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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...
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: Jun 7, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
08:08

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Published on: June 24, 2015

Spiking neural network simulation: memory-optimal synaptic event scheduling.

Robert D Stewart1, Kevin N Gurney

  • 1Department of Psychology, University of Sheffield, Sheffield, S10 2TP, UK. Robert.Stewart@pharm.ox.ac.uk

Journal of Computational Neuroscience
|November 4, 2010
PubMed
Summary
This summary is machine-generated.

New algorithms for spiking neural network simulations reduce memory usage by scaling with neuron count, not synapse count. This improves efficiency for large-scale network simulations with variable transmission delays.

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

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Area of Science:

  • Computational neuroscience
  • Computer science

Background:

  • Spiking neural network (SNN) simulations with variable transmission delays necessitate pre-scheduling of synaptic events.
  • Existing scheduling methods face memory demands that increase with the total number of synapses, limiting scalability.

Purpose of the Study:

  • To introduce novel scheduling algorithms for discrete and continuous event delivery in SNN simulations.
  • To reduce the memory footprint of SNN simulations by shifting the scaling factor from synapses to neurons.

Main Methods:

  • Development of new scheduling algorithms for discrete and continuous event delivery.
  • Implementation and testing of these algorithms in large-scale, benchmarking network simulations.

Main Results:

  • The novel algorithms exhibit memory requirements that scale with the number of neurons, not synapses.
  • Superior algorithmic performance was demonstrated in large-scale network simulations.

Conclusions:

  • The proposed scheduling algorithms offer a more memory-efficient approach for simulating SNNs with variable transmission delays.
  • This advancement facilitates the simulation of larger and more complex neural networks.