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Related Concept Videos

Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
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.
Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
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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Long-term Potentiation01:25

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when presynaptic neurons...
Long-term Potentiation01:35

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.

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Related Experiment Video

Updated: May 8, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

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

Published on: March 25, 2014

Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule.

Michael Beyeler1, Nikil D Dutt, Jeffrey L Krichmar

  • 1Department of Computer Science, University of California Irvine, Irvine, CA 92697-3435, United States.

Neural Networks : the Official Journal of the International Neural Network Society
|September 3, 2013
PubMed
Summary

This study introduces a large-scale spiking neural network (SNN) model for real-time visual classification. The neurobiologically inspired network achieves high accuracy and predicts human reaction times, advancing artificial intelligence.

Keywords:
Decision-makingObject recognitionSTDPSpiking neural networkSupervised learningSynaptic dynamics

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Neuromorphic Engineering

Background:

  • Current artificial neural networks often lack neurobiological plausibility in learning and decision-making.
  • Efficient visual scene perception and understanding remain key research areas in neuroscience.

Purpose of the Study:

  • To develop a large-scale, neurobiologically plausible spiking neural network (SNN) model for real-time visual classification.
  • To integrate low-level memory encoding with higher-level decision processes for enhanced visual recognition.
  • To validate the model's performance against human psychophysical data.

Main Methods:

  • Implemented a hierarchical SNN with Izhikevich neurons and conductance-based synapses for realistic neuronal dynamics.
  • Incorporated spike-timing-dependent plasticity (STDP) with synaptic dynamics for memory encoding.
  • Utilized an accumulator model for memory retrieval and categorization, running on a GPU-supported SNN simulator.

Main Results:

  • The 71,026-neuron network achieved 92% accuracy on the MNIST dataset.
  • The model demonstrated real-time performance on a single GPU.
  • Successfully predicted reaction times from psychophysical experiments, validating its cognitive plausibility.

Conclusions:

  • The developed SNN model offers a neurobiologically faithful approach to visual classification and decision-making.
  • The model's scalability and performance suggest potential for efficient neuromorphic implementations.
  • This research bridges the gap between computational models and biological visual processing.