Achieving Swarm Intelligence with Spiking Neural Oscillators

Fang, Yan; Dickerson, Samuel J.

doi:10.1109/icrc.2017.8123632

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Transforming vector quantization into QUBO is not only interesting for quantum devices. As a heuristic for neuromorphic hardware, another emerging paradigm, were presented [8].…”

Section: Discussionmentioning

confidence: 99%

Quantum-ready vector quantization: Prototype learning as a binary optimization problem

Engelsberger,

Villmann

2023

ESANN 2023 Proceesdings

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Quantum Computing Research proposed strategies to solve binary optimization problems. Application on current and near-term generation Hardware is possible. Even if computational benefits of the strategies are yet to be shown, we want to explore connections to prototype learning schemes. We examine cost functions for vector quantization based on data point selection and how they can be transformed into a common quadratic unconstrained binary optimization formulation (QUBO). There are different approaches for solving QUBO problems using quantum computer or quantum annealer hardware. We look at their current limits and how they might change.

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“…Transforming vector quantization into QUBO is not only interesting for quantum devices. As a heuristic for neuromorphic hardware, another emerging paradigm, were presented [8].…”

Section: Discussionmentioning

confidence: 99%

Quantum-ready vector quantization: Prototype learning as a binary optimization problem

Engelsberger,

Villmann

2023

ESANN 2023 Proceesdings

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“…On the contrary, exploitation focuses on the individual local search within the vicinities of the current best solution. The pseudo-code in Algorithm 1 describes the framework of most SI algorithms (Fang and Dickerson, 2017).…”

Section: Methodsmentioning

confidence: 99%

“…However, these two sets of models share some similarities. Both of them are bio-inspired, highly parallelized, and composed of multiple homogeneous units (agents and neurons) (Fang and Dickerson, 2017). Their computational capabilities origin from the interaction and communication between the individual units.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, SNNs cannot harness the collective properties of optimization problems. In our previous work (Fang and Dickerson, 2017), we explored the opportunities in bridging these two models and proposed a computing paradigm based on SI and coupled spiking oscillator network to address optimization problems. In this work, we provide details and develop an SI-SNN architecture and demonstrate how it is capable of solving two types of optimization problems, parameter optimization of continuous objective functions and TSP.…”

Section: Introductionmentioning

confidence: 99%

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A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

et al. 2019

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As computational models inspired by the biological neural system, spiking neural networks (SNN) continue to demonstrate great potential in the landscape of artificial intelligence, particularly in tasks such as recognition, inference, and learning. While SNN focuses on achieving high-level intelligence of individual creatures, Swarm Intelligence (SI) is another type of bio-inspired models that mimic the collective intelligence of biological swarms, i.e., bird flocks, fish school and ant colonies. SI algorithms provide efficient and practical solutions to many difficult optimization problems through multi-agent metaheuristic search. Bridging these two distinct subfields of artificial intelligence has the potential to harness collective behavior and learning ability of biological systems. In this work, we explore the feasibility of connecting these two models by implementing a generalized SI model on SNN. In the proposed computing paradigm, we use SNNs to represent agents in the swarm and encode problem solutions with the spike firing rate and with spike timing. The coupled neurons communicate and modulate each other's action potentials through event-driven spikes and synchronize their dynamics around the states of optimal solutions. We demonstrate that such an SI-SNN model is capable of efficiently solving optimization problems, such as parameter optimization of continuous functions and a ubiquitous combinatorial optimization problem, namely, the traveling salesman problem with near-optimal solutions. Furthermore, we demonstrate an efficient implementation of such neural dynamics on an emerging hardware platform, namely ferroelectric field-effect transistor (FeFET) based spiking neurons. Such an emerging in-silico neuron is composed of a compact 1T-1FeFET structure with both excitatory and inhibitory inputs. We show that the designed neuromorphic system can serve as an optimization solver with high-performance and high energy-efficiency.

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Solving Quadratic Unconstrained Binary Optimization with Collaborative Spiking Neural Networks

Fang

Lele

2022

2022 IEEE International Conference on Rebooting Computing (ICRC)

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Achieving Swarm Intelligence with Spiking Neural Oscillators

Cited by 4 publications

References 15 publications

Quantum-ready vector quantization: Prototype learning as a binary optimization problem

Quantum-ready vector quantization: Prototype learning as a binary optimization problem

A Swarm Optimization Solver Based on Ferroelectric Spiking Neural Networks

Solving Quadratic Unconstrained Binary Optimization with Collaborative Spiking Neural Networks

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