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Long-term Potentiation

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.

Long-term Potentiation

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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Neuroplasticity

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.

Long-term Depression

Long-term Depression

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

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

Integration of Synaptic Events

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...

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Related Experiment Video

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3D Modeling of Dendritic Spines with Synaptic Plasticity

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Supervised learning with complex spikes and spike-timing-dependent plasticity.

Conor Houghton¹

¹Department of Computer Science, University of Bristol, Bristol, England.

|June 20, 2014

Summary

This summary is machine-generated.

Climbing fibers supervise Purkinje cell learning through complex spikes. A novel mechanism combines complex spike depolarization with spike-timing-dependent plasticity for supervised learning in the cerebellum.

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Area of Science:

Neuroscience
Computational Neuroscience
Cerebellar Function

Background:

Purkinje cells exhibit two discharge types: simple spikes and complex spikes.
Complex spikes arise from climbing fiber input and feature a burst followed by depolarization.
Climbing fiber input is hypothesized to play a role in supervising Purkinje cell learning.

Purpose of the Study:

To propose a mechanism by which climbing fiber input supervises learning in Purkinje cells.
To investigate the role of complex spike-induced depolarization in synaptic plasticity.
To illustrate this mechanism using computational simulation.

Main Methods:

Utilizing a computational model of Purkinje cell function.
Incorporating a simple spike-timing-dependent plasticity rule.
Simulating the interaction between climbing fiber input and Purkinje cell activity.

Main Results:

The proposed mechanism demonstrates successful supervision of learning by climbing fiber input.
The period of depolarization following a complex spike is crucial for this supervised learning.
Spike-timing-dependent plasticity rules are compatible with climbing fiber input.

Conclusions:

Climbing fiber input can effectively supervise Purkinje cell learning via a depolarization-based mechanism.
This mechanism integrates complex spike events with spike-timing-dependent plasticity.
The findings offer insights into cerebellar learning and synaptic plasticity.