liveBook · Manning

Synapse

As explained in Inside AI

A synapse is a specialized junction through which a neuron communicates with another neuron or target cell.

A synapse is a specialized junction through which a neuron communicates with another neuron or target cell. Neurotransmitters cross this junction, carrying signals from an axon terminal to receiving structures such as dendrites. Each neuron can form thousands of connections, creating an extensive communication network in the brain. section 6.1.1

Synapses are changeable rather than fixed. Repeated signaling can strengthen a connection, while learning can alter the brain’s electrochemical pathways and help create new synaptic connections. This physical adaptability links synapses to memory formation and distinguishes biological neural networks from the simpler weight-based learning of artificial neural networks. section 6.1.1 section 6.1.2

Learning and memory in sea slugs

Eric Kandel’s experiments with the simple nervous system of sea slugs showed that learning involved chemical changes in the synaptic network connecting cells. These changes in electrochemical signaling were associated with the formation of new memories. section 6.1.2

Figure 6.1 Image of a typical neuron (courtesy of Wikimedia Commons, CC BY-SA 3.0)
figure

Use the neuron diagram to identify the cell body, axon, dendrites, and the structures involved in synaptic communication. section 6.1.1

How Inside AI covers it

How deep it goes
Defines neurotransmitter-based communication and synaptic plasticity, but stops short of detailed molecular mechanisms.
Examples
Uses neuron anatomy and sea-slug learning experiments to illustrate synaptic communication and change.
What it assumes
Assumes no detailed neural background and introduces the neuron parts needed to understand synaptic communication.
What you can do after
Understand how synaptic signaling and plasticity contribute to biological communication, learning, and memory.
How it connects
Connects neurons, axons, dendrites, neurotransmitters, learning, memory, and artificial neural networks.

This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.