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Related Concept Videos

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional signals...

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Related Experiment Video

Updated: May 12, 2026

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
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Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

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Hierarchical random cellular neural networks for system-level brain-like signal processing.

Robert Kozma1, Marko Puljic

  • 1Department of Mathematical Sciences, University of Memphis, Memphis, TN 38152, USA. rkozma@memphis.edu

Neural Networks : the Official Journal of the International Neural Network Society
|April 4, 2013
PubMed
Summary

This study introduces neuropercolation, a new brain model using random cellular automata, to explain brain dynamics and cognitive processes. It highlights phase transitions as key to higher cognition and suggests VLSI platforms for implementation.

Keywords:
Freeman K-setsMemristorNeurodynamicsNeuropercolationPerceptual information processingPhase transitionRandom cellular automataSynchronization

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Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array
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Published on: March 8, 2024

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

Published on: March 2, 2015

Area of Science:

  • Computational Neuroscience
  • Theoretical Neuroscience
  • Cognitive Science

Background:

  • Brain function and cognition are often modeled using dynamic systems theory, representing neural activity as trajectories in high-dimensional state spaces.
  • Traditional models, often based on differential equations, face challenges in describing spatio-temporal discontinuities inherent in neural processing.

Purpose of the Study:

  • To introduce a novel brain model, neuropercolation, utilizing a hierarchy of random cellular automata.
  • To leverage this model for describing spatio-temporal dynamics of the cortex and its phase transitions.
  • To explore the implications of these phase transitions for higher cognition and awareness.

Main Methods:

  • Development of a hierarchical random cellular automata framework.
  • Application of Monte Carlo simulations, utilizing parallel computing, to analyze the model's dynamics.
  • Comparison of the neuropercolation model with traditional differential equation-based approaches.

Main Results:

  • The neuropercolation model effectively describes spatio-temporal discontinuities as phase transitions.
  • Phase transitions in the model are linked to critical conditions indicative of higher cognitive functions.
  • Simulations underscore the potential of very large-scale integration (VLSI) and analog computing platforms for implementing such brain models.

Conclusions:

  • Neuropercolation offers a powerful alternative to differential equation models for brain dynamics, particularly for discontinuous phenomena.
  • Phase transitions represent critical states associated with complex cognitive functions and consciousness.
  • Efficient computational implementation of neuropercolation may be achievable using advanced hardware like VLSI and analog systems.