Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neural Circuits01:25

Neural Circuits

1.1K
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.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.1K
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

3.7K
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...
3.7K
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

355
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
355
Comparison between RL and RC circuits01:24

Comparison between RL and RC circuits

3.9K
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...
3.9K
Network Function of a Circuit01:25

Network Function of a Circuit

266
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
266
RL Circuit without Source01:14

RL Circuit without Source

877
When a DC source is suddenly disconnected from an RL (Resistor-Inductor) circuit, the circuit becomes source-free. Assuming the inductor has an initial current denoted as I0, the initial energy stored in the inductor can be determined.
Applying Kirchhoff's voltage law around the loop of the circuit and substituting the voltages across the inductor and resistor yields a first-order differential equation. A logarithmic equation is obtained by rearranging the terms in this equation,...
877

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Element-wise and Recursive Solutions for the Power Spectral Density of Biological Stochastic Dynamical Systems at Fixed Points.

Physical review research·2026
Same author

Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long-Term Active Place Avoidance Memory and "Shadow" Proteins.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Emergent universal long-range structure in random-organizing systems.

Nature communications·2026
Same author

PropMolFlow: property-guided molecule generation with geometry-complete flow matching.

Nature computational science·2026
Same author

Gyromorphs: A New Class of Functional Disordered Materials.

Physical review letters·2025
Same author

Unconditional stability of a recurrent neural circuit implementing divisive normalization.

Advances in neural information processing systems·2025

Related Experiment Video

Updated: Jun 10, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

Unconditional stability of a recurrent neural circuit implementing divisive normalization.

Shivang Rawat1,2, David J Heeger3,4, Stefano Martiniani1,2,5

  • 1Courant Institute of Mathematical Sciences, NYU.

Arxiv
|October 14, 2024
PubMed
Summary
This summary is machine-generated.

We introduce Oscillatory Recurrent Gated Neural Integrator Circuits (ORGaNICs), a biologically plausible model offering unconditional stability. This breakthrough enables training via backpropagation without gradient issues, outperforming other neurodynamical models on classification tasks.

More Related Videos

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus
05:01

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

Published on: September 20, 2024

324
Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array
09:48

Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array

Published on: March 27, 2015

8.4K

Related Experiment Videos

Last Updated: Jun 10, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus
05:01

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

Published on: September 20, 2024

324
Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array
09:48

Neural Activity Propagation in an Unfolded Hippocampal Preparation with a Penetrating Micro-electrode Array

Published on: March 27, 2015

8.4K

Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Dynamical Systems

Background:

  • Recurrent neural models face stability challenges, hindering biologically plausible and trainable neurodynamical systems.
  • Traditional cortical models are difficult to train due to nonlinearities, while RNNs lack biological plausibility.
  • Existing models struggle with gradient issues like exploding, vanishing, and oscillations during training.

Purpose of the Study:

  • To link dynamic divisive normalization (DN) to the stability of Oscillatory Recurrent Gated Neural Integrator Circuits (ORGaNICs).
  • To establish ORGaNICs as a biologically plausible, stable, and trainable recurrent neural model.
  • To provide a normative principle for circuit and neuron function.

Main Methods:

  • Utilized the indirect method of Lyapunov to prove unconditional local stability for ORGaNICs with an identity recurrent weight matrix.
  • Connected ORGaNICs to coupled damped harmonic oscillators to derive an energy function.
  • Proved stability for the 2D ORGaNICs model and empirically validated stability in higher dimensions for generic weight matrices.

Main Results:

  • ORGaNICs demonstrate unconditional local stability for arbitrary dimensions when the recurrent weight matrix is the identity.
  • An energy function was derived, revealing a normative principle for ORGaNICs and individual neurons.
  • ORGaNICs can be trained using backpropagation through time without gradient clipping/scaling, overcoming common gradient problems.

Conclusions:

  • ORGaNICs offer a stable and biologically plausible recurrent neural circuit model.
  • The model's intrinsic stability and adaptive time constants facilitate effective training.
  • ORGaNICs show competitive performance on static image classification and sequential tasks, outperforming alternative neurodynamical models.