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Related Concept Videos

Associative Learning01:27

Associative Learning

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...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Observational Learning01:12

Observational Learning

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 because...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Tolman introduced the idea that behavior is influenced by...
Reducing Line Loss01:18

Reducing Line Loss

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With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

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Related Experiment Videos

On-line learning algorithms for locally recurrent neural networks.

P Campolucci1, A Uncini, F Piazza

  • 1Dipartimento di Elettronica ed Automatica, Università di Ancona, Ancona, Italy. paolo@eealab.unian.it

IEEE Transactions on Neural Networks
|February 7, 2008
PubMed
Summary
This summary is machine-generated.

We introduce causal recursive backpropagation (CRBP), a novel online learning method for recurrent neural networks. CRBP offers improved stability and faster convergence compared to existing algorithms like Back-Tsoi, simplifying recurrent network training.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Locally recurrent neural networks (RNNs) with infinite impulse response (IIR) synapses are crucial for time-series processing.
  • Existing online training methods for these networks have limitations in stability and convergence speed.
  • The Back-Tsoi algorithm is a notable method for locally recurrent networks without architectural restrictions.

Purpose of the Study:

  • To propose a new, efficient gradient-based online learning procedure for locally recurrent neural networks.
  • To demonstrate the advantages of the proposed method over existing techniques in terms of stability, convergence, and computational complexity.
  • To provide a unifying framework for gradient calculation in recurrent networks with local feedback.

Main Methods:

  • Development of recursive backpropagation (RBP), an online learning algorithm for MLPs with IIR synapses.
  • Introduction of causal recursive backpropagation (CRBP) as the online version of RBP.
  • Comparison of CRBP with Back-Tsoi, truncated BPTT, and RTRL through theoretical analysis and simulations.

Main Results:

  • CRBP demonstrates superior stability and faster convergence compared to the Back-Tsoi algorithm.
  • The computational complexity of CRBP is comparable to, and often less than, the Back-Tsoi algorithm.
  • CRBP exhibits similar performance to truncated BPTT and RTRL but is significantly simpler and easier to implement.

Conclusions:

  • CRBP offers a unifying and advantageous approach to online training for locally recurrent neural networks.
  • The algorithm's simplicity, stability, and efficiency make it a practical choice for various applications.
  • CRBP's locality in space and time simplifies implementation compared to non-local methods like RTRL.