Extending the Functional Subnetwork Approach to a Generalized Linear Integrate-and-Fire Neuron Model - Szczecinski, Nicholas S.; Quinn, Roger D.; Hunt, Alexander J. (Frontiers in Neurorobotics, 2020)
Engineering neural networks to perform specific tasks often represents a monumental challenge in determining network architecture and parameter values. In this work, we extend our previously-developed method for tuning networks of nonspiking neurons, the “Functional subnetwork approach” (FSA), to the tuning of networks composed of spiking neurons. This extension enables the direct assembly and tuning of networks of spiking neurons and synapses based on the network’s intended function, without the use of global optimization or machine learning. To extend the FSA, we show that the dynamics of a generalized linear integrate and fire (GLIF) neuron model have fundamental similarities to those of a nonspiking leaky integrator neuron model. We derive analytical expressions that show functional parallels between: 1) A spiking neuron’s steady-state spiking frequency and a nonspiking neuron’s steady-state voltage in response to an applied current; 2) a spiking neuron’s transient spiking frequency and a nonspiking neuron’s transient voltage in response to an applied current; and 3) a spiking synapse’s average conductance during steady spiking and a nonspiking synapse’s conductance. The models become more similar as additional spiking neurons are added to each population “node” in the network. We apply the FSA to model a neuromuscular reflex pathway two different ways: Via nonspiking components and then via spiking components. These results provide a concrete example of how a single nonspiking neuron may model the average spiking frequency of a population of spiking neurons. The resulting model also demonstrates that by using the FSA, models can be constructed that incorporate both spiking and nonspiking units. This work facilitates the construction of large networks of spiking neurons and synapses that perform specific functions, for example, those implemented with neuromorphic computing hardware, by providing an analytical method for directly tuning their parameters without time-consuming optimization or learning.