Simulation Environments for Neuroscience

Simulation Environments for Neuroscience

Presented by Ali Nadalizadeh – IPM – Summer 2009

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Simulation Environments

NEURON GENESIS NEST NeoCortical Simulator (NCS) Circuit Simulator (Csim) SPLIT XPPAUT (Discussed before)

NEURON - Introduction

Historically, NEURON’s primary domain of application was in simulating empirically−based models of biological neurons with extended geometry and biophysical mechanisms that are spatially nonuniform and kinetically complex. (COBA Models)

COBA – Stands for Complex Branched Anatomy

NEURON - Introduction

COBA Models may include : Extracellular potential near the membrane Multiple channel types Inhomogeneous channel distribution Ionic accumulation and diffusion Second messengers

NEURON can now simulate artificial models too, like integrate and fire or any combination of COBA and Artificial networks.

NEURON – How it works ?

We'll approximate the continuous system of neuron into a discrete system in both time and space

Every cell is constructed using connected Sections Every section is an unbranched, continuous cable

whose anatomical and biophysical properties can vary continuously along its length

Note that Sections differ from Compartments Neuron can do both Clock-Driven and Event Based

simulations

NEURON - Other Features

Different Integrate & Fire neuron choices Different Integration methods that will result in a

tradeoff between speed and accuracy. Ability to define new biophysical mechanism.

NMODL syntax → Compiled to C → Compiled to Native Machine code to be used by NEURON

Runs under Windows,MacOSX,Linux Freely available plus extensive documentation

NEURON – Creating and using models

Models should be written in an interpreted language named HOC

NMODL language for new biophysic mechanisms

A powerful GUI for conveniently building and using models

ModelDB : Online model collection that are ready to use.

NEURON - Parallel Computing

NEURON Supports 3 types of parallel computing Multiple Simulations on multiple processors Distributed Network models with gap junctions Distributed models of individual cells

NEURON Uses MPI (Message Passing Interface) for parallel computing

GENESIS - Introduction

Stands for General Neural Simulation System was given its name because it was designed, at the

outset, be an extensible general simulation system for the realistic modeling of neural and biological systems (Bower and Beeman 1998)

Typical GENESIS Models are multicompartment models with HH-type voltage/calcium dependent conductances

Parallel Genesis (PGENESIS) is an extension for it, which supports MPI and PVM for parallel computing.

GENESIS – How it works ?

Object-Oriented simulation system Message Passing Self-Knowledge (variables and actions)

Neuron Component Examples Compartments Variable conductance ion channels Synaptic connections to other neurons

Model Neuron into compartmentsand compartments into circuits

Model Neuron into compartmentsand compartments into circuits

Purkinje Cell ModelWith 4550 Compartments and 8021 channels

GENESIS - GUI

GENESIS - GUI

NEST

Simulating neural networks of biologically realistic size and complexity

Implementing a mathematically correct simulator by novices in a few days ?

Reproducing the results of ad hoc simulations ! The NEST initiative was founded as a long term

project to address these problems. Free/Open License with Extra conditions

NEST

Easily copes with a threshold network size of 105 neurons with natural number of synapses

No GUI (Network generation is usually procedural)

NeuroML Project

A step toward making standard formats for neuron/network specifications.

Current Standard Formats MorphML (specification of neuroanatomy) ChannelML (specification of models of ion channels and

receptors) Biophysics (specification of compartmental cell models,

building on MorphML and ChannelML) NetworkML (specification of cell positions and connections in

a network.)

Common syntax of these specifications is XML

Python Integration

What is python ? PyNEURON, PyGenesis, PyNEST, PyNN Benefits

Standard Scripting Syntax Script Readability NeuroML Integration Ability to use current scientific tools (such as scipy)

References

http://www.open-mpi.org/

http://en.wikipedia.org/wiki/Parallel_Virtual_Machine

http://www.neuron.yale.edu/neuron/

http://www.wam-bamm.org/Tutorials/genesis-intro/genesis-intro.html

http://www.python.org/doc/essays/blurb.html

http://en.wikipedia.org/wiki/Purkinje_cell

Michael L. Hines, Andrew P Davison, Eilif MullerNEURON and Python

Romain Brette · Michelle Rudolph · Ted Carnevale and other friends !Simulation of networks of spiking neurons: A review of tools and strategies