Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Янв. 4, 2024

It is widely believed the brain-inspired spiking neural networks have capability of processing temporal information owing to their dynamic attributes. However, how understand what kind mechanisms contributing learning ability and exploit rich properties satisfactorily solve complex computing tasks in practice still remains be explored. In this article, we identify importance capturing multi-timescale components, based on which a multi-compartment model with dendritic heterogeneity, proposed. The enables dynamics by automatically heterogeneous timing factors different branches. Two breakthroughs are made through extensive experiments: working mechanism proposed revealed via an elaborated XOR problem analyze feature integration at levels; comprehensive performance benefits over ordinary achieved several benchmarks for speech recognition, visual electroencephalogram signal robot place shows best-reported accuracy compactness, promising robustness generalization, high execution efficiency neuromorphic hardware. This work moves significant step toward real-world applications appropriately exploiting biological observations.

Язык: Английский

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Brain-Inspired Computing: A Systematic Survey and Future Trends

Proceedings of the IEEE, Год журнала: 2024, Номер 112(6), С. 544 - 584

Опубликована: Июнь 1, 2024

Язык: Английский

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Dendritic branch structure compartmentalizes voltage-dependent calcium influx in cortical layer 2/3 pyramidal cells

eLife, Год журнала: 2022, Номер 11

Опубликована: Март 23, 2022

Back-propagating action potentials (bAPs) regulate synaptic plasticity by evoking voltage-dependent calcium influx throughout dendrites. Attenuation of bAP amplitude in distal dendritic compartments alters a location-specific manner reducing bAP-dependent influx. However, it is not known if neurons exhibit branch-specific variability signals, independent distance-dependent attenuation. Here, we reveal that bAPs fail to evoke through voltage-gated channels (VGCCs) specific population branches mouse cortical layer 2/3 pyramidal cells, despite substantial VGCC-mediated sister branches. These contain VGCCs and successfully propagate the absence input; nevertheless, they bAP-evoked due reduction amplitude. We demonstrate these have more elaborate branch structure compared branches, which causes local electrotonic impedance Finally, show still amplify synaptically-mediated because differences voltage-dependence kinetics NMDA-type glutamate receptors. Branch-specific compartmentalization signals may provide mechanism for diversify tuning across tree.

Язык: Английский

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Ion Intercalation‐Mediated MoS₂ Conductance Switching for Highly Energy‐Efficient Memristor Synapse

Advanced Electronic Materials, Год журнала: 2025, Номер unknown

Опубликована: Фев. 2, 2025

Abstract Emerging memristor synapses with ion dynamics have the potential to process spatiotemporal information and can accelerate development of energy‐efficient neuromorphic computing. However, conventional ion‐migration‐type memristors suffer from low switching speed uncontrollable conductance modulation, hindering hardware implementation. Here, intercalation‐mediated in MoS 2 is introduced for a highly synapse (HEMS) accurately emulate bio‐synaptic function. Li‐ion intercalation into few‐layer induce structural evolution, thereby achieving high‐speed controllable modulation HEMS. Consequently, HEMS exhibits energy efficiency fast 500 ns consumption 2.85 fJ per synaptic event. The stable bidirectional plasticity by consecutive voltage pulses 5000 times be achieved Besides, endowed logic functions multiple sets inputs parallel integration. This work offers an alternative strategy fast‐speed via develop future

Язык: Английский

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Statistical learning algorithms for dendritic neuron model artificial neural network based on sine cosine algorithm

Information Sciences, Год журнала: 2023, Номер 629, С. 398 - 412

Опубликована: Фев. 8, 2023

Язык: Английский

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A GPU-based computational framework that bridges neuron simulation and artificial intelligence

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Сен. 18, 2023

Abstract Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework generate algorithms for artificial intelligence (AI) systems. However, expensive cost severely limits applications in both neuroscience AI fields. The major bottleneck during simulating compartment is ability simulator solve large systems linear equations. Here, we present novel D endritic H ierarchical S cheduling (DHS) method markedly accelerate such process. We theoretically prove that DHS implementation computationally optimal accurate. This GPU-based performs with 2-3 orders magnitude higher speed than classic serial Hines conventional CPU platform. build DeepDendrite framework, which integrates GPU computing engine NEURON demonstrate tasks. investigate how spatial patterns spine inputs affect neuronal excitability human pyramidal neuron model 25,000 spines. Furthermore, provide brief discussion on potential AI, specifically highlighting its enable efficient training biophysically typical image classification

Язык: Английский

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The impact of Hodgkin–Huxley models on dendritic research

The Journal of Physiology, Год журнала: 2022, Номер 601(15), С. 3091 - 3102

Опубликована: Окт. 11, 2022

Abstract For the past seven decades, Hodgkin–Huxley (HH) formalism has been an invaluable tool in arsenal of neuroscientists, allowing for robust and reproducible modelling ionic conductances electrophysiological phenomena they underlie. Despite its apparent age, role as a cornerstone computational neuroscience not waned. The discovery dendritic regenerative events mediated by synaptic solidified importance HH‐based models further, yielding new predictions concerning integration, plasticity neuronal computation. These are often validated through vivo vitro experiments, advancing our understanding neuron biological system emphasizing detailed instrument research. In this article, we discuss recent studies which HH is used to shed light on function phenomena. image

Язык: Английский

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Clustering of synaptic engram: Functional and structural basis of memory

Neurobiology of Learning and Memory, Год журнала: 2024, Номер unknown, С. 107993 - 107993

Опубликована: Окт. 1, 2024

Язык: Английский

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A tunable leaky integrate-and-fire neuron based on one neuromorphic transistor and one memristor

Applied Physics Letters, Год журнала: 2023, Номер 123(1)

Опубликована: Июль 3, 2023

Artificial leaky integrate-and-fire (LIF) neurons have attracted significant attention for building brain-like computing and neuromorphic systems. However, previous artificial LIF primarily focused on implementing function, the function of dendritic modulation has rarely been reported. In this Letter, a tunable neuron based an IGZO electric-double-layer (EDL) transistor TaOx memristor is fabricated, investigated. An IGZO-based EDL with modulatory terminal used to realize nonlinear integration filtering capability, as well neural excitability. Ag/TaOx/ITO threshold switching mimics all-or-nothing spiking soma. By incorporating these two components in customized way, such can emulate key biological rich computational flexibility. Our dynamics great potential perform more complex tasks future

Язык: Английский

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Context association in pyramidal neurons through local synaptic plasticity in apical dendrites

Frontiers in Neuroscience, Год журнала: 2024, Номер 17

Опубликована: Янв. 31, 2024

The unique characteristics of neocortical pyramidal neurons are thought to be crucial for many aspects information processing and learning in the brain. Experimental data suggests that their segregation into two distinct compartments, basal dendrites close soma apical branching out from thick dendritic tuft, plays an essential role cortical organization. A recent hypothesis states layer 5 cells associate top-down contextual arriving at tuft with features sensory input predominantly arrives dendrites. It has however remained unclear whether such context association could established by synaptic plasticity processes. In this work, we formalize objective through a mathematical loss function derive rule synapses optimizes loss. resulting utilizes is available either locally synapse, branch-local NMDA spikes, or global Ca 2+ events, both which have been observed experimentally cells. We show computer simulations enables patterns high somatic activity. Furthermore, it networks neuron models perform context-dependent tasks continual allocating new branches novel contexts.

Язык: Английский

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