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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
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...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

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Related Experiment Videos

An analog VLSI recurrent neural network learning a continuous-time trajectory.

G Cauwenberghs1

  • 1Dept. of Electr. Eng., California Inst. of Technol., Pasadena, CA.

IEEE Transactions on Neural Networks
|January 1, 1996
PubMed
Summary

This study introduces a novel analog very-large-scale integration (VLSI) implementation for recurrent dynamical neural networks using stochastic perturbation learning. This approach overcomes scalability limitations of traditional gradient descent methods for complex neural networks.

Related Experiment Videos

Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • VLSI Design

Background:

  • Gradient descent supervised learning in recurrent dynamical neural networks faces scalability challenges in very-large-scale integration (VLSI) due to computational complexity growth with network size.
  • Existing methods require explicit derivation of network dynamics gradients, which is complex and hinders practical implementation.

Purpose of the Study:

  • To present an alternative analog VLSI implementation for recurrent dynamical neural networks.
  • To overcome the limitations of traditional gradient descent algorithms for scalable neural network hardware.
  • To demonstrate a novel learning approach for recurrent dynamical networks.

Main Methods:

  • Developed an analog VLSI chip with six fully recurrent neurons and continuous-time dynamics, featuring 42 learnable parameters (connection strengths and thresholds).
  • Employed a stochastic perturbation algorithm to directly observe the error index gradient in random parameter space directions, bypassing explicit model derivation.
  • Integrated local learning and parameter storage within a scalable architecture for potential expansion.

Main Results:

  • Successfully implemented a functional analog recurrent neural network on a chip.
  • Demonstrated the network's ability to learn and represent a quadrature-phase oscillator.
  • The scalable architecture supports potential expansion for higher-dimensional network applications.

Conclusions:

  • The proposed stochastic perturbation learning method in analog VLSI offers a scalable solution for recurrent dynamical neural networks.
  • This approach avoids complex gradient derivations, enabling more practical hardware implementations.
  • The demonstrated architecture is suitable for future development of large-scale, on-chip learning systems.