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

Brunel, Nadège; Hansel, David

doi:10.1162/neco.2006.18.5.1066

Cited by 140 publications

(181 citation statements)

References 54 publications

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“…Although the global potential X G is an important ensemble-averaged quantity to describe synchronization in computational neuroscience, it is practically difficult to directly get X G in real experiments. To overcome this difficulty, instead of X G , we use the IPFR which is an experimentally-obtainable population quantity used in both the experimental and the computational neuroscience (Brunel andHakim 1999, 2008;Brunel 2000;Brunel and Wang 2003;Geisler et al 2005;Brunel and Hansel 2006;Wang 2010). The IPFR is obtained from the raster plot of spikes which is a collection of spike trains of individual neurons.…”

Section: Frequency-domain Order Parameters For the Burst And Spike Symentioning

confidence: 99%

“…Population synchronization may be well visualized in the raster plot of neural spikes which can be obtained in experiments. Instantaneous population firing rate (IPFR), RðtÞ, which is directly obtained from the raster plot of spikes, is a realistic population quantity describing collective behaviors in both the computational and the experimental neuroscience (Brunel andHakim 1999, 2008;Brunel 2000;Brunel and Wang 2003;Geisler et al 2005;Brunel and Hansel 2006;Wang 2010). This experimentally-obtainable RðtÞ is in contrast to the ensembleaveraged potential X G which is often used as a population quantity in the computational neuroscience, because to directly get X G in real experiments is very difficult.…”

Section: Introductionmentioning

confidence: 99%

“…In the field of neuroscience, power-spectral analysis of a time-series xðtÞ (e.g., neuronal membrane potential) is often made to examine how the variance of the data xðtÞ is distributed over the frequency components into which xðtÞ may be decomposed (Brunel andHakim 1999, 2008;Brunel 2000;Brunel and Wang 2003;Geisler et al 2005;Brunel and Hansel 2006). Following this conventional direction, we investigate the synchronization transitions of bursting neurons in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

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Frequency-domain order parameters for the burst and spike synchronization transitions of bursting neurons

Kim

Lim

2015

Cogn Neurodyn

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We are interested in characterization of synchronization transitions of bursting neurons in the frequency domain. Instantaneous population firing rate (IPFR) RðtÞ, which is directly obtained from the raster plot of neural spikes, is often used as a realistic collective quantity describing population activities in both the computational and the experimental neuroscience. For the case of spiking neurons, a realistic time-domain order parameter, based on RðtÞ, was introduced in our recent work to characterize the spike synchronization transition. Unlike the case of spiking neurons, the IPFR RðtÞ of bursting neurons exhibits population behaviors with both the slow bursting and the fast spiking timescales. For our aim, we decompose the IPFR RðtÞ into the instantaneous population bursting rate R b ðtÞ (describing the bursting behavior) and the instantaneous population spike rate R s ðtÞ (describing the spiking behavior) via frequency filtering, and extend the realistic order parameter to the case of bursting neurons. Thus, we develop the frequency-domain bursting and spiking order parameters which are just the bursting and spiking ''coherence factors'' b b and b s of the bursting and spiking peaks in the power spectral densities of R b and R s (i.e., ''signal to noise'' ratio of the spectral peak height and its relative width). Through calculation of b b and b s , we obtain the bursting and spiking thresholds beyond which the burst and spike synchronizations break up, respectively. Consequently, it is shown in explicit examples that the frequency-domain bursting and spiking order parameters may be usefully used for characterization of the bursting and the spiking transitions, respectively.

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Section: Frequency-domain Order Parameters For the Burst And Spike Symentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Frequency-domain order parameters for the burst and spike synchronization transitions of bursting neurons

Kim

Lim

2015

Cogn Neurodyn

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“…In such a network, the emergence of SI oscillations can be investigated by analyzing the stability of the network firing rate in the AI state [18,19]. A small perturbation in the steady-state firing rate…”

Section: Control Of Si Activity In I-i Networkmentioning

confidence: 99%

“…In addition, the physiologically plausible SI oscillations are known to be robust to both noise and heterogeneities [18][19][20] and, therefore, require a more differentiated control approach.…”

Section: Introductionmentioning

confidence: 99%

Recovery of dynamics and function in spiking neural networks by closed-loop control

Vlachos

Taşkın

Aertsen

et al. 2015

Preprint

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There is a growing interest in developing novel brain stimulation methods to control disease--related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system. Author SummaryBrain stimulation is being used to ease symptoms in several neurological disorders in cases where pharmacological treatment is not effective (anymore). The most common way for stimulation so far has been to apply a fixed, predetermined stimulus irrespective of the actual state of the brain or the condition of the patient. Recently, alternative strategies such as event-triggered stimulation protocols have attracted the interest of researchers. In these protocols the state of the affected brain area is continuously monitored, but the stimulus is only applied if certain criteria are met. Here we go one step further and present a truly closed-loop stimulation protocol. That is, a stimulus is being continuously provided and the magnitude of the stimulus depends, at any point in time, on the ongoing neural activity dynamics of the affected brain area. This results not only in suppression of the pathological activity, but also in a partial recovery of the transfer function of the activity dynamics. Thus, the ability of the lesioned brain area to carry out relevant computations is restored up to a point as well. PLOS Computational Biology |

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From mechanisms to functions: The role of theta and gamma coherence in the intrahippocampal circuits

Mysin

Shubina

2022

Hippocampus

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Brain rhythms are essential for information processing in neuronal networks. Oscillations recorded in different brain regions can be synchronized and have a constant phase difference, that is, they can be coherent. Coherence between local field potential (LFP) signals from different brain regions may be correlated with the performance of cognitive tasks, indicating that these regions of the brain are jointly involved in the information processing. Why does coherence occur and how is it related to the information transfer between different regions of the hippocampal formation? In this article, we discuss possible mechanisms of theta and gamma coherence and its role in the hippocampus‐dependent attention and memory processes, since theta and gamma rhythms are most pronounced in these processes. We review in vivo studies of interactions between different regions of the hippocampal formation in theta and gamma frequency bands. The key propositions of the review are as follows: (1) coherence emerges from synchronous postsynaptic currents in principal neurons as a result of synchronization of neuronal spike activity; (2) the synchronization of neuronal spike patterns in two regions of the hippocampal formation can be realized through induction or resonance; (3) coherence at a specific time point reflects the transfer of information between the regions of the hippocampal formation; (4) the physiological roles of theta and gamma coherence are different due to their different functions and mechanisms of generation. All hippocampal neurons are involved in theta activity, and theta coherence arranges the firing order of principal neurons throughout the hippocampal formation. In contrast, gamma coherence reflects the coupling of active neuronal ensembles. Overall, the coherence of LFPs between different areas of the brain is an important physiological process based on the synchronized neuronal firing, and it is essential for cooperative information processing.

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

Cited by 140 publications

References 54 publications

Frequency-domain order parameters for the burst and spike synchronization transitions of bursting neurons

Frequency-domain order parameters for the burst and spike synchronization transitions of bursting neurons

Recovery of dynamics and function in spiking neural networks by closed-loop control

From mechanisms to functions: The role of theta and gamma coherence in the intrahippocampal circuits

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