Mechanistic Neural Mass Modeling: from physiology to pathophysiological brain activity

Abstract

Brain rhythms emerge from the mean-field activity of networks of neurons. There have been many efforts to build mathematical and computational embodiments in the form of discrete cell-group activities - termed neural masses - to understand particular the origins of evoked potentials, intrinsic patterns of activities such as theta, regulation of sleep, Parkinsons disease related dynamics, and instabilities such as seizure dynamics. As originally utilized, standard neural masses convert input through a sigmoidal function to a firing rate, and firing rate through a synaptic alpha function to other masses. Here we define a process to build almost from first principles (i.e. Hodgkin Huxley type models) mean field models of different neuron types that duplicate the stability, firing rate, and associated bifurcations as function of relevant slow variables - such as extracellular potassium - and synaptic current; and whose output is both firing rate and impact on the slow variables - such as transmembrane potassium flux. Small networks composed of just excitatory and inhibitory masses demonstrate expected dynamical states including stable firing, runaway excitation and depolarization block, and these transitions change in biologically observed ways with changes in extracellular potassium and excitatory-inhibitory balance.