منابع مشابه
Finding a roadmap to achieve large neuromorphic hardware systems
Neuromorphic systems are gaining increasing importance in an era where CMOS digital computing techniques are reaching physical limits. These silicon systems mimic extremely energy efficient neural computing structures, potentially both for solving engineering applications as well as understanding neural computation. Toward this end, the authors provide a glimpse at what the technology evolution...
متن کاملMethods for applying the Neural Engineering Framework to neuromorphic hardware
We review our current software tools and theoretical methods for applying the Neural Engineering Framework to state-of-the-art neuromorphic hardware. These methods can be used to implement linear and nonlinear dynamical systems that exploit axonal transmission time-delays, and to fully account for nonideal mixed-analog-digital synapses that exhibit higher-order dynamics with heterogeneous time-...
متن کاملUsing Games to Embody Spiking Neural Networks for Neuromorphic Hardware
Adding value to action-selection through reinforcement-learning provides a mechanism for modifying future decisions of real and artificial entities. This behavioral-level modulation is vital for performing in complex and dynamic environments. In this paper we focus on three classes of biologically inspired feed-forward spiking neural networks capable of action-selection via reinforcement-learni...
متن کاملTraining Spiking Deep Networks for Neuromorphic Hardware
We describe a method to train spiking deep networks that can be run using leaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for spiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012 benchmark. Our method for transforming deep artificial neural networks into spiking networks is scalable and works with a wide range of neural nonlinearities. We ac...
متن کاملNeuromorphic Hardware As Database Co-Processors
Today’s databases excel at processing data using fairly simple operators but are not efficient at executing operators which include pattern matching, speech recognition or other cognitive tasks. The only way to use such operators in data processing today is to simulate spiking neural networks. Neuromorphic hardware is supposed to become ubiquitous in complementing traditional computational infr...
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ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2019
ISSN: 1662-453X
DOI: 10.3389/fnins.2019.00483