Inferring network activity from synaptic noise

Abstract

During intense network activity in vivo, cortical neurons are in a high-conductance state, in which the membrane potential (V m ) is subject to a tremendous fluctuating activity. Clearly, this ''synaptic noise'' contains information about the activity of the network, but there are presently no methods available to extract this information. We focus here on this problem from a computational neuroscience perspective, with the aim of drawing methods to analyze experimental data. We start from models of cortical neurons, in which high-conductance states stem from the random release of thousands of excitatory and inhibitory synapses. This highly complex system can be simplified by using global synaptic conductances described by effective stochastic processes. The advantage of this approach is that one can derive analytically a number of properties from the statistics of resulting V m fluctuations. For example, the global excitatory and inhibitory conductances can be extracted from synaptic noise, and can be related to the mean activity of presynaptic neurons. We show here that extracting the variances of excitatory and inhibitory synaptic conductances can provide estimates of the mean temporal correlation-or level of synchrony-among thousands of neurons in the network. Thus, ''probing the network'' through intracellular V m activity is possible and constitutes a promising approach, but it will require a continuous effort combining theory, computational models and intracellular physiology.

A 4-fJ/Spike Artificial Neuron in 65 nm CMOS Technology

“…Such a value was confirmed experimentally, and the first conclusion is that the noise has little impact. It is also worth mentioning that in biology, RMS voltage fluctuations are also in the mV range (Rudolph and Destexhe, 2004) but these fluctuations are obtained for a much higher capacitance (several 10's of pF, see the discussion in the introduction of this paper). This could be explained by the fact that the noise temperature T n in biology is higher than the one considered for the neuron circuit.…”

Discharge Properties of Motor Units of the Abductor Hallucis Muscle During Cramp Contractions

“…Synaptic noise of the membrane voltage trajectory results from the integration of inhibitory and excitatory afferent inputs (Berg et al 2007;Destexhe and Contreras 2006;Stein et al 2005). The balanced increase in these two types of inputs increases the discharge variability, if the inputs are uncorrelated, by increasing the fluctuations in the resting membrane potential (Berg et al 2007;Rudolph and Destexhe 2004;Rudolph et al 2007). The observation that cramp contractions are associated with high ISI variability indicates that the synaptic noise is likely greater during cramp with respect to voluntary contractions.…”

Complexity in Neuronal Noise Depends on Network Interconnectivity

“…At the cellular level, this activity is thought to arise out of the integrated dynamical activity of various intrinsic and extrinsic sources, including thermal (i.e., Johnson-Nyquist) noise, shot noise, ionic channel fluctuations, chemical synaptic events and gap junction-related activity. 8,12,16,23,35,37,42,43,53 The importance of understanding the mechanisms underlying cellular NLA in the nervous system, however, emerges from studies identifying the relevance of its information content to the activity of the surrounding local network. 35 Strong correlations have been reported, for example, between the intracellular activity of neocortical neurons and comparatively macroscopic EEG recordings, suggesting that individual cellular activity reflects that of the local network.…”

“…8,12,16,23,35,37,42,43,53 The importance of understanding the mechanisms underlying cellular NLA in the nervous system, however, emerges from studies identifying the relevance of its information content to the activity of the surrounding local network. 35 Strong correlations have been reported, for example, between the intracellular activity of neocortical neurons and comparatively macroscopic EEG recordings, suggesting that individual cellular activity reflects that of the local network. 36 One study reported on the smoothing effect of cellular noise on the relation between membrane potential and spike rates throughout the brain, ultimately contributing to contrast invariance in the cat visual cortex.…”

“…17,27 At the cellular level, this activity is thought to arise out of the integrated dynamical activity of intrinsic and extrinsic sources, including thermal noise, shot noise, ionic channel fluctuations, chemical synaptic events, and gap junctions. 8,12,16,23,35,42,43,50,53 Amongst other functions, it is thought to enhance neuronal signal detection via stochastic resonance. 41 Here, we introduce the term ''noise-like activity'' (NLA) to describe subthreshold, time-varying, background electrical membrane potential fluctuations at the cellular level of the nervous system.…”