Related Experiment Video
Updated: Aug 28, 2025
05:19
Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
Published on: November 12, 2019
7.1K
Synchronization of Stochastic Neural Networks Using Looped-Lyapunov Functional and Its Application to Secure
IEEE Transactions on Neural Networks and Learning Systems
|September 14, 2022
Summary
This study enhances neural network (NN) synchronization by addressing chaotic solutions, time delays, and stochastic disturbances. The proposed sampled-data control scheme improves synchronization and enables secure data transmission using delayed NNs as a cryptosystem.
Area of Science:
- Computational Neuroscience
- Control Theory
- Information Security
Background:
- Traditional neural networks (NNs) often lack robustness to real-world complexities.
- Implementing NNs requires emulating dynamical properties rather than redefining them.
- Factors like time delays, parameter uncertainties, and stochastic disturbances impact NN performance.
Purpose of the Study:
- To investigate synchronization in neural networks with chaotic solutions, time-varying delays, and parameter uncertainties.
- To design a user-desired NN by emulating dynamical properties of traditional NNs.
- To develop a secure cryptosystem using delayed NNs for data transmission.
Main Methods:
- A stochastic differential neural network model is developed to account for disturbances.
- Itô's formula and integral inequalities are used to derive stability conditions.
- A sampled-data-based control scheme is proposed for synchronization.
- A looped-type Lyapunov functional is employed to handle system complexities.
Main Results:
- The proposed control scheme and derived conditions ensure effective synchronization.
- The study successfully handles stochastic disturbances, time-varying delays, and parameter uncertainties.
- Delayed NNs demonstrate effectiveness as a cryptosystem for secure data transmission.
Conclusions:
- The developed control strategy enhances synchronization in complex neural networks.
- The proposed delayed neural network cryptosystem offers improved security for data transmission.
- The findings are validated through simulations and statistical measures on standard images.
Related Concept Videos
01:28Neuronal Communication
1.3K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
1.3K
01:23Linear time-invariant Systems
357
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
357
00:59Propagation of Uncertainty from Random Error
962
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
962
01:23Propagation of Action Potentials
6.6K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
6.6K
01:25Overview of Synapses
2.6K
A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...
2.6K
01:25Network Function of a Circuit
356
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.
356