Interaction of neuronal and network mechanisms on firing propagation in a feedforward network

Abstract

The mammalian brain has enormously complex neuronal diversity and a highly modular structure. The propagation of information in the modular brain network can be modeled by a feedforward network (FFN). Although studies in this area have yielded many important results, neuronal diversity has rarely been considered. In the current work, we investigate the complex interactions between the intrinsic properties of neurons and the FFN structure in the propagation of spiking activity. Here, four typical types of cortical neurons reproduced by the Izhikevich neuron model are introduced. A homogeneous FFN composed of a single type of excitatory neuron (regular spiking, mixed model, or tonic bursting) can propagate spiking activity. However, an FFN with fast spiking neurons does not propagate spiking activity. By modifying the network structure and synaptic weights, the spiking propagation of the homogeneous FFNs can vary from synchronous transmission (with a high firing rate) to asynchronous transmission (with a low firing rate). Among the homogeneous FFNs, both the firing rate and the synchrony of the FFN with tonic bursting neurons are the highest, but those of the FFN with regular spiking neurons is lowest, even when implementing the same FFN structure. For the FFN with mixed neuronal types, interestingly, the spiking propagation is very sensitive to the composition of the four types of neurons. By introducing fast spiking neurons into the homogeneous FFN composed of excitatory neurons, spiking propagation can be modified from synchronous to asynchronous. Similarly, changing the proportion of any of the types of neuron affects the spiking propagation, even for very small changes. The underlying mechanism of these observed results has also been discussed.