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Cognitive Learning01:21

Cognitive Learning

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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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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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function
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Exploring Neuromodulation for Dynamic Learning.

Anurag Daram1, Angel Yanguas-Gil2, Dhireesha Kudithipudi1

  • 1Neuromorphic AI Lab, University of Texas, San Antonio, TX, United States.

Frontiers in Neuroscience
|October 12, 2020
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Summary

Neuromodulatory plasticity enables efficient few-shot learning by allowing systems to adapt quickly with minimal data. This approach enhances dynamic learning architectures for power and area efficiency in AI.

Keywords:
ModNetdynamic learningmushroom body output neurons (MBONs)neuromodulationone-shot learning

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Continual learning systems need to adapt to new tasks using limited data.
  • Neuromodulation in the central nervous system facilitates dynamic learning across species.

Purpose of the Study:

  • To embed neuromodulatory plasticity into dynamic learning architectures for efficient few-shot learning.
  • To develop power and area efficient few-shot learning systems inspired by biological neuromodulation.

Main Methods:

  • Introduced ModNet, an architecture with a modulatory layer in a random projection framework, enhanced with attention and compartmentalized plasticity.
  • Developed a modulatory trace learning rule for deeper networks, utilizing time-dependent traces updated via simple plasticity rules.
  • Integrated an inbuilt modulatory unit to regulate learning based on context and internal state, enabling self-modification of weights.

Main Results:

  • ModNet architectures demonstrated rapid learning, completing benchmark image classification tasks in just 2 epochs.
  • The modulatory trace learning rule achieved 98.8% accuracy on the Omniglot dataset for few-shot image classification.
  • Achieved state-of-the-art few-shot learning performance with significantly reduced computational resources (20x fewer trainable parameters).

Conclusions:

  • Neuromodulatory plasticity is a viable mechanism for creating efficient and adaptable few-shot learning systems.
  • The proposed ModNet and learning rules offer a computationally efficient approach to few-shot image classification.
  • This biologically inspired approach advances the development of AI systems capable of rapid, low-data learning.