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Related Concept Videos

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.
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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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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...
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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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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

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

Published on: November 12, 2019

Time-free spiking neural P systems.

Linqiang Pan1, Xiangxiang Zeng, Xingyi Zhang

  • 1Key Laboratory of Image Processing and Intelligent Control, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China. lqpan@mail.hust.edu.cn

Neural Computation
|February 9, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces time-free spiking neural P systems (SN P systems) that are robust to environmental factors. These systems yield consistent results regardless of rule execution times, proving their computational universality.

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Area of Science:

  • Computational Biology
  • Theoretical Computer Science
  • Artificial Neural Networks

Background:

  • Biological processes exhibit variable timing influenced by environmental factors.
  • Nonsynchronized spiking neural P systems (SN P systems) have arbitrary rule execution times.
  • Existing SN P systems may lack robustness against environmental variability.

Purpose of the Study:

  • To introduce and define time-free SN P systems for robust computation.
  • To investigate the computational universality of these time-free systems.
  • To analyze the impact of bounded spikes on the system's power.

Main Methods:

  • Definition of time-free SN P systems with arbitrary rule execution times.
  • Investigation of universality using extended rules capable of producing multiple spikes.
  • Analysis of system power under bounded spike conditions.

Main Results:

  • Time-free SN P systems with extended rules are computationally equivalent to register machines.
  • The universality of time-free SN P systems is established.
  • Bounding the number of spikes reduces the computational power, characterizing semilinear sets.

Conclusions:

  • Time-free SN P systems offer a robust model for computation in variable environments.
  • These systems demonstrate universality comparable to register machines.
  • Bounded spike conditions reveal limitations and specific computational capabilities of these systems.