Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Variability of Spike Trains

Description

This lecture explores the variability of spike trains in computational neuroscience, covering experiments, sources of variability, rate code definitions, stochastic spike arrival, and firing. It also delves into variability in vivo and in vitro, discussing membrane potential fluctuations and spike time variations.

In MOOC

Neuronal Dynamics - Computational Neuroscience of Single Neurons

Official source

Related concepts (25)

Neural coding

Neural coding (or neural representation) is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among the electrical activity of the neurons in the ensemble. Based on the theory that sensory and other information is represented in the brain by networks of neurons, it is thought that neurons can encode both digital and analog information.

Neural oscillation

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons.

Spiking neural network

Artificial neural network Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. In addition to neuronal and synaptic state, SNNs incorporate the concept of time into their operating model. The idea is that neurons in the SNN do not transmit information at each propagation cycle (as it happens with typical multi-layer perceptron networks), but rather transmit information only when a membrane potential—an intrinsic quality of the neuron related to its membrane electrical charge—reaches a specific value, called the threshold.

Membrane potential

Membrane potential (also transmembrane potential or membrane voltage) is the difference in electric potential between the interior and the exterior of a biological cell. That is, there is a difference in the energy required for electric charges to move from the internal to exterior cellular environments and vice versa, as long as there is no acquisition of kinetic energy or the production of radiation. The concentration gradients of the charges directly determine this energy requirement.

Resting potential

A relatively static membrane potential which is usually referred to as the ground value for trans-membrane voltage. The relatively static membrane potential of quiescent cells is called the resting membrane potential (or resting voltage), as opposed to the specific dynamic electrochemical phenomena called action potential and graded membrane potential. Apart from the latter two, which occur in excitable cells (neurons, muscles, and some secretory cells in glands), membrane voltage in the majority of non-excitable cells can also undergo changes in response to environmental or intracellular stimuli.

Related lectures (36)

Stochastic spike arrival MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores stochastic spike arrival in single neurons and the impact of network noise.

Stochastic spike firing in integrate-and-fire models MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores stochastic spike firing in integrate-and-fire models and its impact on neural coding.

Membrane potential fluctuations MOOC: Neuronal Dynamics - Computational Neuroscience of Single Neurons

Explores stochastic spike arrival and membrane potential fluctuations in single neurons.

Neurophysiological Data Analysis

Covers neurophysiological data analysis, including AP detection, firing rate computation, and spectral analysis, with a focus on predicting cell classes.

Neural Signals and Signal Processing: Modeling and Simulation