Hebb's Law

Core Principle:

"Neurons that fire together, wire together": When two neurons are activated simultaneously or repeatedly, the synaptic connection between them strengthens. This underlies associative learning and memory formation.

Synaptic Plasticity:
- Long-Term Potentiation (LTP): Persistent strengthening of synapses due to high-frequency activation, often linked to learning.
- Long-Term Depression (LTD): Weakening of synapses when activity is asynchronous, maintaining neural balance.
Biological Basis:
- Co-activation of pre- and postsynaptic neurons triggers biochemical changes (e.g., NMDA receptor activation, calcium influx), leading to structural modifications like increased neurotransmitter receptors.

Spike-Timing-Dependent Plasticity (STDP): Timing-specific plasticity where synapses strengthen if the presynaptic neuron fires just before the postsynaptic neuron (and weaken if the order is reversed).
Homeostatic Plasticity: Regulatory mechanisms (e.g., synaptic scaling) prevent runaway excitation by stabilizing overall neural activity.

Learning and Memory: Explains associative learning (e.g., Pavlovian conditioning) and skill acquisition through reinforced neural pathways.
Artificial Intelligence: Inspired unsupervised learning algorithms (e.g., Hebbian learning rules), though modified with normalization (e.g., Oja's rule) to avoid instability.

Simplistic Model: Original theory lacks details on inhibitory synapses, timing, and weakening mechanisms.
Stability Issues: Pure Hebbian learning can lead to uncontrolled synaptic growth, necessitating additional regulatory principles.

Hebb's Law remains a cornerstone of neuroscience, providing a theoretical framework for understanding learning. Modern research has expanded it to include precise timing (STDP) and balancing mechanisms (LTD, homeostasis), enriching its applicability to both biological and artificial neural networks.