Results 1  10
of
37
Generalized IntegrateandFire Models of Neuronal Activity Approximate Spike Trains of a . . .
"... We demonstrate that singlevariable integrateandfire models can quantitatively capture the dynamics of a physiologicallydetailed model for fastspiking cortical neurons. Through a systematic set of approximations, we reduce the conductance based model to two variants of integrateandfire mode ..."
Abstract

Cited by 58 (14 self)
 Add to MetaCart
We demonstrate that singlevariable integrateandfire models can quantitatively capture the dynamics of a physiologicallydetailed model for fastspiking cortical neurons. Through a systematic set of approximations, we reduce the conductance based model to two variants of integrateandfire models. In the first variant (nonlinear integrateandfire model), parameters depend on the instantaneous membrane potential whereas in the second variant, they depend on the time elapsed since the last spike (Spike Response Model). The direct reduction links features of the simple models to biophysical features of the full conductance based model. To quantitatively
Developmental simulation with cellerator
 In Proc. of the Second International Conference on Systems Biology (ICSB
, 2001
"... We describe how to perform developmental simulations with Cellerator. Biochemical reactions, specified in Cellerator with a compact, arrowbased notation, are automatically translated into the appropriate ordinary differential equations. These reactions can be combined into modules, leading to a nat ..."
Abstract

Cited by 15 (3 self)
 Add to MetaCart
We describe how to perform developmental simulations with Cellerator. Biochemical reactions, specified in Cellerator with a compact, arrowbased notation, are automatically translated into the appropriate ordinary differential equations. These reactions can be combined into modules, leading to a natural graphbased hierarchical implementation. We demonstrate how the paradigm of organismsasgraphs can represent the basic features of developing tissue, and propose a variablestructure graphbased algorithm to describe simple developmental processes. In particular, we show how such a variablestructure system (VSS) can be implemented using a prepackaged fixedstructure differential equation solver. 1.
Predicting Spike Times of a Detailed ConductanceBased Neuron Model Driven by Stochastic Spike Arrival
, 2004
"... Reduced models of neuronal activity such as IntegrateandFire models allow a description of neuronal dynamics in simple, intuitive terms and are easy to simulate numerically. We present a method to fit an Integrateand Firetype model of neuronal activity, namely a modified version of the Spike Res ..."
Abstract

Cited by 9 (3 self)
 Add to MetaCart
Reduced models of neuronal activity such as IntegrateandFire models allow a description of neuronal dynamics in simple, intuitive terms and are easy to simulate numerically. We present a method to fit an Integrateand Firetype model of neuronal activity, namely a modified version of the Spike Response Model, to a detailed HodgkinHuxleytype neuron model driven by stochastic spike arrival. In the HogkinHuxley model, spike arrival at the synapse is modeled by a change of synaptic conductance. For such conductance spike input, more than 70% of the postsynaptic action potentials can be predicted with the correct timing by the IntegrateandFire type model. The modified Spike Response Model is based upon a linearized theory of conductancedriven IntegrateandFire neuron. Keywords: conductance injection  IntegrateandFire model  stochastic input  mapping techniques  predictive power. PACS: 87.10.+e  87.19.La  87.17.Nn  87.17.Aa. 1
Test of spike sorting algorithms on the basis of simulated network data. Neurocomputing
 Eds.), Fifth German Workshop on Artificial Life:Abstracting and
, 2002
"... data ..."
Large neural simulations on large parallel computers
 International Journal of Bioelectromagnetism
, 2005
"... Abstract—Simulations of biologically realistic neurons in large densely connected networks pose many problems to application programmers, particularly on distributed memory computers. We discuss simulations of hundreds of thousands to millions of cells in a model of neocortex in the context of new c ..."
Abstract

Cited by 5 (2 self)
 Add to MetaCart
Abstract—Simulations of biologically realistic neurons in large densely connected networks pose many problems to application programmers, particularly on distributed memory computers. We discuss simulations of hundreds of thousands to millions of cells in a model of neocortex in the context of new computing platforms with many tens of thousands to hundreds of thousands of processing elements. We are developing a performance model for this simulation so that we can gauge its performance on these platforms in terms of memory usage, time to setup and execute the simulation, and to estimate the practical limits to the size and simulation timescale available to us in our simulation experiments. Recent results from runs on a BlueGene/L computer, which could ultimately scale to over one hundred thousand processors, are described. Keywords—Neural networks, neuron modeling, epilepsy, parallel computing, performance scaling I.
SimSPiNN  A Simulator for SpikeProcessing Neural Networks
 In Proceedings of the 15th IMACS World Congress on Scientific Computation, Modelling amd Applied Mathematics
"... Introduction Substantial evidence indicates that the time structure of neuronal spike trains is relevant in neuronal signal processing [1]. Furthermore, experimental results [2] [3] together with theoretical studies [4] [5] suggest that temporal correlation of activity might be used by the brain as ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
Introduction Substantial evidence indicates that the time structure of neuronal spike trains is relevant in neuronal signal processing [1]. Furthermore, experimental results [2] [3] together with theoretical studies [4] [5] suggest that temporal correlation of activity might be used by the brain as a code to bind features to an object and to segregate one object from others. This mechanism could also be useful for machine vi sion, where robust scene segmentation is still a difficult and intricate problem in a real world environment. In order to apply this mechanism we need basic processing elements with temporal behavior, i.e. spiking neurons. Unfortunately, available neurosimulators are not suited for the simulation of spikeprocessing neural networks. The underlying neuronmodel either does not contain temporal behavior (e.g. SNNS [6] or PDP++ [7]) or the model is too detailed regarding spiking neurons (e.g. GENESIS [8]). So the simulation of largescale spikeprocessing neural n
Developmental Evolution of Dendritic Morphology in a MultiCompartmental Neuron Model
, 1999
"... Through the use of a multicompartmental neuron simulation, Mainen and Sejnowski demonstrated that spike generation in neurons is a function of their dendritic structure [1]. In this paper we investigate the determination of dendritic morphology given a desired set of spike traces. A genetic algorit ..."
Abstract

Cited by 3 (3 self)
 Add to MetaCart
Through the use of a multicompartmental neuron simulation, Mainen and Sejnowski demonstrated that spike generation in neurons is a function of their dendritic structure [1]. In this paper we investigate the determination of dendritic morphology given a desired set of spike traces. A genetic algorithm is used to identify optimal parameters for a developmental model which simulates the growth of 3dimensional dendrites. For two classes of neurons with dierent spiking behaviour, the developmental evolutionary process discovers ranges of viable dendritic morphologies which satisfactorally match the desired spike traces. 1 Compartmental Neuron Simulations The eld of computational neuroscience demonstrates that by reducing the levels of complexity found in biology to computational models, biologically defensible results can be achieved [2]. Detailed compartmental models of neurons for example, can be accurately simulated using a number of software packages [3, 4]. Using the NEURON sim...
Computing Functions with Spiking Neurons in Temporal Coding
 Proc. of the Int. WorkConference on Artificial and Natural Neural Networks IWANN'97, Lecture Notes in Computer Science
, 1997
"... . For fast neural computations within the brain it is very likely that the timing of single firing events is relevant. Recently Maass has shown that under certain weak assumptions functions can be computed in temporal coding by leaky integrateandfire neurons. Here we demonstrate with the help of c ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
. For fast neural computations within the brain it is very likely that the timing of single firing events is relevant. Recently Maass has shown that under certain weak assumptions functions can be computed in temporal coding by leaky integrateandfire neurons. Here we demonstrate with the help of computer simulations using GENESIS that biologically more realistic neurons can compute linear functions in a natural and straightforward way based on the basic principles of the construction given by Maass. One only has to assume that a neuron receives all its inputs in a time intervall of approximately the length of the rising segment of its excitatory postsynaptic potentials. We also show that under certain assumptions there exists within this construction some type of activation function being computed by such neurons, which allows the fast computation of arbitrary continuous bounded functions. 1 Introduction There exist several models for explaining how the brain can perform computation...
The Spike Response Model
, 1999
"... A description of neuronal activity on the level of ion channels, as in the HodgkinHuxley model, leads to a set of coupled nonlinear differential equations which are difficult to analyze. In this paper, we present a conceptual framework for a reduction of the nonlinear spike dynamics to a threshold ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
A description of neuronal activity on the level of ion channels, as in the HodgkinHuxley model, leads to a set of coupled nonlinear differential equations which are difficult to analyze. In this paper, we present a conceptual framework for a reduction of the nonlinear spike dynamics to a threshold process. Spikes occur if the membrane potential u(t) reaches a threshold #. The voltage response to spike input is described by the postsynaptic potential ffl. Postsynaptic potentials of several input spikes are added linearly until u reaches #. The output pulse itself and the reset/refractory period which follow the pulse are described by a function j. Since ffl and j can be interpreted as response kernels, the resulting model is called the Spike Response Model (SRM). After a short review of the HodgkinHuxley model we show that (i) HodgkinHuxley dynamics with timedependent input can be reproduced to a high degree of accuracy by the SRM; (ii) the simple integrateandfire neuron is a spe...