Effects of Input Synchrony of the Firing Rate of Three-Conductance Cortical Neuron Model

1999 ◽  
Keyword(s):  
Firing Rate ◽  
Cortical Neuron ◽  
Neuron Model

Variability in somatosensory cortical neuron discharge: effects on capacity to signal different stimulus conditions using a mean rate code

1978 ◽  
Vol 41 (2) ◽  
pp. 338-349 ◽  
Author(s):  
R. C. Schreiner ◽  
G. K. Essick ◽  
B. L. Whitsel
Keyword(s):  
Firing Rate ◽  
Linear Equation ◽  
Cortical Neuron ◽  
Single Neuron ◽  
Rate Code ◽  
Firing Rates ◽  
Discrimination Criterion ◽  
The Mean ◽  
Change In Mean ◽  
The Relationship

1. The present study is based on the demonstration (8, 9) that the relationship between mean interval (MI) and standard deviation (SD) for stimulus-driven activity recorded from SI neurons is well fitted by the linear equation SD = a X MI + b and on the observations that the values of the slope (a) and y intercept (b) parameters of this relationship are independent of stimulus conditions and may vary widely from one neuron to the next (8). 2. A criterion for the discriminability of two different mean firing rates requiring that the mean intervals of their respective interspike interval (ISI) distributions be separated by a fixed interval (expressed in SD units) is developed and, on the basis of this criterion, a graphical display of the capacity of a neuron with a known SD-MI relationship to reflect a change in stimulus conditions with a change in mean firing rate is derived. Using this graphical approach, it is shown that the parameters of the SD-MI relationship for a single neuron determine a range of firing frequencies, within which that neuron exhibits the greatest capacity to signal differences in stimulus conditions using a frequency code. 3. The discrimination criterion is modified to incorporate the changes in the symmetry of the ISI distribution observed to accompany changes in mean firing rate. It is shown that, although the observed symmetry changes do influence the capacity of a cortical neuron to signal a change in stimulus conditions with a change in mean firing rate, they do not alter the range of firing rates (determined by the parameters of the SD-MI relationship) within which the capacity for discrimination is maximal. 4. The maximal number of firing levels that can be distinguished by a somatosensory cortical neuron (using the same discrimination criterion described above) discharging within a specified range of mean frequencies also is demonstrated to depend on the parameters of the linear equation which relates SD to MI. 5. Two approaches based on the t test for differences between two means are developed in an attempt to ascertain the minimum separation of the mean intervals of the ISI distributions necessary for two different mean firing rates to be discriminated with 80% certainty.

Changes of Firing Rate Induced by Changes of Phase Response Curve in Bifurcation Transitions

2014 ◽  
Vol 26 (11) ◽  
pp. 2395-2418 ◽  
Author(s):  
Yasuomi D. Sato ◽  
Kazuyuki Aihara
Keyword(s):  
Firing Rate ◽  
Neuron Model ◽  
Phase Response Curve ◽  
Dimensional Space ◽  
External Input ◽  
Phase Response ◽  
Input Current ◽  
Rate Curve ◽  
Response Curves ◽  
Snic Bifurcation

We study dynamical mechanisms responsible for changes of the firing rate during four different bifurcation transitions in the two-dimensional Hindmarsh-Rose (2DHR) neuron model: the saddle node on an invariant circle (SNIC) bifurcation to the supercritical Andronov-Hopf (AH) one, the SNIC bifurcation to the saddle-separatrix loop (SSL) one, the AH bifurcation to the subcritical AH (SAH) one, and the SSL bifurcation to the AH one. For this purpose, we study slopes of the firing rate curve with respect to not only an external input current but also temperature that can be interpreted as a timescale in the 2DHR neuron model. These slopes are mathematically formulated with phase response curves (PRCs), expanding the firing rate with perturbations of the temperature and external input current on the one-dimensional space of the phase [Formula: see text] in the 2DHR oscillator. By analyzing the two different slopes of the firing rate curve with respect to the temperature and external input current, we find that during changes of the firing rate in all of the bifurcation transitions, the calculated slope with respect to the temperature also changes. This is largely dependent on changes in the PRC size that is also related to the slope with respect to the external input current. Furthermore, we find phase transition–like switches of the firing rate with a possible increase of the temperature during the SSL-to-AH bifurcation transition.

scholarly journals A fast-computational spiking neuron model adaptable to any cortical neuron

2009 ◽  
Vol 10 (S1) ◽  
Author(s):  
Ryota Kobayashi ◽  
Yasuhiro Tshubo ◽  
Shigeru Shinomoto
Keyword(s):  
Cortical Neuron ◽  
Neuron Model ◽  
Spiking Neuron ◽  
Spiking Neuron Model

scholarly journals Channel Density Regulation of Firing Patterns in a Cortical Neuron Model

2006 ◽  
Vol 90 (12) ◽  
pp. 4392-4404 ◽  
Author(s):  
P. Århem ◽  
G. Klement ◽  
C. Blomberg
Keyword(s):  
Cortical Neuron ◽  
Neuron Model ◽  
Firing Patterns ◽  
Density Regulation ◽  
Channel Density

Effects of Input Synchrony on the Firing Rate of a Three-Conductance Cortical Neuron Model

1994 ◽  
Vol 6 (6) ◽  
pp. 1111-1126 ◽  
Author(s):  
Venkatesh N. Murthy ◽  
Eberhard E. Fetz
Keyword(s):  
Steady State ◽  
Firing Rate ◽  
Cortical Neuron ◽  
Critical Value ◽  
Time Varying ◽  
Synaptic Inputs ◽  
Firing Rates ◽  
Steady State Responses ◽  
Cross Correlations ◽  
State Responses

For a model cortical neuron with three active conductances, we studied the dependence of the firing rate on the degree of synchrony in its synaptic inputs. The effect of synchrony was determined as a function of three parameters: number of inputs, average input frequency, and the synaptic strength (maximal unitary conductance change). Synchrony alone could increase the cell's firing rate when the product of these three parameters was below a critical value. But for higher values of the three parameters, synchrony could reduce firing rate. Instantaneous responses to time-varying input firing rates were close to predictions from steady-state responses when input synchrony was high, but fell below steady-state responses when input synchrony was low. Effectiveness of synaptic transmission, measured by the peak area of cross-correlations between input and output spikes, increased with increasing synchrony.

Fitting of adaptive neuron model to electrophysiological recordings using particle swarm optimization algorithm

2017 ◽  
Vol 31 (05) ◽  
pp. 1750023 ◽  
Author(s):  
Bonan Shan ◽  
Jiang Wang ◽  
Lvxia Zhang ◽  
Bin Deng ◽  
Xile Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Firing Rate ◽  
Neuron Model ◽  
Estimation Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Electrophysiological Recordings ◽  
Spiking Neuron Model

In order to fit neural model’s spiking features to electrophysiological recordings, in this paper, a fitting framework based on particle swarm optimization (PSO) algorithm is proposed to estimate the model parameters in an augmented multi-timescale adaptive threshold (AugMAT) model. PSO algorithm is an advanced evolutionary calculation method based on iteration. Selecting a reasonable criterion function will ensure the effectiveness of PSO algorithm. In this work, firing rate information is used as the main spiking feature and the estimation error of firing rate is selected as the criterion for fitting. A series of simulations are presented to verify the performance of the framework. The first step is model validation; an artificial training data is introduced to test the fitting procedure. Then we talk about the suitable PSO parameters, which exhibit adequate compromise between speediness and accuracy. Lastly, this framework is used to fit the electrophysiological recordings, after three adjustment steps, the features of experimental data are translated into realistic spiking neuron model.

The effects of induction electric field on sensitivity of firing rate in a single-compartment neuron model

2013 ◽  
Vol 99 ◽  
pp. 555-563 ◽  
Author(s):  
Xiu Wang ◽  
Jiang Wang ◽  
Bin Deng ◽  
Xi-le Wei ◽  
Hui-Yan Li
Keyword(s):  
Electric Field ◽  
Firing Rate ◽  
Neuron Model ◽  
Single Compartment
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