Physiologically realistic gamma activity produced in silico by weakening the PING attractor state

Abstract

Network oscillations in the gamma (30-80 Hz) frequency range are implicated in many vital neurological functions. The seminal Pyramidal Interneuron Network Gamma (PING) mechanism produces gamma oscillations in silico with dynamics associated with a strongly attracting limit cycle. However, this activity is stronger and more stable than what is observed during in vivo gamma oscillations, which are transient and driven by sparse spiking activity. Here we describe multiple biophysically motivated changes degenerately driving PING-motivated networks to produce more realistic gamma activity. Increased heterogeneity, synapse-like noise, and a depolarized chloride reversal potential each destabilize the attractor represented by PING-driven activity: minor alterations disrupt the unrealistic organization of idealized PING oscillations, while larger changes entirely prevent this overly-synchronous network activity. This allows new dynamics to emerge: spontaneous and transient increases in gamma-band oscillatory power with excitatory cells only weakly entrained to the population rhythm. These features, better approximating the in vivo reality, mirror a systems oscillatory return to a stable focus following noise-induced perturbations. In fact, dynamical systems with both a stable focus and limit cycle are associated with Hopf bifurcations, which have been characterized in models of reciprocally connected excitatory and inhibitory neuronal populations as required for PING. We therefore propose a revised explanation for physiologically-realistic gamma activity that retains the key elements of the PING mechanism--strong reciprocal connection between excitatory and inhibitory neurons--but where biophysical phenomena bias the system towards damped oscillations around a weakly stable focus rather than a stable limit cycle. Significance StatementThe mechanisms underlying network oscillations at gamma frequencies have long been a focus of computational neuroscience. This research has produced idealized mechanisms yielding highly synchronous, active, and stable oscillations; however, in vivo gamma rhythms are transient with sparse neuronal spiking. Here, we illustrate minimal, biophysically relevant adjustments to established Pyramidal Interneuron Network Gamma (PING) networks that bridge a major divide between these traditional in silico systems and the experimental reality. Uncorrelated noisy input and a depolarized chloride reversal potential disrupt stereotyped PING rhythms and promote more realistic transient increased gamma power in network activity. This important step forward in the modeling of gamma oscillations illustrates how including biophysical detail can promote more realistic activity in computational systems.