Results 51 to 60 of about 68,095 (317)
A Cre‐dependent lentiviral vector for neuron subtype‐specific expression of large proteins
FEBS Letters, EarlyView.We designed a versatile and modular lentivector comprising a Cre‐dependent switch and self‐cleaving 2A peptide and tested it for co‐expression of GFP and a 2.8 kb gene of interest (GOI) in mouse cortical parvalbumin (PV+) interneurons and midbrain dopamine (TH+) neurons.
Weixuan Xue +6 more
wiley +1 more source
High-performance deep spiking neural networks with 0.3 spikes per neuron
Nature CommunicationsCommunication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks.
Ana Stanojevic +5 more
doaj +1 more source
Progress and Benchmark of Spiking Neuron Devices and Circuits
Advanced Intelligent Systems, 2021 The sustainability of ever more sophisticated artificial intelligence relies on the continual development of highly energy‐efficient and compact computing hardware that mimics the biological neural networks.
Fu-Xiang Liang +2 more
doaj +1 more source
, 2017 Elucidating principles that underlie computation in neural networks is currently a major research topic of interest in neuroscience. Transfer Entropy (TE) is increasingly used as a tool to bridge the gap between network structure, function, and behavior ...
de Ruyter F. +3 more
core +1 more source
Visual Recovery Reflects Cortical MeCP2 Sensitivity in Rett Syndrome
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Rett syndrome (RTT) is a devastating neurodevelopmental disorder with developmental regression affecting motor, sensory, and cognitive functions. Sensory disruptions contribute to the complex behavioral and cognitive difficulties and represent an important target for therapeutic interventions.
Alex Joseph Simon +12 more
wiley +1 more source
Efficient Computation in Adaptive Artificial Spiking Neural Networks [PDF]
, 2017 Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and ...
Bohte, Sander +3 more
core +1 more source
Stochastic dynamics of a finite-size spiking neural network
, 2006 We present a simple Markov model of spiking neural dynamics that can be analytically solved to characterize the stochastic dynamics of a finite-size spiking neural network.
Chow, C. C., Soula, H.
core +4 more sources
Spiking Neural Networks Trained via Proxy
IEEE Access, 2022 We propose a new learning algorithm to train spiking neural networks (SNN) using conventional artificial neural networks (ANN) as proxy. We couple two SNN and ANN networks, respectively, made of integrate-and-fire (IF) and ReLU neurons with the same network architectures and shared synaptic weights.
Kheradpisheh, Saeed Reza +2 more
openaire +5 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Neurodegeneration with brain iron accumulation (NBIA) comprises a genetically and clinically heterogeneous group of rare neurological disorders characterized particularly by iron accumulation in the basal ganglia. To date, 15 genes have been associated with NBIA.
Seda Susgun +95 more
wiley +1 more source
Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach [PDF]
, 2018 Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or power demand, recent interest for
Davidson, Simón +6 more
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