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Related Concept Videos

Integration of Synaptic Events01:28

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Graded Potential01:19

Graded Potential

Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or calcium...

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Related Experiment Videos

Temporal integration by stochastic recurrent network dynamics with bimodal neurons.

Hiroshi Okamoto1, Yoshikazu Isomura, Masahiko Takada

  • 1Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, Hirosawa 2-1, Wako, Saitama 351-0198, Japan.

Journal of Neurophysiology
|March 30, 2007
PubMed
Summary

This study introduces a neural network model that generates graded neuronal activity, crucial for cognitive processes. The model explains how neuronal firing patterns, observed in monkeys, arise from synaptic inputs and afterpotentials.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Temporal integration of information is vital for cognition.
  • Graded neuronal activity in cortical areas, like the prefrontal cortex, underlies this process.
  • Existing models often simplify the complex dynamics of neuronal firing during cognitive tasks.

Purpose of the Study:

  • To propose a novel neural network model for generating graded neuronal activity.
  • To investigate the mechanisms underlying sustained and graded firing patterns in neurons.
  • To validate the model by comparing its predictions with experimental data from primate studies.

Main Methods:

  • Developed a recurrent neural network with fast excitatory synapses and noisy background inputs.
  • Incorporated a prolonged afterdepolarizing potential in individual neurons.
  • Simulated neuronal responses to external input and analyzed population activity dynamics.
  • Recorded and analyzed anterior cingulate cortex neuronal activity in monkeys performing a Go/No-go task.

Main Results:

  • The model successfully produced graded (climbing and descending) neuronal activity with a nearly constant slope.
  • Bimodal rate changes in individual neurons were observed, sustained by regenerated afterdepolarizing potentials.
  • Stochastic noise and reverberating synaptic inputs were shown to organize bimodal changes into graded population activity.
  • Experimental data from monkey anterior cingulate cortex neurons showed bimodal activity patterns and trial-to-trial variability consistent with model predictions.

Conclusions:

  • The proposed neural network model provides a plausible mechanism for generating graded neuronal activity observed in cognitive tasks.
  • Afterdepolarizing potentials, combined with tuned synaptic and noise parameters, can explain sustained and graded neuronal firing.
  • The findings offer insights into the neural basis of temporal information processing in the brain.