A Neuronal Circuit Simulator for non Monte Carlo Analysis of Neuronal Noise (Kilinc et al)

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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.
1 . Kilinc D, Demir A (2018) Spike Timing Precision of Neuronal Circuits Journal of Computational Neuroscience
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;
Simulation Environment: MATLAB;
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;
cirsiumNeuron is a neuronal circuit simulator that can directly and 
efficiently compute characterizations of stochastic behavior, i.e., 
noise, for multi-neuron circuits. It is still in its early stages of 
development, so beware of bugs!

cirsiumNeuron requires the SUNDIALS suite and the MATLAB interface of 
SUNDIALS called sundialsTB. In order to install SUNDIALS and sundialsTB, 
please download SUNDIALS (version 2.6.0), which is available 
at http://computation.llnl.gov/casc/sundials/main.html, and follow 
the installation instructions given at the above link. 
In order to install sundialsTB, within MATLAB, navigate to the sundialsTB 
subdirectory and run  the script called "install_STB.m". 
You will need a C compiler that is recognized by MATLAB in order to 
generate .mex files. The installed sundialsTB and the source code 
for cirsiumNeuron should be added to the MATLAB path before attempting 
to use cirsiumNeuron.

Please type "help circuit" in MATLAB for a brief tutorial on how to 
construct a simple circuit with cirsiumNeuron.

Please see the papers below for the details of the formulations and 
techniques implemented in cirsiumNeuron:

Kilinc D, Demir A (2017) Noise in neuronal and electronic circuits: 
A general modeling framework and non-Monte Carlo simulation techniques. 
IEEE Transactions on Biomedical Circuits and Systems 11(4):958–974 

Kilinc D, Demir A (2015) Simulation of noise in neurons and neuronal
circuits. In Proceedings of the IEEE/ACM International Conference on
Computer-Aided Design (ICCAD), IEEE, pp. 589–596.

Mahmutoglu A. G., Demir A. (2013). CIRSIUM: A circuit simulator in MATLAB 
with object oriented design. In 2013 9th Conference on Ph. D. Research 
in Microelectronics and Electronics (PRIME), IEEE, pp. 173-176.

The directory "run_scripts" contains the scripts that were used to 
generate the results reported in the manuscript below

Title: Spike Timing Precision of Neuronal Circuits
Authors: Deniz Kilinc, Alper Demir
accepted for publication by the Journal of Computational Neuroscience
DOI: 10.1007/s10827-018-0682-z
Manuscript ID JCNS-D-17-00083R2
For questions please contact Deniz Kilinc: dkilinc@ku.edu.tr

Copyright 2018 by Koc University and Deniz Kilinc, A. Gokcen Mahmutoglu, Alper Demir 
All Rights Reserved 

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