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The Spike Response Model: A Framework to Predict Neuronal Spike Trains

Publications associées (38)

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Statistical tests for neural population models - The multivariate time rescaling theorem

Joao Emanuel Felipe Gerhard

The coordinated, collective spiking activity of neuronal populations encodes and processes information. One approach towards understanding such population based computation is to fit statistical models to simultaneously recorded spike trains and use these ...

2010

NEURON and Python

Eilif Benjamin Muller, Michael Lee Hines

The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engine ...

Frontiers Research Foundation2009

Simple Neuron Models and Adaptation

Skander Mensi

Predicting activity of single neuron is an important part of the computational neuroscience and a great challenge. Several mathematical models exist, from the simple (one compartment and few parameters, like the SRM or the IF-type models), to the more comp ...

2008

The response of cortical neurons to in vivo-like input current: theory and experiment

Michele Giugliano, Walter Senn

The study of several aspects of the collective dynamics of interacting neurons can be highly simplified if one assumes that the statistics of the synaptic input is the same for a large population of similarly behaving neurons (mean field approach). In part ...

2008

Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces

Wulfram Gerstner, Carl Petersen, Laurent Badel, Sandrine Lefort, Romain Brette

Neuronal response properties are typically probed by intracellular measurements of current-voltage (I-V) relationships during application of current or voltage steps. Here we demonstrate the measurement of a novel I-V curve measured while the neuron exhibi ...

American Physiological Society2008

Predicting single spikes and spike patterns with the Hindmarsh–Rose model

Martin Hasler

Most simple neuron models are only able to model traditional spiking behavior. As physiologists discover and classify different electrical phenotypes, computational neuroscientists become interested in using simple phenomenological models that can exhibit ...

Springer-Verlag2008

PhotoMEA: An opto-electronic biosensor for monitoring in vitro neuronal network activity

Diego Ghezzi

PhotoMEA is a biosensor useful for the analysis of an in vitro neuronal network, fully based on optical methods. Its function is based on the stimulation of neurons with caged glutamate and the recording of neuronal activity by Voltage-Sensitive fluorescen ...

Elsevier2007

Evolution of Spiking Neural Circuits in Autonomous Mobile Robots

Dario Floreano, Jean-Christophe Zufferey, Claudio Mattiussi

We describe evolution of spiking neural architectures to control navigation of autonomous mobile robots. Experimental results with simple fitness functions indicate that evolution can rapidly generate spiking circuits capable of navigating in textured envi ...

2006

Neuron models of the generic bifurcation type

Minimal nonlinear dynamic neuron models of the generic bifurcation type may provide the middle way between the detailed models favored by experimentalists and the simplified threshold and rate model of computational neuroscientists. This thesis investigate ...

EPFL2006

Effective minimal threshold models of neuronal activity

Renaud Jolivet

This work investigates the capacity of Integrate-and-Fire-type (I&F-type) models to quantitatively predict spike trains of real neurons in various laboratory and in vivo-like settings. A step-by-step methodology is developed to build an equivalent effectiv ...

EPFL2005