The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

Abstract

Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes that occur during development that may be observable even in actual neural systems where these changes are convoluted with changes in synaptic connectivity and intrinsic neural plasticity.

Population Bursts in a Modular Neural Network as a Mechanism for Synchronized Activity in KNDy Neurons

“…However, there are three instances when the entire cluster is active, i.e., there is a population burst that we refer to as a “cluster event” (CE). The onset is triggered by a few initiating cells that spike and, due to the extensive coupling, cause others in the cluster to spike [28]. An episode terminates due to the buildup of synaptic depression that reduces the coupling between the cells to a level that is eventually too low to continue the regenerative activity [28, 33].…”

“…We do not assume any special properties (such as rhythmic bursting) for the KNDy neurons, so we model them using a reduced Hodgkin-Huxley model, as we used in previous studies [27][28][29][30]. In Ca 2+ measurements from intact animals, SEs occur once every 5-20 min [15,22,26].…”

Mitigating Computer Limitations in Replicating Numerical Simulations of a Neural Network Model With Hodgkin-Huxley-Type Neurons

“…Early studies already modeled the spontaneous neural network activity during early development, which is characterized by a cyclic profile where episodes of strong neural activity are followed by episodes of quiescence (Tabak et al, 2010 ; Blanco et al, 2017 ). This pattern had been seen in regions such as: the spinal cord (Tabak et al, 2001 ), retina (Grzywacz and Sernagor, 2000 ) and cortical networks (Opitz et al, 2002 ).…”

“…Two variables are the main outputs for each neuron, namely the spontaneous activity, and the synaptic efficacy, represented by a j and s j , respectively. These two variables present a cyclic profile; a j has a fast profile variable with elevated activity episodes divided by quiescent periods (inter-episode intervals), while s j is a slow profile during quiescent periods of a j , but has fast depression during the high activity episodes (Tabak et al, 2010 ; Blanco et al, 2017 ). Their equations have the form:…”