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A neuronal circuit simulator for non Monte Carlo analysis of neuronal noise (Kilinc & Demir 2018)
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Model Information
Model File
Accession:
239146
cirsiumNeuron is a neuronal circuit simulator that can directly and efficiently compute characterizations of stochastic behavior, i.e., noise, for multi-neuron circuits. In cirsiumNeuron, we utilize a general modeling framework for biological neuronal circuits which systematically captures the nonstationary stochastic behavior of the ion channels and the synaptic processes. In this framework, we employ fine-grained, discrete-state, continuous-time Markov Chain (MC) models of both ion channels and synaptic processes in a unified manner. Our modeling framework can automatically generate the corresponding coarse-grained, continuous-state, continuous-time Stochastic Differential Equation (SDE) models. In addition, for the stochastic characterization of neuronal variability and noise, we have implemented semi-analytical, non Monte Carlo analysis techniques that work both in time and frequency domains, which were previously developed for analog electronic circuits. In these semi-analytical noise evaluation schemes, (differential) equations that directly govern probabilistic characterizations in the form of correlation functions (time domain) or spectral densities (frequency domain) are first derived analytically, and then solved numerically. These semi-analytical noise analysis techniques correctly and accurately capture the second order statistics (mean, variance, autocorrelation, and power spectral density) of the underlying neuronal processes as compared with Monte Carlo simulations.
Reference:
1 .
Kilinc D, Demir A (2018) Spike timing precision of neuronal circuits.
J Comput Neurosci
44
:341-362
[
PubMed
]
2 .
Kilinc D, Demir A (2017) Noise in Neuronal and Electronic Circuits: A General Modeling Framework and Non-Monte Carlo Simulation Techniques.
IEEE Trans Biomed Circuits Syst
11
:958-974
[
PubMed
]
3 .
Kilinc D,Demir A (2015) Simulation of noise in neurons and neuronal circuits
Proceedings of the IEEE/ACM international conference on computer-aided design (ICCAD)
:589-596
4 .
Mahmutoglu AG, Demir A (2013) CIRSIUM: A circuit simulator in MATLABĀ® with object oriented design
Proceedings of the 2013 9th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME)
:173-176
Citations
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Model Information
(Click on a link to find other models with that property)
Model Type:
Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Hodgkin-Huxley neuron;
Channel(s):
I Potassium;
I Sodium;
I Cl, leak;
Gap Junctions:
Receptor(s):
AMPA;
GabaA;
Gene(s):
Transmitter(s):
Simulation Environment:
MATLAB;
cirsiumNeuron;
Model Concept(s):
Markov-type model;
Stochastic simulation;
Reaction-diffusion;
Synaptic noise;
Synaptic Integration;
Implementer(s):
Kilinc, Deniz [dkilinc at ku.edu.tr];
Mahmutoglu, A. Gokcen [amahmutoglu at ku.edu.tr];
Demir, Alper [aldemir at ku.edu.tr];
Search NeuronDB
for information about:
GabaA
;
AMPA
;
I Sodium
;
I Potassium
;
I Cl, leak
;
/
simulation_scripts
run_scripts
Sec_4_1_feedback_inhibition_steady_state_method.m
Sec_4_1_feedback_inhibition_transient_method.m
Sec_4_3_energy_consumption_analysis.m
Sec_4_4_feedback_inhibition_transient_method.m
Sec_5_synaptic_integ_heterogeneous_in_transient_method.m
Sec_5_synaptic_integ_identical_in_transient_method.m
Sec_6_1_single_neuron_transient_method.m
Sec_6_1_synaptic_coupling_E_network_transient_method.m
Sec_6_1_synaptic_coupling_EI_network_transient_method.m
Sec_6_1_synaptic_coupling_I_network_transient_method.m
Sec_6_2_single_neuron_steady_state_method.m
Sec_6_2_synaptic_coupling_E_network_steady_state_method.m
Sec_6_2_synaptic_coupling_EI_network_steady_state_method.m
Sec_6_2_synaptic_coupling_I_network_steady_state_method.m
Sec_6_3_synaptic_coupling_het_E_network_steady_state_method.m
Sec_6_3_synaptic_coupling_het_EI_network_steady_state_method.m
Sec_6_3_synaptic_coupling_het_I_network_steady_state_method.m
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