Proceedings of the International Conference on Neuromorphic Systems最新文献_第4页

DANNA 2: Dynamic Adaptive Neural Network Arrays DANNA 2:动态适应神经网络阵列

Proceedings of the International Conference on Neuromorphic Systems Pub Date : 2018-07-23 DOI: 10.1145/3229884.3229894

J. P. Mitchell, Mark E. Dean, Grant Bruer, J. Plank, G. Rose

引用次数: 26

Relative Efficiency of Memristive and Digital Neuromorphic Crossbars 记忆和数字神经形态交叉杆的相对效率

Proceedings of the International Conference on Neuromorphic Systems Pub Date : 2018-07-23 DOI: 10.1145/3229884.3229887

Christopher D. Krieger, D. Mountain, Mark McLean

引用次数: 1

Retinal-Inspired Algorithms for Detection of Moving Objects 基于视网膜的运动物体检测算法

Proceedings of the International Conference on Neuromorphic Systems Pub Date : 2018-07-23 DOI: 10.1145/3229884.3229895

Frances S. Chance, C. Warrender

{"title":"Retinal-Inspired Algorithms for Detection of Moving Objects","authors":"Frances S. Chance, C. Warrender","doi":"10.1145/3229884.3229895","DOIUrl":"https://doi.org/10.1145/3229884.3229895","url":null,"abstract":"The retina plays an important role in animal vision --- namely preprocessing visual information before sending it to the brain through the optic nerve. Understanding how the retina does this is of particular relevance for development and design of neuromorphic sensors, especially those focused towards image processing. Our research focuses on examining mechanisms of motion processing in the retina. We are specifically interested in detection of moving targets under challenging conditions, specifically small or low-contrast (dim) targets amidst high quantities of clutter or distractor signals. In this paper we compare a classic motion-sensitive cell model, the Hassenstein-Reichardt model, to a model of the OMS (object motion-sensitive) cell, that relies primarily on change-detection, and describe scenarios for which each model is better suited. We also examine mechanisms, inspired by features of retinal circuitry, by which performance may be enhanced. For example, lateral inhibition (mediated by amacrine cells) conveys selectivity for small targets to the W3 ganglion cell --- we demonstrate that a similar mechanism can be combined with the previously mentioned motion-processing cell models to select small moving targets for further processing.","PeriodicalId":225636,"journal":{"name":"Proceedings of the International Conference on Neuromorphic Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123609603","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: 0

Sparse Coding Enables the Reconstruction of High-Fidelity Images and Video from Retinal Spike Trains 稀疏编码能够重建高保真图像和视频从视网膜尖峰列车

Proceedings of the International Conference on Neuromorphic Systems Pub Date : 2018-07-23 DOI: 10.1145/3229884.3229892

Y. Watkins, A. Thresher, D. Mascarenas, Garrett T. Kenyon

{"title":"Sparse Coding Enables the Reconstruction of High-Fidelity Images and Video from Retinal Spike Trains","authors":"Y. Watkins, A. Thresher, D. Mascarenas, Garrett T. Kenyon","doi":"10.1145/3229884.3229892","DOIUrl":"https://doi.org/10.1145/3229884.3229892","url":null,"abstract":"The optic nerve transmits visual information to the brain as trains of discrete events, a low-power, low-bandwidth communication channel also exploited by silicon retina cameras. Extracting high-fidelity visual input from retinal event trains is thus a key challenge for both computational neuroscience and neuromorphic engineering. Here, we investigate whether sparse coding can enable the reconstruction of high-fidelity images and video from retinal event trains. Our approach is analogous to compressive sensing, in which only a random subset of pixels are transmitted and the missing information is estimated via inference. We employed a variant of the Locally Competitive Algorithm to infer sparse representations from retinal event trains, using a dictionary of convolutional features optimized via stochastic gradient descent and trained in an unsupervised manner using a local Hebbian learning rule with momentum. We used an anatomically realistic retinal model with stochastic graded release from cones and bipolar cells to encode thumbnail images as spike trains arising from ON and OFF retinal ganglion cells. The spikes from each model ganglion cell were summed over a 32 msec time window, yielding a noisy rate-coded image. Analogous to how the primary visual cortex is postulated to infer features from noisy spike trains arising from the optic nerve, we inferred a higher-fidelity sparse reconstruction from the noisy rate-coded image using a convolutional dictionary trained on the original CIFAR10 database. To investigate whether a similar approach works on non-stochastic data, we demonstrate that the same procedure can be used to reconstruct high-frequency video from the asynchronous events arising from a silicon retina camera moving through a laboratory environment.","PeriodicalId":225636,"journal":{"name":"Proceedings of the International Conference on Neuromorphic Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127950338","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: 13

Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system 网格机器人:由模拟大脑导航系统的脉冲神经网络控制的自主机器人

Proceedings of the International Conference on Neuromorphic Systems Pub Date : 2018-07-05 DOI: 10.1145/3229884.3229888

Guangzhi Tang, K. Michmizos

{"title":"Gridbot: An autonomous robot controlled by a Spiking Neural Network mimicking the brain's navigational system","authors":"Guangzhi Tang, K. Michmizos","doi":"10.1145/3229884.3229888","DOIUrl":"https://doi.org/10.1145/3229884.3229888","url":null,"abstract":"It is true that the \"best\" neural network is not necessarily the one with the most \"brain-like\" behavior. Understanding biological intelligence, however, is a fundamental goal for several distinct disciplines. Translating our understanding of intelligence to machines is a fundamental problem in robotics. Propelled by new advancements in Neuroscience, we developed a spiking neural network (SNN) that draws from mounting experimental evidence that a number of individual neurons is associated with spatial navigation. By following the brain's structure, our model assumes no initial all-to-all connectivity, which could inhibit its translation to a neuromorphic hardware, and learns an uncharted territory by mapping its identified components into a limited number of neural representations, through spike-timing dependent plasticity (STDP). In our ongoing effort to employ a bioinspired SNN-controlled robot to real-world spatial mapping applications, we demonstrate here how an SNN may robustly control an autonomous robot in mapping and exploring an unknown environment, while compensating for its own intrinsic hardware imperfections, such as partial or total loss of visual input.","PeriodicalId":225636,"journal":{"name":"Proceedings of the International Conference on Neuromorphic Systems","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131857757","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: 33

A Neural-Astrocytic Network Architecture: Astrocytic calcium waves modulate synchronous neuronal activity 神经-星形细胞网络结构:星形细胞钙波调节同步神经元活动

Proceedings of the International Conference on Neuromorphic Systems Pub Date : 2018-07-05 DOI: 10.1145/3229884.3229890

Ioannis E. Polykretis, Vladimir A. Ivanov, K. Michmizos

{"title":"A Neural-Astrocytic Network Architecture: Astrocytic calcium waves modulate synchronous neuronal activity","authors":"Ioannis E. Polykretis, Vladimir A. Ivanov, K. Michmizos","doi":"10.1145/3229884.3229890","DOIUrl":"https://doi.org/10.1145/3229884.3229890","url":null,"abstract":"Understanding the role of astrocytes in brain computation is a nascent challenge, promising immense rewards, in terms of new neurobiological knowledge that can be translated into artificial intelligence. In our ongoing effort to identify principles endowing the astrocyte with unique functions in brain computation, and translate them into neural-astrocytic networks (NANs), we propose a biophysically realistic model of an astrocyte that preserves the experimentally observed spatial allocation of its distinct subcellular compartments. We show how our model may encode, and modulate, the extent of synchronous neural activity via calcium waves that propagate intracellularly across the astrocytic compartments. This relationship between neural activity and astrocytic calcium waves has long been speculated but it is still lacking a mechanistic explanation. Our model suggests an astrocytic \"calcium cascade\" mechanism for neuronal synchronization, which may empower NANs by imposing periodic neural modulation known to reduce coding errors. By expanding our notions of information processing in astrocytes, our work aims to solidify a computational role for non-neuronal cells and incorporate them into artificial networks.","PeriodicalId":225636,"journal":{"name":"Proceedings of the International Conference on Neuromorphic Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129075473","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: 13