Background Synaptic Activity as a Switch Between Dynamical States in a Network

Abstract

A bright red light may trigger a sudden motor action in a driver crossing an intersection: stepping at once on the brakes. The same red light, however, may be entirely inconsequential if it appears, say, inside a movie theater. Clearly, context determines whether a particular stimulus will trigger a motor response, but what is the neural correlate of this? How does the nervous system enable or disable whole networks so that they are responsive or not to a given sensory signal? Using theoretical models and computer simulations, I show that networks of neurons have a built-in capacity to switch between two types of dynamic state: one in which activity is low and approximately equal for all units, and another in which different activity distributions are possible and may even change dynamically. This property allows whole circuits to be turned on or off by weak, unstructured inputs. These results are illustrated using networks of integrate-and-fire neurons with diverse architectures. In agreement with the analytic calculations, a uniform background input may determine whether a random network has one or two stable firing levels; it may give rise to randomly alternating firing episodes in a circuit with reciprocal inhibition; and it may regulate the capacity of a center-surround circuit to produce either self-sustained activity or traveling waves. Thus, the functional properties of a network may be drastically modified by a simple, weak signal. This mechanism works as long as the network is able to exhibit stable firing states, or attractors.

How well do mean field theories of spiking quadratic-integrate-and-fire networks work in realistic parameter regimes?

“…First, it forms the substrate upon which computations are built, and has been shown to play a major role in determining exactly how those computations are carried out (Latham et al, 2000a,b;Salinas, 2003;Latham and Nirenberg, 2004;Roudi and Latham, 2007). Second, it leads naturally to the next question: would mean field theory apply to networks with structured connectivity, which are just as prone to oscillations as randomly connected ones?…”

A Microcircuit Model of the Frontal Eye Fields

“…The basic elements of the FEF model, IF neurons, and synapses, were defined similarly to those of Salinas (2003). (In our common effort for transparency and reproducibility of computer simulations, our complete code is available at www.ini.uzh.ch/ϳjakob/ code/FEF_DEMO.zip.)…”

“…Second, the external inputs to each neuron were modeled as fluctuating conductances g ext (t) (Salinas, 2003) and added to the internal conductances:…”

Mechanisms for Frequency Control in Neuronal Competition Models

“…Slow adaptation may be implemented via a cellular mechanism (fatigue in the spike generation mechanism) or a negative feedback in the coupling (depression of the synaptic transmission mechanism). In some neuronal competition models the alternations may be irregular and caused primarily by noise, with adaptation playing a secondary role [30,10,24].…”