A Bayesian Model of Polychronicity

2014 ◽  
Vol 26 (9) ◽  
pp. 2052-2073 ◽  
Author(s):  
Mira Guise ◽  
Alistair Knott ◽  
Lubica Benuskova
Keyword(s):  
Neural Networks ◽  
Spiking Neural Networks ◽  
Familiar Stimulus ◽  
Significant Feature ◽  
Neural Basis ◽  
Representational System ◽  
Large Numbers ◽  
Strongly Connected ◽  
Activation Response ◽  
First Time

A significant feature of spiking neural networks with varying connection delays, such as those in the brain, is the existence of strongly connected groups of neurons known as polychronous neural groups (PNGs). Polychronous groups are found in large numbers in these networks and are proposed by Izhikevich ( 2006a ) to provide a neural basis for representation and memory. When exposed to a familiar stimulus, spiking neural networks produce consistencies in the spiking output data that are the hallmarks of PNG activation. Previous methods for studying the PNG activation response to stimuli have been limited by the template-based methods used to identify PNG activation. In this letter, we outline a new method that overcomes these difficulties by establishing for the first time a probabilistic interpretation of PNG activation. We then demonstrate the use of this method by investigating the claim that PNGs might provide the foundation of a representational system.

scholarly journals Event-based backpropagation can compute exact gradients for spiking neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Timo C. Wunderlich ◽  
Christian Pehle
Keyword(s):  
Neural Networks ◽  
Loss Function ◽  
Spiking Neural Networks ◽  
Backpropagation Algorithm ◽  
General Loss Function ◽  
Gradient Based ◽  
Rigorous Study ◽  
Spiking Networks ◽  
Event Based ◽  
First Time

AbstractSpiking neural networks combine analog computation with event-based communication using discrete spikes. While the impressive advances of deep learning are enabled by training non-spiking artificial neural networks using the backpropagation algorithm, applying this algorithm to spiking networks was previously hindered by the existence of discrete spike events and discontinuities. For the first time, this work derives the backpropagation algorithm for a continuous-time spiking neural network and a general loss function by applying the adjoint method together with the proper partial derivative jumps, allowing for backpropagation through discrete spike events without approximations. This algorithm, EventProp, backpropagates errors at spike times in order to compute the exact gradient in an event-based, temporally and spatially sparse fashion. We use gradients computed via EventProp to train networks on the Yin-Yang and MNIST datasets using either a spike time or voltage based loss function and report competitive performance. Our work supports the rigorous study of gradient-based learning algorithms in spiking neural networks and provides insights toward their implementation in novel brain-inspired hardware.

scholarly journals An artificial spiking afferent nerve based on Mott memristors for neurorobotics

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Xumeng Zhang ◽  
Ye Zhuo ◽  
Qing Luo ◽  
Zuheng Wu ◽  
Rivu Midya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Complementary Metal Oxide Semiconductor ◽  
Tactile Sensor ◽  
Metal Oxide Semiconductor ◽  
Afferent Nerve ◽  
Spiking Neural Networks ◽  
Oxide Semiconductor ◽  
Semiconductor Technology ◽  
Hardware Implementations ◽  
First Time

AbstractNeuromorphic computing based on spikes offers great potential in highly efficient computing paradigms. Recently, several hardware implementations of spiking neural networks based on traditional complementary metal-oxide semiconductor technology or memristors have been developed. However, an interface (called an afferent nerve in biology) with the environment, which converts the analog signal from sensors into spikes in spiking neural networks, is yet to be demonstrated. Here we propose and experimentally demonstrate an artificial spiking afferent nerve based on highly reliable NbOx Mott memristors for the first time. The spiking frequency of the afferent nerve is proportional to the stimuli intensity before encountering noxiously high stimuli, and then starts to reduce the spiking frequency at an inflection point. Using this afferent nerve, we further build a power-free spiking mechanoreceptor system with a passive piezoelectric device as the tactile sensor. The experimental results indicate that our afferent nerve is promising for constructing self-aware neurorobotics in the future.

A Developmental Method for Evolving Large-Scale Spiking Neural Networks

2012 ◽  
Vol 35 (12) ◽  
pp. 2633 ◽  
Author(s):  
Xiang-Hong LIN ◽  
Tian-Wen ZHANG ◽  
Gui-Cang ZHANG
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Spiking Neural Networks

scholarly journals Spiking Neural Networks and online learning: An overview and perspectives

2020 ◽  
Vol 121 ◽  
pp. 88-100 ◽  
Author(s):  
Jesus L. Lobo ◽  
Javier Del Ser ◽  
Albert Bifet ◽  
Nikola Kasabov
Keyword(s):  
Neural Networks ◽  
Online Learning ◽  
Spiking Neural Networks

scholarly journals Symmetry perception with spiking neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jonathan K. George ◽  
Cesare Soci ◽  
Mario Miscuglio ◽  
Volker J. Sorger
Keyword(s):  
Neural Networks ◽  
Mirror Symmetry ◽  
Satellite Image ◽  
Image Data ◽  
Coincidence Detection ◽  
Spiking Neural Networks ◽  
Feed Forward Neural Network ◽  
Neuromorphic Computing ◽  
Symmetry Perception ◽  
High Level

AbstractMirror symmetry is an abundant feature in both nature and technology. Its successful detection is critical for perception procedures based on visual stimuli and requires organizational processes. Neuromorphic computing, utilizing brain-mimicked networks, could be a technology-solution providing such perceptual organization functionality, and furthermore has made tremendous advances in computing efficiency by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation. The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. More importantly it enables a technology pathway in bridging the gap between the low-level incoming sensor stimuli and high-level interpretation of these inputs as recognized objects and scenes in the world.

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