaker: Prof.Fang Han（Donghua University）

Title One: Global firing rate contrast enhancement in E/I neuronal networks by recurrent synchronized inhibition

Time: Sat, Jul.20 2019, AM:8:30-9:30

Title Two: A novel time-event-driven algorithm for simulating spiking neural networks based on circular array

Time: Sun, Jul.21 2019, AM:8:30-9:30

Location: Room 4318, Building No.4, Wushan Campus

Abstract One:   

Inhibitory synchronization is commonly observed and may play some important functional roles in excitatory/inhibitory (E/I) neuronal networks. The firing rate contrast enhancement is a general feature of information processing in sensory pathways, and a new mechanism of contrast enhancement by inhibitory synchronization in E/I neuronal networks is investigated. Inspired by the firing rate contrast enhancement phenomenon by the lateral feed-forward inhibition, we reveal that the firing rate contrast enhancement could also occur by recurrent inhibition in E/I networks. It is further found that the synchronized inhibitory neurons act as a global inhibition which can enhance the firing rate contrast of excitatory neurons globally in synchronized E/I networks, even in partially synchronous states. Therefore, the firing rate contrast enhancement might be an important function of inhibitory synchronization and might facilitate information transmission in neural systems. 

Abstract Two:   

The computing of synaptic currents occupies a major part of computational cost when simulating a large scale spiking neural network. Based on the observation that the probability of a neuron receiving at least one spike from any synapses during a very tiny simulation time step is very small, we propose a time-driven algorithm corrected by an event-driven process (a hybrid time-event-driven algorithm) which consists of two procedures of computation. In the first procedure of the synaptic current computation, we suppose that the neuron in question receives no spike during the simulation time step, and thereby propose a time-driven method of joint decay process to reduce the computational complexity of the synaptic current. In the second procedure of the computation, we suppose that the neuron in question receives spikes during the simulation time step, and propose an event-driven local correction process to correct the total synaptic current that is calculated in the first procedure of the computation. We design a data structure of circular two-dimensional array for storing both conductance coefficients related with pre synaptic neurons and correcting conductance related with postsynaptic neurons. Furthermore, in order to realize the local correction process quickly and effectively, we propose a new event-processing method to realize the local correction process based on the data structure of circular two-dimensional array. By theoretically comparing with that of traditional time-driven algorithm, it is found that the proposed time-event-driven algorithm reduces computational cost of synaptic current substantially. The simulation results further show the efficiency of the proposed algorithm.