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Observational Learning01:12

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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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Cognitive learning is based on purposive behavior, incidental learning, and insight 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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Online Spatio-Temporal Learning in Deep Neural Networks.

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    We introduce online spatio-temporal learning (OSTL), a novel algorithm for training deep recurrent neural networks (RNNs) and spiking neural networks (SNNs) online. OSTL enables efficient, biologically inspired online learning for SNNs, matching traditional offline methods.

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    Area of Science:

    • Computational Neuroscience
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Biological neural networks exhibit continuous online learning, unlike traditional deep learning methods.
    • Error backpropagation through time (BPTT) requires offline gradient computation, limiting real-time adaptation.
    • Existing methods struggle to achieve efficient online training for complex neural network architectures like SNNs.

    Purpose of the Study:

    • To develop a novel online learning algorithm for deep recurrent neural networks (RNNs) and spiking neural networks (SNNs).
    • To enable biologically plausible, continuous adaptation in artificial neural networks.
    • To achieve BPTT-equivalent gradient performance in an online setting for SNNs.

    Main Methods:

    • Introduced online spatio-temporal learning (OSTL), a framework inspired by biological neural processing.
    • OSTL separates spatial and temporal gradient computations for efficient online processing.
    • Extended OSTL to accommodate various architectures, including LSTMs and GRUs.

    Main Results:

    • OSTL achieves gradient equivalence to BPTT for shallow SNNs, enabling online training with comparable performance.
    • Demonstrated online training of SNNs at low time complexity.
    • Achieved results on par with BPTT baselines in language modeling and speech recognition tasks.

    Conclusions:

    • OSTL provides a viable and efficient framework for online learning in deep RNNs and SNNs.
    • The proposed method bridges the gap between biological online learning and artificial neural network training.
    • OSTL offers a promising direction for developing more adaptive and biologically realistic AI systems.