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An RC circuit consists of resistance and capacitance, while in an RL circuit, capacitance is replaced by an inductor. RL and RC circuits are first-order differential circuits that store energy. An RC circuit stores energy in the electric field, while an RL circuit stores energy in the magnetic field. When connected to a battery, an RC circuit charges the capacitor, causing the current to decrease from maximum to zero upon being fully charged. This increases the voltage across the capacitor from...
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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,
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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...
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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Simple Recurrent Networks are Interactive.

James S Magnuson1,2,3, Sahil Luthra4

  • 1BCBL, Basque Center on Cognition Brain and Language, Donostia-San Sebastián, Spain. james.magnuson@uconn.edu.

Psychonomic Bulletin & Review
|November 13, 2024
PubMed
Summary
This summary is machine-generated.

Simple Recurrent Networks (SRNs) are not feedforward systems, contrary to some claims. Their cyclic structure allows for crucial feedback, impacting cognitive science theories of learning and processing.

Keywords:
InteractionNeural networks

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Machine Learning

Background:

  • Simple Recurrent Networks (SRNs) are foundational computational models in cognitive science.
  • SRNs have been used for over 30 years to model learning, development, and processing.
  • There is ongoing debate regarding whether SRNs function as feedforward or interactive systems.

Purpose of the Study:

  • To resolve the debate on whether Simple Recurrent Networks (SRNs) are feedforward systems.
  • To clarify the computational nature of SRNs and their implications for cognitive theories.
  • To demonstrate the interactive nature of SRNs through their architectural properties.

Main Methods:

  • Analysis of the network architecture of Simple Recurrent Networks (SRNs).
  • Examination of the flow of information and computation within SRNs, including feedback loops.
  • Comparison of SRN architecture to the definition of feedforward networks (acyclic graphs).

Main Results:

  • SRNs possess recurrent connections (loops) between hidden units, classifying them as cyclic graphs.
  • Contrary to claims, SRNs are not feedforward systems.
  • Bottom-up inputs in SRNs are intrinsically mixed with prior internal computations via feedback.

Conclusions:

  • SRNs are fundamentally interactive systems due to their recurrent feedback loops.
  • The interactive nature of SRNs has significant theoretical implications for understanding cognitive processes.
  • Reclassifying SRNs as interactive systems necessitates a re-evaluation of their role in cognitive modeling.