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Oscillations In An LC Circuit

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An idealized LC circuit of zero resistance can oscillate without any source of emf by shifting the energy stored in the circuit between the electric and magnetic fields. In such an LC circuit, if the capacitor contains a charge q before the switch is closed, then all the energy of the circuit is initially stored in the electric field of the capacitor. This energy is given by

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A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

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Published on: June 22, 2015

Contrastive Learning and Neural Oscillations.

Pierre Baldi¹, Fernando Pineda²

¹Jet Propulsion Laboratory and Division of Biology, California Institute of Technology, Pasadena, CA 91125 USA.

Neural Computation

|June 7, 2019

Summary

This summary is machine-generated.

Contrastive Learning (CL) offers a unified framework for neural network algorithms, extending Deterministic Boltzmann Machines. This method uses a two-phase oscillation with a teacher signal to optimize network weights via gradient descent.

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Neural network learning algorithms require robust frameworks.
Deterministic Boltzmann Machines offer a foundation but have limitations.
A unified approach can enhance algorithm development and understanding.

Purpose of the Study:

To introduce Contrastive Learning (CL) as a generalized framework for neural network algorithms.
To extend Deterministic Boltzmann Machines to broader dynamical systems.
To provide a unified perspective on various learning rules and signal types.

Main Methods:

Developing CL as a family of learning algorithms.
Utilizing a two-phase network oscillation: one with a teacher signal, one without.
Employing a learning rule based on gradient descent of a contrast function measuring network discrepancies.

Main Results:

CL provides a unified framework for new learning algorithms.
Demonstrates the possibility of diverse clamping and teacher signals.
Analyzes the contrast function landscape, predicting CL curve behaviors.

Conclusions:

Contrastive Learning offers a versatile and unified approach to neural network training.
The framework supports various implementations, including analog systems.
Potential implications for understanding brain oscillations are suggested.