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Ampere-Maxwell's Law: Problem-Solving

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Solving Max-Cut Problem Using Spiking Boltzmann Machine Based on Neuromorphic Hardware with Phase Change Memory.

Yu Gyeong Kang¹, Masatoshi Ishii², Jaeweon Park¹

¹Department of Material Science & Engineering, Inter-University Semiconductor Research Center, Research Institute of Advanced Materials, Seoul National University, Seoul, 08826, Republic of Korea.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|October 23, 2024

Summary

This summary is machine-generated.

This study introduces a novel hardware-friendly method using spiking neural networks (SNNs) to efficiently solve complex combinatorial optimization problems like Max-Cut on neuromorphic chips. The approach demonstrates effective convergence and high accuracy for large-scale problems.

Keywords:

Boltzmann machines combinatorial optimizations leaky integrate‐and‐fire neurons max‐cut problems neuromorphic hardware spiking neural networks

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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

Published on: November 12, 2019

Area of Science:

Neuromorphic Engineering
Computational Neuroscience
Optimization Algorithms

Background:

Combinatorial optimization problems (COPs), such as Max-Cut, are computationally intensive, with resource requirements growing exponentially with problem size.
Existing methods struggle with scalability and efficiency for large-scale COPs.
Neuromorphic hardware offers a promising platform for energy-efficient computation.

Purpose of the Study:

To propose a hardware-friendly method for solving the Max-Cut problem using spiking neural networks (SNNs) implemented on neuromorphic hardware.
To analyze the stochastic dynamics of leaky integrate-and-fire (LIF) neurons for hardware implementation of spiking Boltzmann machines (sBMs).
To develop an innovative algorithm for efficient and accurate solutions to large-scale COPs.

Main Methods:

Implementation of a spiking neural network (SNN)-based Boltzmann machine (BM) in neuromorphic hardware.
Analysis of stochastic dynamics of leaky integrate-and-fire (LIF) neurons with random walk noise.
Development of an innovative algorithm utilizing overlapping time windows for sBM.
Hardware validation on a 6-transistor/2-resistor (6T2R) neuromorphic chip with phase change memory (PCM) synapses.

Main Results:

Demonstrated effective convergence and high accuracy for large-scale Max-Cut problems through simulations.
Successful hardware implementation and validation on a custom neuromorphic chip.
Proposed annealing techniques and bias split methods to enhance convergence.
Introduced circuit design ideas for efficient sampling convergence evaluation.

Conclusions:

The proposed SNN-based neuromorphic hardware approach offers a potential solution for energy-efficient and hardware-implementable solving of COPs.
This work represents the first known instance of solving the Max-Cut problem using an SNN neuromorphic hardware chip.
The findings pave the way for practical applications of neuromorphic computing in complex optimization tasks.