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Related Experiment Video

Updated: Jun 28, 2026

Microdissection of Mouse Brain into Functionally and Anatomically Different Regions

Microdissection of Mouse Brain into Functionally and Anatomically Different Regions

Published on: February 15, 2021

A structural and a functional aspect of stable information processing by the brain.

Kaushik Kumar Majumdar¹

¹Institute of Mathematical Sciences, CIT Campus, Taramani, Chennai, 600113, India, kaushik@imsc.res.in.

Cognitive Neurodynamics

|November 13, 2008

Summary

This study proposes a neural circuit model with cyclic sub-circuits that amplify signals and process information. The brain interactively selects behaviors by minimizing a potential function, representing neural activity as vectors in multidimensional space.

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Last Updated: Jun 28, 2026

Microdissection of Mouse Brain into Functionally and Anatomically Different Regions

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Published on: February 15, 2021

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

The brain exhibits remarkable robustness in processing information, even in noisy environments.
Neural circuits are fundamental to brain function, yet their organizational principles remain incompletely understood.

Purpose of the Study:

To propose a novel model of neural circuits composed of cyclic sub-circuits.
To elucidate the principles of information processing, signal amplification, and behavior selection within these circuits.
To mathematically describe the dynamics governing neural circuit function and stability.

Main Methods:

Mathematical modeling of neural circuits with feedforward and feedback paths (big loops).
Signal amplification analysis within smaller cyclic sub-circuits.
Exact synchronization detection for identifying neuronal pairs.
Information extraction from neural spike trains using Fourier transforms and vector space representation.
Application of a Coulomb force-like expression and potential function minimization to model circuit dynamics.

Main Results:

Demonstrated how cyclic sub-circuits can amplify signals.
Showed that big loops process information via contrast and amplification principles.
Represented neural circuit function as clusters of points in a multidimensional vector space.
Proposed that behavior selection involves interactive choices based on memory and emotion, governed by a force-like expression.

Conclusions:

The proposed cyclic neural circuit model provides a framework for understanding information processing and behavior generation.
Mathematical analysis reveals mechanisms for signal amplification and dynamic stability within neural networks.
Neural circuit behavior can be represented and understood through vector space dynamics and potential function minimization.