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Related Concept Videos

Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Spike-timing error backpropagation in theta neuron networks.

Sam McKennoch1, Thomas Voegtlin, Linda Bushnell

  • 1INRIA, Campus Scientifique, F-54506 Vandoevre-Les-Nancy, France. Samuel.McKennoch@loria.fr

Neural Computation
|May 12, 2009
PubMed
Summary

This study introduces a novel steepest gradient descent learning rule for theta neuron networks, enabling complex computations using spike timing. This method achieves comparable classification accuracy to existing models and excels at regression tasks.

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Artificial Neural Networks

Background:

  • Traditional neural network models often rely on precise postsynaptic current shapes.
  • Existing supervised learning rules like SpikeProp and Tempotron have limitations in handling complex temporal tasks.
  • Theta neurons, a type of nonlinear integrate-and-fire model, offer a promising basis for temporal coding.

Purpose of the Study:

  • To develop a novel steepest gradient descent learning rule for multilayer networks of theta neurons.
  • To demonstrate that intrinsic neuron dynamics are sufficient for consistent time coding, without needing precise postsynaptic current shapes.
  • To enable complex computations using supervised learning on spike times and temporal response properties.

Main Methods:

  • Derivation of a steepest gradient descent learning rule tailored for theta neuron networks.
  • Application of supervised learning techniques to spike timing and temporal response data.
  • Training multilayer networks of theta neurons for classification and regression tasks.

Main Results:

  • The proposed learning rule successfully trains multilayer theta neuron networks for complex computations.
  • Networks trained with this rule exhibit comparable generalization abilities to Tempotron learning for spike latency pattern classification.
  • The developed rule demonstrates capability in training networks for complex regression tasks, outperforming SpikeProp and Tempotron learning.

Conclusions:

  • Supervised learning applied to spike times and temporal properties of nonlinear integrate-and-fire neurons can achieve complex computations.
  • The new steepest gradient descent learning rule for theta neurons offers a powerful alternative for temporal processing in artificial neural networks.
  • This approach expands the applicability of neural network learning to complex regression problems previously intractable for other spike-based learning rules.