How Noise Affects the Synchronization Properties of Recurrent Networks of Inhibitory Neurons

• Published in: Neural computation Vol. 18; no. 5; pp. 1066 - 1110
• Main Authors: Brunel, Nicolas, Hansel, David
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.05.2006; MIT Press Journals, The; Massachusetts Institute of Technology Press (MIT Press)
• Subjects: Action Potentials - physiology; Algorithms; Animals; Applied sciences; Artificial intelligence; Biological and medical sciences; Biological Clocks - physiology; Brain - physiology; Cell Membrane - physiology; Computer science; control theory; systems; Connectionism. Neural networks; Cortical Synchronization; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Models. Methods; Humans; Ion Channels - physiology; Letters; Life Sciences; Mathematics; Models, Neurological; Nerve Net - physiology; Neural Inhibition - physiology; Neural networks; Neural Networks (Computer); Neural Pathways - physiology; Neurons; Neurons - physiology; Neurons and Cognition; Noise; Poisson Distribution; Probability and statistics; Probability theory and stochastic processes; Sciences and techniques of general use; Special processes (renewal theory, markov renewal processes, semi-markov processes, statistical mechanics type models, applications); Synaptic Transmission - physiology; Time Factors; Vertebrates: nervous system and sense organs; Asynchronous; Neural computation; Cluster analysis (statistics); Central nervous system; Firing rate; Probability distribution; Strong coupling; Neural network; Experimental study; Synchronization; Modeling; Integrate-and-fire neuron; Interneuron; Noise level; Hodgkin Huxley current; Dynamics; Oscillatory instability; Exponential model
• ISSN: 0899-7667; 1530-888X
• DOI: 10.1162/neco.2006.18.5.1066

GABAergic interneurons play a major role in the emergence of various types of synchronous oscillatory patterns of activity in the central nervous system. Motivated by these experimental facts, modeling studies have investigated mechanisms for the emergence of coherent activity in networks of inhibitory neurons. However, most of these studies have focused either when the noise in the network is absent or weak or in the opposite situation when it is strong. Hence, a full picture of how noise affects the dynamics of such systems is still lacking. The aim of this letter is to provide a more comprehensive understanding of the mechanisms by which the asynchronous states in large, fully connected networks of inhibitory neurons are destabilized as a function of the noise level. Three types of single neuron models are considered: the leaky integrate-and-fire (LIF) model, the exponential integrate-and-fire (EIF), model and conductance-based models involving sodium and potassium Hodgkin-Huxley (HH) currents. We show that in all models, the instabilities of the asynchronous state can be classified in two classes. The first one consists of clustering instabilities, which exist in a restricted range of noise. These instabilities lead to synchronous patterns in which the population of neurons is broken into clusters of synchronously firing neurons. The irregularity of the firing patterns of the neurons is weak. The second class of instabilities, termed oscillatory firing rate instabilities, exists at any value of noise. They lead to cluster state at low noise. As the noise is increased, the instability occurs at larger coupling, and the pattern of firing that emerges becomes more irregular. In the regime of high noise and strong coupling, these instabilities lead to stochastic oscillations in which neurons fire in an approximately Poisson way with a common instantaneous probability of firing that oscillates in time.