查看更多>>摘要：A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data.We found that the more similar the brain model is to the biological counterpart in both scale and architecture,the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics.The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain.We identified that the removal of connections from the primary visual cortex(V1)to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart,despite a slight influence on the whole brain.In conclusion,the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions,and for digitally trying and testing diverse cognitive,medical and lesioning approaches that would otherwise be unfeasible in real subjects.
查看更多>>摘要：Spiking neural networks(SNNs)are gaining increasing attention for their biological plausibility and potential for improved computational efficiency.To match the high spatial-temporal dynamics in SNNs,neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly.This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture,which comprises 10 primary instructions and a few extended instructions.It supports flexible neuron model programming and local learning rule designs.The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm.We used a compression mechanism to represent synaptic connections,significantly reducing memory usage.The Darwin3 chip supports up to 2.35 million neurons,making it the largest of its kind on the neuron scale.The experimental results showed that the code density was improved by up to 28.3 × in Darwin3,and that the neuron core fan-in and fan-out were improved by up to 4096 × and 3072 × by connection compression compared to the physical memory depth.Our Darwin3 chip also provided memory saving between 6.8 × and 200.8 × when mapping convolutional spiking neural networks onto the chip,demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips.
Jakub VohryzekJoana CabralChristopher TimmermannSelen Atasoy...
39-48页
查看更多>>摘要：The human brain is a complex system,whose activity exhibits flexible and continuous reorganization across space and time.The decomposition of whole-brain recordings into harmonic modes has revealed a repertoire of gradient-like activity patterns associated with distinct brain functions.However,the way these activity patterns are expressed over time with their changes in various brain states remains unclear.Here,we investigate healthy participants taking the serotonergic psychedelic N,N-dimethyltryptamine(DMT)with the Harmonic Decomposition of Spacetime(HADES)framework that can characterize how different harmonic modes defined in space are expressed over time.HADES demonstrates significant decreases in contributions across most low-frequency harmonic modes in the DMT-induced brain state.When normalizing the contributions by condition(DMT and non-DMT),we detect a decrease specifically in the second functional harmonic,which represents the uni-to transmodal functional hierarchy of the brain,supporting the leading hypothesis that functional hierarchy is changed in psychedelics.Moreover,HADES'dynamic spacetime measures of fractional occupancy,life time and latent space provide a precise description of the significant changes of the spacetime hierarchical organization of brain activity in the psychedelic state.
查看更多>>摘要：Brain-inspired computing,drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain,has gained significant momentum in recent years.It has emerged as a research paradigm centered on brain-computer dual-driven and multi-network integration.One noteworthy instance of this paradigm is the hybrid neural network(HNN),which integrates computer-science-oriented artificial neural networks(ANNs)with neuroscience-oriented spiking neural networks(SNNs).HNNs exhibit distinct advantages in various intelligent tasks,including perception,cognition and learning.This paper presents a comprehensive review of HNNs with an emphasis on their origin,concepts,biological perspective,construction framework and supporting systems.Furthermore,insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.
查看更多>>摘要：Virtual brain twins are personalized,generative and adaptive brain models based on data from an individual's brain for scientific and clinical use.After a description of the key elements of virtual brain twins,we present the standard model for personalized whole-brain network models.The personalization is accomplished using a subject's brain imaging data by three means:(1)assemble cortical and subcortical areas in the subject-specific brain space;(2)directly map connectivity into the brain models,which can be generalized to other parameters;and(3)estimate relevant parameters through model inversion,typically using probabilistic machine learning.We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases:epilepsy,Alzheimer's disease,multiple sclerosis,Parkinson's disease and psychiatric disorders.Specifically,we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses.Finally,we pinpoint the key challenges and future directions.
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