Global firing rate contrast enhancement in E/I neuronal networks by recurrent synchronized inhibition

2018 ◽  
Vol 28 (10) ◽  
pp. 106324 ◽  
Author(s):  
Fang Han ◽  
Xiaochun Gu ◽  
Zhijie Wang ◽  
Hong Fan ◽  
Jinfeng Cao ◽  
...  
Keyword(s):  
Firing Rate ◽  
Contrast Enhancement ◽  
Neuronal Networks

scholarly journals High-Frequency Synchronization Improves Firing Rate Contrast and Information Transmission Efficiency in E/I Neuronal Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fang Han ◽  
Zhijie Wang ◽  
Hong Fan ◽  
Yaopeng Zhang
Keyword(s):  
Information Transmission ◽  
Firing Rate ◽  
Contrast Enhancement ◽  
High Frequency ◽  
Functional Role ◽  
Neuronal Networks ◽  
Time Window ◽  
Frequency Synchronization ◽  
Neuronal System ◽  
Information Encoding

High-frequency synchronization has been found in many real neural systems and is confirmed by excitatory/inhibitory (E/I) network models. However, the functional role played by it remains elusive. In this paper, it is found that high-frequency synchronization in E/I neuronal networks could improve the firing rate contrast of the whole network, no matter if the network is fully connected or randomly connected, with noise or without noise. It is also found that the global firing rate contrast enhancement can prevent the number of spikes of the neurons measured within the limited time window from being confused by noise, thereby enhancing the information encoding efficiency (quantified by entropy theory here) of the neuronal system. The mechanism of firing rate contrast enhancement is also investigated. Our work implies a possible functional role in information transmission of high-frequency synchronization in neuronal systems.

scholarly journals Intrinsic Dynamics in Neuronal Networks. I. Theory

2000 ◽  
Vol 83 (2) ◽  
pp. 808-827 ◽  
Author(s):  
P. E. Latham ◽  
B. J. Richmond ◽  
P. G. Nelson ◽  
S. Nirenberg
Keyword(s):  
Firing Rate ◽  
Firing Pattern ◽  
Neuronal Networks ◽  
Network Connectivity ◽  
Excitatory Input ◽  
High Rate ◽  
Inhibitory Neurons ◽  
Inhibitory Input ◽  
Dynamic Interactions ◽  
Firing Patterns

Many networks in the mammalian nervous system remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How are these firing patterns generated? Specifically, how do dynamic interactions between excitatory and inhibitory neurons produce these firing patterns, and how do networks switch from one firing pattern to the other? We investigated these questions theoretically by examining the intrinsic dynamics of large networks of neurons. Using both a semianalytic model based on mean firing rate dynamics and simulations with large neuronal networks, we found that the dynamics, and thus the firing patterns, are controlled largely by one parameter, the fraction of endogenously active cells. When no endogenously active cells are present, networks are either silent or fire at a high rate; as the number of endogenously active cells increases, there is a transition to bursting; and, with a further increase, there is a second transition to steady firing at a low rate. A secondary role is played by network connectivity, which determines whether activity occurs at a constant mean firing rate or oscillates around that mean. These conclusions require only conventional assumptions: excitatory input to a neuron increases its firing rate, inhibitory input decreases it, and neurons exhibit spike-frequency adaptation. These conclusions also lead to two experimentally testable predictions: 1) isolated networks that fire at low rates must contain endogenously active cells and 2) a reduction in the fraction of endogenously active cells in such networks must lead to bursting.

scholarly journals Multi-frequency activation of neuronal networks by coordinated reset stimulation

2010 ◽  
Vol 1 (1) ◽  
pp. 75-85 ◽  
Author(s):  
Borys Lysyansky ◽  
Oleksandr V. Popovych ◽  
Peter A. Tass
Keyword(s):  
Firing Rate ◽  
Neuronal Networks ◽  
Epileptic Seizures ◽  
Neuronal Population ◽  
Arnold Tongues ◽  
Clustering Pattern ◽  
Coordinated Reset

We computationally study whether it is possible to stimulate a neuronal population in such a way that its mean firing rate increases without an increase of the population's net synchronization. For this, we use coordinated reset (CR) stimulation, which has previously been developed to desynchronize populations of oscillatory neurons. Intriguingly, delivered to a population of predominantly silent FitzHugh–Nagumo or Hindmarsh–Rose neurons at sufficient stimulation amplitudes, CR robustly causes a multi-frequency activation: different Arnold tongues such as 1 : 1 or n  :  m entrained neuronal clusters emerge, which consist of phase-shifted sub clusters. Owing to the clustering pattern the neurons' timing is well balanced, so that in total there is no synchronization. Our findings may contribute to the development of novel and safe stimulation treatments that specifically counteract cerebral hypo-activity without promoting pathological synchronization or inducing epileptic seizures.

scholarly journals Simulation scalability of large brain neuronal networks thanks to time asynchrony

2021 ◽  
Author(s):  
Cyrille Mascart ◽  
Gilles Scarella ◽  
Patricia Reynaud-Bouret ◽  
Alexandre Muzy
Keyword(s):  
Firing Rate ◽  
Neuronal Networks ◽  
Synaptic Connection ◽  
Real Data ◽  
Random Model ◽  
Model Parameters ◽  
Desktop Computer ◽  
Connection Probability ◽  
Large Brain ◽  
Single Processor

We present here a new algorithm based on a random model for simulating efficiently large brain neuronal networks. Model parameters (mean firing rate, number of neurons, synaptic connection probability and postsynaptic duration) are easy to calibrate further on real data experiments. Based on time asynchrony assumption, both computational and memory complexities are proved to be theoretically linear with the number of neurons. These results are experimentally validated by sequential simulations of millions of neurons and billions of synapses in few minutes on a single processor desktop computer.

scholarly journals Intrinsic Dynamics in Neuronal Networks. II. Experiment

2000 ◽  
Vol 83 (2) ◽  
pp. 828-835 ◽  
Author(s):  
P. E. Latham ◽  
B. J. Richmond ◽  
S. Nirenberg ◽  
P. G. Nelson
Keyword(s):  
Theoretical Analysis ◽  
Firing Rate ◽  
Neuronal Networks ◽  
Firing Patterns ◽  
Firing Rates ◽  
Recurrent Excitation ◽  
Key Parameter ◽  
Mammalian Cns

Neurons in many regions of the mammalian CNS remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How these firing patterns are maintained in the presence of powerful recurrent excitation, and how networks switch between them, is not well understood. In the previous paper, we addressed these issues theoretically; in this paper we address them experimentally. We found in both studies that a key parameter in controlling firing patterns is the fraction of endogenously active cells. The theoretical analysis indicated that steady firing rates are possible only when the fraction of endogenously active cells is above some threshold, that there is a transition to bursting when it falls below that threshold, and that networks becomes silent when the fraction drops to zero. Experimentally, we found that all steadily firing cultures contain endogenously active cells, and that reducing the fraction of such cells in steadily firing cultures causes a transition to bursting. The latter finding implies indirectly that the elimination of endogenously active cells would cause a permanent drop to zero firing rate. The experiments described here thus corroborate the theoretical analysis.

Impact of Partial Time Delay on Temporal Dynamics of Watts–Strogatz Small-World Neuronal Networks

2017 ◽  
Vol 27 (07) ◽  
pp. 1750112 ◽  
Author(s):  
Hao Yan ◽  
Xiaojuan Sun
Keyword(s):  
Time Delay ◽  
Firing Rate ◽  
Neuronal Networks ◽  
Temporal Dynamics ◽  
Great Influence ◽  
Small World ◽  
Temporal Coherence ◽  
Small Time ◽  
Neuronal Firing ◽  
Two Parameters

In this paper, we mainly discuss effects of partial time delay on temporal dynamics of Watts–Strogatz (WS) small-world neuronal networks by controlling two parameters. One is the time delay [Formula: see text] and the other is the probability of partial time delay [Formula: see text]. Temporal dynamics of WS small-world neuronal networks are discussed with the aid of temporal coherence and mean firing rate. With the obtained simulation results, it is revealed that for small time delay [Formula: see text], the probability [Formula: see text] could weaken temporal coherence and increase mean firing rate of neuronal networks, which indicates that it could improve neuronal firings of the neuronal networks while destroying firing regularity. For large time delay [Formula: see text], temporal coherence and mean firing rate do not have great changes with respect to [Formula: see text]. Time delay [Formula: see text] always has great influence on both temporal coherence and mean firing rate no matter what is the value of [Formula: see text]. Moreover, with the analysis of spike trains and histograms of interspike intervals of neurons inside neuronal networks, it is found that the effects of partial time delays on temporal coherence and mean firing rate could be the result of locking between the period of neuronal firing activities and the value of time delay [Formula: see text]. In brief, partial time delay could have great influence on temporal dynamics of the neuronal networks.

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