Effects of shift in the excitatory-inhibitory balance on firing statistics

2014 ◽  
Vol 1 ◽  
pp. 489-492
Author(s):  
Kantaro Fujiwara ◽  
Tohru Ikeguchi
Keyword(s):  
Firing Statistics

scholarly journals Delayed feedback makes neuronal firing statistics non-Markovian

2013 ◽  
Vol 64 (12) ◽  
pp. 1793-1815 ◽  
Author(s):  
A. K. Vidybida ◽  
K. G. Kravchuk
Keyword(s):  
Delayed Feedback ◽  
Neuronal Firing ◽  
Firing Statistics

Spiking Statistics of Excitatory Neuron with Feedback

2012 ◽  
Vol 3 (2) ◽  
pp. 1-42
Author(s):  
Alexander Vidybida ◽  
Kseniia Kravchuk
Keyword(s):  
Time Delay ◽  
Probability Density ◽  
Conditional Probability ◽  
Delayed Feedback ◽  
Neuronal Firing ◽  
Conditional Probability Density ◽  
Function Type ◽  
Type Singularity ◽  
Poisson Stream ◽  
Firing Statistics

Firing statistics of excitatory binding neuron (BN) is considered. The neuron is driven externally by a Poisson stream. Influence of feedback, which conveys every output impulse to the input with time delay , on the statistics of output spikes is studied. The resulting output stream is not Poissonian, and the authors obtain its inter-spike intervals (ISI) distribution for the case of BN, BN with instantaneous, , and delayed, , feedback. Output statistics of neuron with delayed feedback differs essentially from that found for the case of no feedback as well as from the case of instantaneous feedback. ISI distributions, found for delayed feedback, are characterized with jumps, derivative discontinuities and include -function type singularity. Also, for non-zero refractory time, the authors obtain multiple-ISI conditional probability density and prove, that delayed feedback presence results in non-Markovian statistics of neuronal firing. It is concluded, that delayed feedback presence can radically change neuronal firing statistics.

scholarly journals Firing statistics in the bistable regime of neurons with homoclinic spike generation

2021 ◽  
Vol 103 (1) ◽  
Author(s):  
Jan-Hendrik Schleimer ◽  
Janina Hesse ◽  
Susana Andrea Contreras ◽  
Susanne Schreiber
Keyword(s):  
Spike Generation ◽  
Bistable Regime ◽  
Firing Statistics

scholarly journals Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution

2021 ◽  
Vol 15 ◽  
Author(s):  
Stefan Dasbach ◽  
Tom Tetzlaff ◽  
Markus Diesmann ◽  
Johanna Senk
Keyword(s):  
Spike Train ◽  
Neuronal Networks ◽  
Synaptic Weight ◽  
Spatial Scales ◽  
Correlation Coefficients ◽  
Discrete Distribution ◽  
Vast Number ◽  
Dynamical Characteristics ◽  
The Mean ◽  
Firing Statistics

The representation of the natural-density, heterogeneous connectivity of neuronal network models at relevant spatial scales remains a challenge for Computational Neuroscience and Neuromorphic Computing. In particular, the memory demands imposed by the vast number of synapses in brain-scale network simulations constitute a major obstacle. Limiting the number resolution of synaptic weights appears to be a natural strategy to reduce memory and compute load. In this study, we investigate the effects of a limited synaptic-weight resolution on the dynamics of recurrent spiking neuronal networks resembling local cortical circuits and develop strategies for minimizing deviations from the dynamics of networks with high-resolution synaptic weights. We mimic the effect of a limited synaptic weight resolution by replacing normally distributed synaptic weights with weights drawn from a discrete distribution, and compare the resulting statistics characterizing firing rates, spike-train irregularity, and correlation coefficients with the reference solution. We show that a naive discretization of synaptic weights generally leads to a distortion of the spike-train statistics. If the weights are discretized such that the mean and the variance of the total synaptic input currents are preserved, the firing statistics remain unaffected for the types of networks considered in this study. For networks with sufficiently heterogeneous in-degrees, the firing statistics can be preserved even if all synaptic weights are replaced by the mean of the weight distribution. We conclude that even for simple networks with non-plastic neurons and synapses, a discretization of synaptic weights can lead to substantial deviations in the firing statistics unless the discretization is performed with care and guided by a rigorous validation process. For the network model used in this study, the synaptic weights can be replaced by low-resolution weights without affecting its macroscopic dynamical characteristics, thereby saving substantial amounts of memory.

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