14 papers · ranked by Valyu relevance
Joel Zylberberg, Jason Timothy Murphy, Michael Robert DeWeese, Olaf Sporns
'Olaf Sporns'] Sparse coding algorithms trained on natural images can accurately predict the features that excite visual cortical neurons, but it is not known whether such codes can be learned using biologically realistic plasticity rules. We have developed a biophysically motivated spiking network, relying solely on…
Joel Zylberberg, Michael Robert DeWeese, Olaf Sporns
The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using…
Rieke Fruengel, Marcel Oberlaender
Neurons in cortical networks are very sparsely connected; even neurons whose axons and dendrites overlap are highly unlikely to form a synaptic connection. What is the relevance of such sparse connectivity for a network’s function? Surprisingly, it has been shown that sparse connectivity impairs information processing…
Ahmed Hasssan, Jian Meng, Anupreetham Anupreetham, Jae-sun Seo
Spiking neural networks (SNNs) have received increasing attention due to their high biological plausibility and energy efficiency. The binary spike-based information propagation enables efficient sparse computation in event-based and static computer vision applications. However, the weight precision and especially the…
Xingyu Liu, Zonglei Zhen, Jia Liu
Recently, deep convolutional neural networks (DCNNs) have attained human-level performances on challenging object recognition tasks owing to their complex internal representation. However, it remains unclear how objects are represented in DCNNs with an overwhelming number of features and non-linear operations. In…
Jianning Li, Christina Gsaxner, Antonio Pepe, Dieter Schmalstieg + 2 more
'Jens Kleesiek' 'Jan Egger'] Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of high-resolution shapes represented as binary voxel occupancy grids.…
Adrien Fois, Bernard Girau
Current representation learning methods in Spiking Neural Networks (SNNs) rely on rate-based encoding, resulting in high spike counts, increased energy consumption, and slower information transmission. In contrast, our proposed method, Weight-Temporally Coded Representation Learning (W-TCRL), utilizes temporally coded…
Sheng Zhou, Chang Gao, Tobi Delbruck, Marian Verhelst + 1 more
Artificial intelligence (AI) has made significant strides towards efficient online processing of sensory signals at the edge through the use of deep neural networks with ever-expanding size. However, this trend has brought with it escalating computational costs and energy consumption, which have become major obstacles…
Mingjing Li, Huihui Zhou, Xiaofeng Xu, Zhiwei Zhong + 15 more
There is a growing necessity for edge training to adapt to dynamically changing environments. Neuromorphic computing represents a significant pathway for highly efficient intelligent computation in energy-constrained edges, but existing neuromorphic architectures lack the ability of directly training spiking neural…
Yulong Yan, Haoming Chu, Yi Jin, Yuxiang Huan + 2 more
'Lirong Zheng'] The spiking neural network (SNN) is a possible pathway for low-power and energy-efficient processing and computing exploiting spiking-driven and sparsity features of biological systems. This article proposes a sparsity-driven SNN learning algorithm, namely backpropagation with sparsity regularization…
Liyu Qian, Zikai Zhu, Yuhan He, Jie Lu + 4 more
This paper presents a neuromorphic processing system integrating a compressed sensing spiking neural network (CSSNN) designed for sparse signal classification. The proposed CSSNN combines data coding, data compression, and SNN classification, enabling end-to-end optimization of network performance and model…
Yuwei Cui, Subutai Ahmad, Jeff Hawkins
Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the…
Milad Mozafari, Mohammad Ganjtabesh, Abbas Nowzari-Dalini, Timothée Masquelier
'Timothée Masquelier'] Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient. Unlike the non-spiking counterparts, most of the existing SNN simulation frameworks are not…
Hongtu Xie, Xinqiao Jiang, Xiao Hu, Zhitao Wu + 2 more
'Kai Xie'] Ship recognition using synthetic aperture radar (SAR) images has important applications in the military and civilian fields. Aiming at the problems of the many model parameters and high-energy losses in the traditional deep learning methods for the target recognition in the SAR images, this study has…