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Researchers from @engineering_UT introduce an analog spiking neuron that utilizes time-domain information to construct a spiking neural network (SNN) for a hardware-friendly physical reservoir computing on a CMOS platform in.@UTokyo_News_en @JST_Kisokenkyu

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Researchers from @ntua fabricated a free memory forming device consisting of a MoS2 monolayer with dimensions of ∼100 μm, decorated with ∼3 nm diameter Pt nanoparticles, demonstrating synaptic plasticity and neuronal coding schemes in experiments.

Our latest Rising Star is Suma George Cardwell, who with colleagues at @SandiaLabs presents work that aims to incorporate dendrites for "compute-on-wire" in neuromorphic architectures. Read the paper at see the whole collection at

Part of this Perspective article from @westernsydneyu, @UniofHerts and @CUBoulder takes a look at machine olfaction, finding that applying neuromorphic principles may improve response time and task performance in robotic olfaction:.

Part of this paper from @IITKanpur proposes a novel, highly scalable, compact, energy-efficient, and reconfigurable logic-in-memory implementation by exploiting the drain-erase scheme utilizing a single ferroelectric FDSOI FET.

Part of here @Mattias_Borg and colleagues from @lunduniversity and @ericssonlabs evaluate the viability of FTJ memristors as compute units for analog in-memory computing (AIMC), revealing benefits alongside intrinsic challenges:.

Part of this paper from @UTKnoxville @EECS_UTK presents cutting-edge neuromorphic technology beating a retro Atari 2600 at Pong. What's your high score?.

Part of this paper from @TU_Muenchen presents a novel spiking neuron model for signal processing of frequency-modulated continuous wave radars that outperforms the state-of-the-art spectrum analysis algorithms in many respects.

Our latest Rising Star is Chiara De Luca (Institute of Neuroinformatics, @uzh_en @eth_en), who with @FFilippoCCosta proposes an encoding framework inspired by biological principles, using at most one spike per neuron, for low-power, low-latency processing:.

#TUBraunschweig researchers with colleagues at @amsOSRAM & elsewhere in Germany and Switzerland present a novel approach for the implementation of a mixed photonic/electronic neural network using gallium nitride based microLEDs as an efficient key element.

Part of this paper from Cristian Axenie and TH Nürnberg colleagues proposes a novel neuromorphic edge fusion of SNNs and event-based expectation maximization for the detection and tracking of pedestrians and cyclists. Read more at:.

Part of this paper from @univgroningen proposes Fused-MemBrain, inspired by Golgi's theory modeling the brain as a syncytial continuum, with a memristive network interfaced with spiking neural networks. Read more at:.

Lei Deng (@Tsinghua_Uni) is our latest Rising Star. With colleagues at @CBICR_THU and Taiyuan University of Technology he explores the emulation of finite state machines (FSMs) with neural networks, proposing discrete-time spiking recurrent neural networks.

As part of @HerbozoFernando, @JHuang_ai, @OmidKavehei & colleagues from @Sydney_Uni investigate biologically plausible algorithms, specifically emphasizing time-domain inputs with low computational overhead, to monitor EEG signals:.

Sofie J Studholme, Joshua B Mallinson, Jamie K Steel & @cluster_guy (@UCNZ) demonstrate a route to experimental implementation of an integer factorization algorithm in percolating networks of nanoparticles (PNNs), which have a brain-like network structure:.

Congratulations to all recipients of our Outstanding Reviewer Awards! 🎉Your commitment, timeliness, quality, & quantity of reports is celebrated across our journals. Let's celebrate all #PeerReview contributions that uphold #ResearchIntegrity.Discover 👉

Researchers from @jkulinz provide a mathematical framework that shows that the information encoding mechanisms of Threshold-Based Representation based on LIF and Send-on-Delta distinguish by maximal sparsity while meeting certain approximation conditions.

Part of this paper from Michael Stuck, @XingyunWang4 & @neuronaud (@uOttawaCMM, @uOttawaBMRI) extends burst-dependent learning (Burstprop) to address more complex tasks with hundreds of neurons:.

Part of this Perspective article from Jiyong Woo (@knustory) and colleagues investigates gate-controlled ion-transport primarily in electrolytes for electrochemical random-access memory (ECRAM) for three-terminal synaptic devices:.