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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...

Overview of Synapses

Overview of Synapses

A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...

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.

Chemical Synapses

Chemical Synapses

Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...

Chemical Synapses

Chemical Synapses

Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...

Electrical Synapses

Electrical Synapses

Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...

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Related Experiment Video

Updated: Mar 7, 2026

Evaluation of Synapse Density in Hippocampal Rodent Brain Slices

Evaluation of Synapse Density in Hippocampal Rodent Brain Slices

Published on: October 6, 2017

Optimal Degrees of Synaptic Connectivity.

Ashok Litwin-Kumar¹, Kameron Decker Harris², Richard Axel³

¹Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA.

|February 21, 2017

Summary

This summary is machine-generated.

Sparse neural connectivity, like in cerebellar granule cells, can maximize representation dimension. However, dense wiring is advantageous when synapses use supervised plasticity for learning associations.

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Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

Published on: January 10, 2015

Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number

Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number

Published on: November 16, 2010

Related Experiment Videos

Last Updated: Mar 7, 2026

Evaluation of Synapse Density in Hippocampal Rodent Brain Slices

Evaluation of Synapse Density in Hippocampal Rodent Brain Slices

Published on: October 6, 2017

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

Published on: January 10, 2015

Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number

Quantifying Synapses: an Immunocytochemistry-based Assay to Quantify Synapse Number

Published on: November 16, 2010

Area of Science:

Neuroscience
Computational Neuroscience
Neural Circuits

Background:

Neuronal connectivity, the number of inputs per neuron, varies significantly across different brain regions and neuronal types.
For example, cerebellar granule cells receive far fewer inputs than Purkinje cells, while cerebral cortex neurons have more uniform and numerous inputs.

Purpose of the Study:

To investigate how the number of inputs per neuron affects the dimensional representation formed by a neural population.
To understand the implications of varying connectivity degrees for learning associations.

Main Methods:

Theoretical modeling of neural representations.
Analysis of how representation dimension scales with synaptic connectivity.

Main Results:

The study's theory predicts that representation dimensions are maximized at specific, anatomically observed sparse connectivity levels in systems like the cerebellum and insect mushroom body.
This suggests sparse connectivity can be optimal for representation capacity in certain neural architectures.

Conclusions:

Sparse connectivity can be superior to dense connectivity for maximizing representation dimensions in specific neural circuits.
The type of synaptic plasticity, particularly supervised plasticity, significantly influences whether dense connectivity becomes advantageous for learning.