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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...

The Role of Ion Channels in Neuronal Computation

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.

Synaptic Signaling

Synaptic Signaling

Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.

Synaptic Signaling

Synaptic Signaling

Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...

The Synapse

The Synapse

Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.

Postsynaptic Potential (PSP)

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...

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Related Experiment Video

Updated: Jun 17, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Synaptic computation underlying probabilistic inference.

Alireza Soltani¹, Xiao-Jing Wang

¹Department of Neurobiology and Kavli Institute of Neuroscience, Yale University School of Medicine, New Haven, Connecticut, USA. soltani@caltech.edu

Nature Neuroscience

|December 17, 2009

Summary

This summary is machine-generated.

Synapses perform neuronal computations for probabilistic reasoning by learning cue probabilities. A neural circuit model shows synapses compute posterior probabilities, enabling near-optimal cue combination and decision-making.

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Area of Science:

Computational neuroscience
Cognitive neuroscience
Biophysics

Background:

Probabilistic reasoning involves integrating sensory cues and deducing their predictive power.
Understanding the neuronal mechanisms of probabilistic inference is a key challenge.

Purpose of the Study:

To propose synapses as the computational units for probabilistic reasoning.
To develop a neural circuit model for probabilistic inference.
To investigate how synapses compute posterior probabilities and guide decisions.

Main Methods:

Constructed a neural circuit model for probabilistic inference.
Incorporated reward-dependent plasticity for synaptic learning.
Simulated cue integration and decision-making processes.
Validated the model against a monkey categorization experiment.

Main Results:

Bounded synapses naturally compute posterior probabilities through reward-dependent plasticity.
The model decision circuit performs near-optimal cue combination based on summed log posterior odds.
The model reproduces key findings from a monkey experiment.

Conclusions:

Synapses may serve as the biophysical basis for Bayesian decision rules in probabilistic reasoning.
The model predicts deviations from ideal Bayesian behavior, such as base-rate neglect.
This work offers insights into the neural implementation of probabilistic inference.