Model-Agnostic Neural Mean Field With The Refractory SoftPlus Transfer Function.

ABSTRACT: Due to the complexity of neuronal networks and the nonlinear dynamics of individual neurons, it is challenging to develop a systems-level model which is accurate enough to be useful yet tractable enough to apply. Mean-field models which extrapolate from single-neuron descriptions to large-scale models can be derived from the neuron's transfer function, which gives its firing rate as a function of its synaptic input. However, analytically derived transfer functions are applicable only to the neurons and noise models from which they were originally derived. In recent work, approximate transfer functions have been empirically derived by fitting a sigmoidal curve, which imposes a maximum firing rate and applies only in the diffusion limit, restricting applications. In this paper, we propose an

SUBMITTER: Spaeth A 

PROVIDER: S-EPMC10871173 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature