Lecture

Models of Long-Term Plasticity: Hebbian Learning

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Description

This lecture covers the classification of plasticity into short-term and long-term, Hebbian learning rules, spike-timing models of plasticity, and the online learning of memories. It delves into rate-based Hebbian learning, the Bienenstock-Cooper-Munro rule, and the functional consequences of Hebbian learning in detecting correlations in the input and developing receptive fields. The lecture also explores synaptic changes for the development of the cortex, models for Hebbian long-term plasticity, and the implications of BCM rule in avoiding blow-up and promoting competition.

In MOOC

Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

This course gives an introduction to the field of theoretical and computational neuroscience with a focus on models of single neurons. Neurons encode information about stimuli in a sequence of short electrical pulses (spikes).

Official source

Related concepts (31)

Spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) is a biological process that adjusts the strength of connections between neurons in the brain. The process adjusts the connection strengths based on the relative timing of a particular neuron's output and input action potentials (or spikes). The STDP process partially explains the activity-dependent development of nervous systems, especially with regard to long-term potentiation and long-term depression.

Synaptic plasticity

In neuroscience, synaptic plasticity is the ability of synapses to strengthen or weaken over time, in response to increases or decreases in their activity. Since memories are postulated to be represented by vastly interconnected neural circuits in the brain, synaptic plasticity is one of the important neurochemical foundations of learning and memory (see Hebbian theory). Plastic change often results from the alteration of the number of neurotransmitter receptors located on a synapse.

Homeostatic plasticity

In neuroscience, homeostatic plasticity refers to the capacity of neurons to regulate their own excitability relative to network activity. The term homeostatic plasticity derives from two opposing concepts: 'homeostatic' (a product of the Greek words for 'same' and 'state' or 'condition') and plasticity (or 'change'), thus homeostatic plasticity means "staying the same through change". Homeostatic synaptic plasticity is a means of maintaining the synaptic basis for learning, respiration, and locomotion, in contrast to the Hebbian plasticity associated with learning and memory.

Neuroplasticity

Neuroplasticity, also known as neural plasticity, or brain plasticity, is the ability of neural networks in the brain to change through growth and reorganization. It is when the brain is rewired to function in some way that differs from how it previously functioned. These changes range from individual neuron pathways making new connections, to systematic adjustments like cortical remapping. Examples of neuroplasticity include circuit and network changes that result from learning a new ability, information acquisition, environmental influences, practice, and psychological stress.

Long-term potentiation

In neuroscience, long-term potentiation (LTP) is a persistent strengthening of synapses based on recent patterns of activity. These are patterns of synaptic activity that produce a long-lasting increase in signal transmission between two neurons. The opposite of LTP is long-term depression, which produces a long-lasting decrease in synaptic strength. It is one of several phenomena underlying synaptic plasticity, the ability of chemical synapses to change their strength.

Related lectures (64)

Classification of Plasticity: Short-term vs. Long-term Changes MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores short-term and long-term synaptic changes, Hebbian learning, and the role of neuromodulators.

Synaptic Plasticity: Online Memory Learning MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores synaptic plasticity, spike-timing models, and online memory learning challenges in computational neuroscience.

From average to online learning

Covers the transition from a Monte Carlo approximation of the average to deriving batch and online update rules.

Introduction to synaptic plasticity MOOC: Simulating a hippocampus micro-circuit

Covers synaptic plasticity, types of synapses, Hebb's postulate, LTP, LTD, and network oscillations in the hippocampus.

Neural Networks: Multilayer Perceptrons