Spiking neural networks take controlneural net architectureControl TheoryTelerobotsspikingStanding OrdersNeural networkBrain-inspired neural network architecture overcomes unsolved classical control theory problem for telerobotics.doi:10.1126/scirobotics.abk3268DeWolf, TravisScience Robotics...
The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect
Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to reproduce fast learning capabilities of the ...
Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, characterized by Ordinary Differential Equations (ODEs), allow for dynamic...
In the era of large-scale pretrained models, artificial neural networks (ANNs) have excelled in natural language understanding (NLU) tasks. However, their success often necessitates substantial computational resources and energy consumption. To address this, we explore the potential of spiking neural ...
The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that
Spiking Neural Network (SNN) control systems have demonstrated advantages over conventional Artificial Neural Networks (ANNs) in energy efficiency and data paucity. In this study, we introduce a SNN-based controller designed within the Neural Engineering Framework (NEF) for the stabilization and trajecto...
🔥 This repo collects top international conference papers, codes about Spiking Neural Networks for anyone who wants to do research on it. We are continuously improving the project. The part of 2018-2021 is referenced in Awesome-SNN-Paper-Collection. The part of 2022 is referenced in 2022年顶...
Additionally, we processed images using VGG16, one of the most commonly used convolutional neural networks of computer vision, characterized by its high number of convolutional layers and its very high accuracy in object classification22. Here, each input image is processed by a stack of 13 convol...
In machine learning, one trains recurrent neural networks by unrolling the network into a virtual feedforward network1, see Fig. 1b, and applying the backpropagation algorithm to that (Fig. 1c). This method is called backpropagation through time (BPTT), as it requires propagation of gradients...