Discrete Modeling of Multi-Transmitter Neural Networks with Neuron Competition
This work addresses the challenge of computationally tractable modeling of neural rhythms for neuroscience researchers, but it is incremental as it builds on existing discrete modeling approaches.
The authors tackled the problem of modeling rhythmic activity in central pattern generators by proposing a discrete model that incorporates nonsynaptic interactions and neurotransmitter diversity, showing that neuronal competition generates proper rhythms in examples like a half-center oscillator and a pond snail feeding network.
We propose a novel discrete model of central pattern generators (CPG), neuronal ensembles generating rhythmic activity. The model emphasizes the role of nonsynaptic interactions and the diversity of electrical properties in nervous systems. Neurons in the model release different neurotransmitters into the shared extracellular space (ECS) so each neuron with the appropriate set of receptors can receive signals from other neurons. We consider neurons, differing in their electrical activity, represented as finite-state machines functioning in discrete time steps. Discrete modeling is aimed to provide a computationally tractable and compact explanation of rhythmic pattern generation in nervous systems. The important feature of the model is the introduced mechanism of neuronal competition which is shown to be responsible for the generation of proper rhythms. The model is illustrated with two examples: a half-center oscillator considered to be a basic mechanism of emerging rhythmic activity and the well-studied feeding network of a pond snail. Future research will focus on the neuromodulatory effects ubiquitous in CPG networks and the whole nervous systems.