Term · Topic

Hebbian Learning Rule

Q: What is Hebbian Learning Rule?

“Hebbian learning rule” is a local learning principle for neural systems that increases the connection weight between two units when they activate together, formalizing the correlation-based strengthening of synapses in biological and artificial neural networks. Expanded Explanation 1. Technical Function and Core Characteristics The Hebbian learning rule updates synaptic weights as a function of the correlation between pre-synaptic and post-synaptic activity. In its basic mathematical form, the

Hebbian learning rule is a local correlation-based learning principle for neural networks in which synaptic weights increase when connected units are co-active, and it matters in enterprise AI contexts for modeling unsupervised, online adaptation and biologically inspired learning mechanisms.

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MCP

curl -X POST https://mcp.decisioninsights.ai/api/mcp-public \
  -H "content-type: application/json" \
  -H "accept: application/json, text/event-stream" \
  -d '{"id":1,"jsonrpc":"2.0","method":"tools/call","params":{"arguments":{"slug":"hebbian-learning-rule"},"name":"di_get_term"}}'

REST

GET https://api.decisioninsights.ai/api/public/v1/glossary/13ae4166-cc57-4ae9-add3-3fb200b77ef0

CLI

di get-glossary --slug hebbian-learning-rule

JSON-LD

The schema.org markup this page publishes for search engines and AI agents, exactly as they read it.

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How an agent can rely on this definition

What the record lets an agent check before it uses the term in an answer.

The check	What the record shows	Where it is in the data
It's a known term	One entry, with its type (topic)	`name`
It sits in a glossary	Linked to Symbolic-Connectionist Hybrid Model, Spike-Timing Dependent Plasticity, Neural Network	`inDefinedTermSet`

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