Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19最新文献

Synaptic plasticity in an artificial Hebbian network exhibiting continuous, unsupervised, rapid learning 人工Hebbian网络的突触可塑性表现出连续、无监督、快速的学习

Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19 Pub Date : 2019-03-26 DOI: 10.1145/3320288.3320292

J. C. Scott, Thomas F. Hayes, A. Ozcan, W. Wilcke

引用次数: 1

Spatiotemporal Sequence Memory for Prediction using Deep Sparse Coding 基于深度稀疏编码的时空序列记忆预测

Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19 Pub Date : 2019-03-26 DOI: 10.1145/3320288.3320295

Edward Kim, E. Lawson, Keith Sullivan, Garrett T. Kenyon

{"title":"Spatiotemporal Sequence Memory for Prediction using Deep Sparse Coding","authors":"Edward Kim, E. Lawson, Keith Sullivan, Garrett T. Kenyon","doi":"10.1145/3320288.3320295","DOIUrl":"https://doi.org/10.1145/3320288.3320295","url":null,"abstract":"Our brains are, \"prediction machines\", where we are continuously comparing our surroundings with predictions from internal models generated by our brains. This is demonstrated by observing our basic low level sensory systems and how they predict environmental changes as we move through space and time. Indeed, even at higher cognitive levels, we are able to do prediction. We can predict how the laws of physics affect people, places, and things and even predict the end of someone's sentence. In our work, we sought to create an artificial model that is able to mimic early, low level biological predictive behavior in a computer vision system. Our predictive vision model uses spatiotemporal sequence memories learned from deep sparse coding. This model is implemented using a biologically inspired architecture: one that utilizes sequence memories, lateral inhibition, and top-down feedback in a generative framework. Our model learns the causes of the data in a completely unsupervised manner, by simply observing and learning about the world. Spatiotemporal features are learned by minimizing a reconstruction error convolved over space and time, and can subsequently be used for recognition, classification, and future video prediction. Our experiments show that we are able to accurately predict what will happen in the future; furthermore, we can use our predictions to detect anomalous, unexpected events in both synthetic and real video sequences.","PeriodicalId":250150,"journal":{"name":"Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133768480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Shortest Path and Neighborhood Subgraph Extraction on a Spiking Memristive Neuromorphic Implementation 尖峰记忆神经形态实现的最短路径和邻域子图提取

Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19 Pub Date : 2019-03-26 DOI: 10.1145/3320288.3320290

Catherine D. Schuman, Kathleen E. Hamilton, T. Mintz, Md Musabbir Adnan, B. W. Ku, S. Lim, G. Rose

引用次数: 25

Acquisition and Representation of Spatio-Temporal Signals in Polychronizing Spiking Neural Networks 多时变脉冲神经网络中时空信号的获取与表征

Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19 Pub Date : 2019-03-26 DOI: 10.1145/3320288.3320291

Felix Wang, William M. Severa, Fred Rothganger

引用次数: 1

A Pulse-gated, Neural Implementation of the Backpropagation Algorithm 反向传播算法的脉冲门控神经实现

Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19 Pub Date : 2019-03-26 DOI: 10.1145/3320288.3320305

A. Sornborger, L. Tao, J. Snyder, Anatoly Zlotnik

引用次数: 5

Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform 基于SpiNNaker神经形态平台的低功耗深度学习推理

Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19 Pub Date : 2019-03-26 DOI: 10.1145/3320288.3320300

C. Vineyard, Ryan Dellana, J. Aimone, Fred Rothganger, William M. Severa

{"title":"Low-Power Deep Learning Inference using the SpiNNaker Neuromorphic Platform","authors":"C. Vineyard, Ryan Dellana, J. Aimone, Fred Rothganger, William M. Severa","doi":"10.1145/3320288.3320300","DOIUrl":"https://doi.org/10.1145/3320288.3320300","url":null,"abstract":"With the successes deep neural networks have achieved across a range of applications, researchers have been exploring computational architectures to more efficiently execute their operation. In addition to the prevalent role of graphics processing units (GPUs), many accelerator architectures have emerged. Neuromorphic is one such particular approach which takes inspiration from the brain to guide the computational principles of the architecture including varying levels of biological realism. In this paper we present results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia National Laboratories developed Whetstone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.","PeriodicalId":250150,"journal":{"name":"Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131472594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Neuromorphic Computing for Autonomous Mobility in Natural Environments 自然环境下自主移动的神经形态计算

Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19 Pub Date : 2019-03-26 DOI: 10.1145/3320288.3320297

M. O. Khyam, D. Alexandre, Ananya Bhardwaj, Ruihao Wang, R. Müller

{"title":"Neuromorphic Computing for Autonomous Mobility in Natural Environments","authors":"M. O. Khyam, D. Alexandre, Ananya Bhardwaj, Ruihao Wang, R. Müller","doi":"10.1145/3320288.3320297","DOIUrl":"https://doi.org/10.1145/3320288.3320297","url":null,"abstract":"Certain bat species (horseshoe bats, Rhinolophidae) can navigate, avoid obstacles, and pursue prey in dense vegetation with exceptionally capable and efficient biosonar systems that operate in complete darkness and process biosonar echoes with brain masses as low as 0.2 g. Hence, they provide an opportunity to develop a novel integrated sensor system to enable autonomous navigation capabilities in small drones operating in complex natural environments. To explore this, we have investigated the biosonar system of Horseshoe bats from engineering, neuroscience, and signal processing perspectives, with the goal of developing a biomimetic system for small drones so that they can navigate in complex natural environments. To achieve this, a biomimetic sonar head capable of mimicking the peripheral dynamics seen in the biosonar of horseshoe bats has been used to collect a large data set of echoes (220,000) from natural forests. Then, these echoes were passed through a prototype brain-inspired signal processing chain that simulates the complete auditory process from input sound stimulus to the neural spike response. It should be possible to implement an integrated biomimetic/brain-inspired prototype system on a single chip (e.g., like prior neuromorphic implementations of auditory and vision systems). Such a single-chip implementation would make it possible to miniaturize the \"brain\" of an autonomous drone and serve to expand the use of autonomous small drones to numerous applications.","PeriodicalId":250150,"journal":{"name":"Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129865454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Binding of Sparse Distributed Representations in Hierarchical Temporal Memory 分层时间存储器中稀疏分布表示的绑定

Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19 Pub Date : 2019-03-26 DOI: 10.1145/3320288.3320299

Luke G Boudreau, D. Kudithipudi

引用次数: 0

Current-Mode Memristor Crossbars for Neuromorphic Computing 用于神经形态计算的电流模式记忆电阻横栅

Proceedings of the 7th Annual Neuro-inspired Computational Elements Workshop on - NICE '19 Pub Date : 2019-03-26 DOI: 10.1145/3320288.3320298

Cory E. Merkel

引用次数: 2