Lecture

Nonlinear Integrate-and-Fire Model

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Description

This lecture covers the reduction of a two-dimensional neuron model to one dimension, focusing on the Nonlinear Integrate-and-Fire Model. It discusses the separation of time scales, the stimulus impact, and the stability of fixed points. The Exponential Integrate-and-Fire Model is also presented, derived from Hodgkin-Huxley equations. The lecture explores the sparse activity in vivo, the prediction of spike initiation times, and subthreshold voltage in neuronal modeling.

In MOOC

Neuronal Dynamics - Computational Neuroscience of Single Neurons

This course gives an introduction to the field of theoretical and computational neuroscience with a focus on models of single neurons. Neurons encode information about stimuli in a sequence of short electrical pulses (spikes).

Official source

Related concepts (25)

Biological neuron model

Biological neuron models, also known as a spiking neuron models, are mathematical descriptions of the properties of certain cells in the nervous system that generate sharp electrical potentials across their cell membrane, roughly one millisecond in duration, called action potentials or spikes (Fig. 2). Since spikes are transmitted along the axon and synapses from the sending neuron to many other neurons, spiking neurons are considered to be a major information processing unit of the nervous system.

Dimensionality reduction

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable (hard to control or deal with).

Threshold voltage

The threshold voltage, commonly abbreviated as Vth or VGS(th), of a field-effect transistor (FET) is the minimum gate-to-source voltage (VGS) that is needed to create a conducting path between the source and drain terminals. It is an important scaling factor to maintain power efficiency. When referring to a junction field-effect transistor (JFET), the threshold voltage is often called pinch-off voltage instead.

Subthreshold conduction

Subthreshold conduction or subthreshold leakage or subthreshold drain current is the current between the source and drain of a MOSFET when the transistor is in subthreshold region, or weak-inversion region, that is, for gate-to-source voltages below the threshold voltage. The amount of subthreshold conduction in a transistor is set by its threshold voltage, which is the minimum gate voltage required to switch the device between on and off states.

Nonlinear dimensionality reduction

Nonlinear dimensionality reduction, also known as manifold learning, refers to various related techniques that aim to project high-dimensional data onto lower-dimensional latent manifolds, with the goal of either visualizing the data in the low-dimensional space, or learning the mapping (either from the high-dimensional space to the low-dimensional embedding or vice versa) itself. The techniques described below can be understood as generalizations of linear decomposition methods used for dimensionality reduction, such as singular value decomposition and principal component analysis.

Related lectures (61)

Interspike Intervals & Renewal Processes MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores interspike intervals, renewal processes, and escape noise experiments in neuronal dynamics.

In Silico Neuroscience: Ion Channels and Neuronal Models

Explores detailed modeling of ion channels and neuronal morphologies in in silico neuroscience, covering neuron classification, ion channel kinetics, and experimental observations.

Compartmental Models: Active Dendrites MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores compartmental models in computational neuroscience, focusing on active dendrites and experimental findings.

Models and data MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Covers the optimization of neuron models for coding and decoding in computational neuroscience.

Neural Signals and Signal Processing: Modeling and Simulation