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Related Concept Videos

Long-Term Memory01:18

Long-Term Memory

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Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
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Long-term Potentiation01:35

Long-term Potentiation

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Long-term Potentiation01:25

Long-term Potentiation

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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when...
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System of Memory01:23

System of Memory

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Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
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Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

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Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
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Storage01:23

Storage

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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Related Experiment Video

Updated: Mar 6, 2026

Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording
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A continual learning framework with long-term and multiple short-term memory networks.

Shangge Liu1, Lei Wang2, Rui Yan3

  • 1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210008, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel continual learning framework with multiple short-term memory networks and a Gaussian mixture model regularizer. It enhances knowledge retention and new learning by better balancing stability and plasticity.

Keywords:
Catastrophic forgettingContinual learningMultiple memory networksStability-plasticity dilemma

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neuroscience

Background:

  • Continual learning aims to sequentially accumulate knowledge, balancing stability and plasticity.
  • Existing methods often prioritize mitigating catastrophic forgetting, potentially sacrificing plasticity.
  • Single auxiliary memory networks may not capture the full diversity of new task knowledge.

Purpose of the Study:

  • To propose a novel continual learning framework inspired by neuroscience.
  • To enhance plasticity by capturing diverse task-specific knowledge.
  • To improve the balance between knowledge retention and new learning.

Main Methods:

  • Incorporating multiple short-term memory networks for diverse knowledge capture.
  • Utilizing a long-term memory network to preserve prior knowledge.
  • Developing a Gaussian mixture model-based regularizer for flexible knowledge integration, overcoming limitations of Euclidean distance regularizers.

Main Results:

  • The proposed framework effectively balances knowledge retention and new learning.
  • Theoretical analysis and experimental studies confirm the framework's efficacy.
  • Demonstrated advantage over existing methods in continual learning benchmarks.

Conclusions:

  • The novel framework enhances continual learning by leveraging distributed memory representations.
  • The Gaussian mixture model regularizer facilitates flexible knowledge selection and integration.
  • The framework is versatile and compatible with various existing continual learning algorithms.