Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET.
PulseAugur coverage of Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET. — every cluster mentioning Leaky Integrate and Fire Neuron by Charge-Discharge Dynamics in Floating-Body MOSFET. across labs, papers, and developer communities, ranked by signal.
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New KvLIF neuron model boosts Spiking Neural Network capacity and robustness
Researchers have developed a new neuron model called Potassium-regulated LIF (KvLIF) to improve the performance and robustness of Spiking Neural Networks (SNNs). This model, inspired by biological potassium channels, in…
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HyVox system merges neuromorphic and von Neumann computing for low-power voice AI
Researchers have developed HyVox, a novel hybrid system that combines neuromorphic and von Neumann computing for low-power, open-set speaker identification. This system splits the workload at the speaker embedding stage…
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New Spiking Reservoir Computing Method Enhances Frequency Discrimination
Researchers have developed a new spiking reservoir computing method called HRF-Res, which replaces traditional Leaky Integrate-and-Fire (LIF) neurons with Harmonic Resonate-and-Fire (HRF) neurons. This innovation allows…
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New method enhances robustness of event-based spiking neural networks
Researchers have developed Perturbation-Aware Firing Scaffolds (PAFS), a novel post-training calibration method for event-based spiking neural networks (SNNs). PAFS addresses the sensitivity of SNNs to perturbations lik…
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FPGA accelerator for Spiking Neural Networks uses minifloat arithmetic
Researchers have developed a new hardware accelerator architecture for Spiking Neural Networks (SNNs) that utilizes parameterized minifloat arithmetic. This design allows for configurable precision, enabling a trade-off…
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Bio-inspired Spiking Ternary GRU achieves competitive speech recognition
Researchers have developed a novel Spiking Ternary GRU (stGRU) architecture inspired by the human cerebral cortex for improved speech recognition. This bio-inspired model couples parallel resonate-and-fire dynamics with…
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New method controls latency in temporal coded spiking neural networks
Researchers have developed a method to analytically control the latency of Delay Neural Networks (DeNN), a type of spiking neural network that directly computes spiking times. This work investigates the trade-off betwee…
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Locust-inspired neural network enhances collision perception
Researchers have developed a new biologically plausible neural network inspired by the visual system of locusts for detecting looming objects and potential collisions. This model mimics the ommatidial organization of a …
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New MeMark technique embeds watermarks in SNN neuron states
Researchers have developed a new watermarking technique called MeMark for Spiking Neural Networks (SNNs) to protect against unauthorized reuse of pre-trained models. Unlike previous methods that focused on output verifi…
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Bayes-Markov Model of Visual Cortex Orientation Selectivity Re-implemented and Simulated
Researchers have computationally re-implemented and simulated Shirazi's Bayes-Markov model, which proposes a probabilistic explanation for orientation selectivity in the primary visual cortex (V1). The study reconstruct…
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New DSTD method enables scalable training of continuous-time SNNs
Researchers have developed a new method called Differentiable Spike-Time Discretization (DSTD) to enable more efficient training of continuous-time spiking neural networks (SNNs). This approach significantly reduces mem…
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New open-source framework aids SNN hardware design and exploration
A new open-source framework has been developed to aid in the design and exploration of mixed-signal spiking neural networks (SNNs) for energy-efficient neuromorphic computing. This framework, built within PyTorch, allow…
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New open-source framework aids SNN hardware design exploration
Researchers have developed an open-source framework designed to simulate and explore the design space of mixed-signal Spiking Neural Networks (SNNs). This tool integrates device-level nonlinearities directly into PyTorc…
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Neuromorphic circuits exploit ECRAM dynamics for short-term plasticity
Researchers have developed a new neuromorphic circuit architecture that leverages the inherent non-equilibrium dynamics of electrochemical random-access memory (ECRAM) devices to implement short-term plasticity (STP). T…
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Researchers develop BadSNN to exploit spiking neuron hyperparameters for backdoor attacks
Researchers have developed "BadSNN," a novel backdoor attack targeting Spiking Neural Networks (SNNs). This attack exploits variations in the hyperparameters of spiking neurons, such as the Leaky Integrate-and-Fire mode…