Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Fisher's Exact Test

Fisher's Exact Test

Fisher's exact test is a statistical significance test widely used to analyze 2x2 contingency tables, particularly in situations where sample sizes are small. Unlike the chi-squared test, which approximates P-values and assumes minimum expected frequencies of at least five in each cell, Fisher's exact test calculates the exact probability (P-value) of observing the data or more extreme results under the null hypothesis. This feature makes it especially valuable when the assumptions of the...

Determination of Expected Frequency

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

Signal Sequences and Sorting Receptors

Signal Sequences and Sorting Receptors

Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Energy landscapes and synergetic state transitions in frustrated Stuart-Landau oscillator networks: a homotopy continuation study.

Frontiers in network physiology·2026

Same author

CauFinder: Steering Cell-State and Phenotype Transitions by Causal Disentanglement Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Beyond variance: sensitivity-based dimensions in brain networks underlie individual differences in cognitive ability.

ArXiv·2026

Same author

Modeling flexible behavior with remapping-based hippocampal sequence learning.

eLife·2026

Same author

Identifying the optimal rapid antigen test for screening and determining the end of isolation: A modeling study.

PLoS computational biology·2026

Same author

Force Learning in Balanced Cortical E-I Networks.

Neural computation·2026

Search research articles

Related Experiment Video

Updated: Jul 19, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Fisher information for spike-based population decoding.

Taro Toyoizumi¹, Kazuyuki Aihara, Shun-ichi Amari

¹Institute of Industrial Science, University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo 113-8656, Japan. taro.toyoizumi@brain.riken.jp

Physical Review Letters

|October 10, 2006

Summary

This study analyzes how model neurons encode information using spike timing and synaptic connections. We found optimal network structures that efficiently process complex inputs, highlighting the importance of spike timing for neural coding.

More Related Videos

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish

Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish

Published on: November 26, 2019

Related Experiment Videos

Last Updated: Jul 19, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish

Wireless Electrophysiological Recording of Neurons by Movable Tetrodes in Freely Swimming Fish

Published on: November 26, 2019

Area of Science:

Computational Neuroscience
Neural Coding
Information Theory

Background:

Neurons process information through dynamic inputs and spike interactions.
Fisher information quantifies the information content in neural signals.
Spike timing is crucial for efficient neural information processing.

Purpose of the Study:

To evaluate Fisher information in spiking neural populations.
To determine the impact of spike timing and synaptic connections on information coding.
To derive optimal network connectivity for processing specific inputs.

Main Methods:

Analytical calculation of spike-based Fisher information.
Quantification of information loss when ignoring spike timing.
Modeling of recurrent synaptic connections and their effect on Fisher information.
Derivation of optimal connectivity for spatiotemporal inputs.

Main Results:

A simple analytical form for spike-based Fisher information with independent threshold noise.
Quantification of information loss due to neglecting spike timing.
Identification of optimal recurrent connectivity as local excitation and global inhibition.
Demonstration that optimal connections are input-feature dependent.

Conclusions:

Spike timing significantly contributes to the information capacity of neural populations.
Network structure, specifically recurrent connectivity, can be optimized for efficient coding of specific input features.
The findings provide insights into how neural systems achieve robust information processing.