Top 20 spiking neural network in 2022

Author: en.wikipedia.org

Date Submitted: 07/18/2021 06:05 PM

Average star voting: 4 ⭐ ( 43865 reviews)

Summary: Among many artificial neural networks, the research on Spike Neural Network (SNN), which mimics the energy-efficient signal system in the brain, is drawing much attention. Memristor is a promising candidate as a synaptic component for hardware implementation of SNN, but several non-ideal device properties are making it challengeable. In this work, we conducted an SNN simulation by adding a device model with a non-linear weight update to test the impact on SNN performance. We found that SNN has a strong tolerance for the device non-linearity and the network can keep the accuracy high if a device meets one of the two conditions: 1. symmetric long-term potentiation (LTP) and long-term depression (LTD) curves and 2. positive non-linearity factors for both LTP and LTD. The reason was analyzed in terms of the balance between network parameters as well as the variability of weight. The results are considered to be a piece of useful prior information for the future implementation of emerging device-based neuromorphic hardware.

Match with the search results: Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. … In addition to neuronal and synaptic state, ……. read more

Author: www.frontiersin.org

Date Submitted: 02/10/2019 05:10 AM

Average star voting: 3 ⭐ ( 85762 reviews)

Summary: Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are communicated and processed in a massively parallel fashion. SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, and event-driven information processing. This makes them interesting candidates for the efficient implementation of deep neural networks, the method of choice for many machine learning tasks. In this review, we address the opportunities that deep spiking networks offer and investigate in detail the challenges associated with training SNNs in a way that makes them competitive with conventional deep learning, but simultaneously allows for efficient mapping to hardware. A wide range of training methods for SNNs is presented, ranging from the conversion of conventional deep networks into SNNs, constrained training before conversion, spiking variants of backpropagation, and biologically motivated variants of STDP. The goal of our review is to define a categorization of SNN training methods, and summarize their advantages and drawbacks. We further discuss relationships between SNNs and binary networks, which are becoming popular for efficient digital hardware implementation. Neuromorphic hardware platforms have great potential to enable deep spiking networks in real-world applications. We compare the suitability of various neuromorphic systems that have been developed over the past years, and investigate potential use cases. Neuromorphic approaches and conventional machine learning should not be considered simply two solutions to the same classes of problems, instead it is possible to identify and exploit their task-specific advantages. Deep SNNs offer great opportunities to work with new types of event-based sensors, exploit temporal codes and local on-chip learning, and we have so far just scratched the surface of realizing these advantages in practical applications.

Match with the search results: Introduction to spiking neural networks: Information ……. read more

Author: www.frontiersin.org

Date Submitted: 07/13/2021 05:51 AM

Average star voting: 4 ⭐ ( 66334 reviews)

Summary:

Match with the search results: A spiking neural network (SNN) is an artificial neural network constructed using the knowledge observed in biology, in which neurons communicate ……. read more

Author: www.ncbi.nlm.nih.gov

Date Submitted: 05/13/2019 09:41 AM

Average star voting: 5 ⭐ ( 93566 reviews)

Summary: Nowadays, Deep Learning (DL) is a hot topic within the Data Science community. Despite being quite effective in various tasks across the industries Deep

Match with the search results: Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are ……. read more

Author: cnvrg.io

Date Submitted: 06/03/2019 10:19 AM

Average star voting: 5 ⭐ ( 80676 reviews)

Summary: Artificial neural networks that closely mimic natural neural networks are known as spiking neural networks (SNNs). In addition to neuronal and synaptic status, SNNs incorporate time into their working model.

Match with the search results: In SNNs, such as in biological neural networks, neurons communicate with each other with discrete electrical signals called spikes and work in ……. read more

Author: analyticsindiamag.com

Date Submitted: 09/15/2019 06:30 PM

Average star voting: 4 ⭐ ( 39573 reviews)

Summary: By emulating biological features in brain, Spiking Neural Networks (SNNs) offer an energy-efficient alternative to conventional deep learning. To make SNNs ubiquitous, a ‘visual explanation’ technique for analysing and explaining the internal spike behavior of such temporal deep SNNs is crucial. Explaining SNNs visually will make the network more transparent giving the end-user a tool to understand how SNNs make temporal predictions and why they make a certain decision. In this paper, we propose a bio-plausible visual explanation tool for SNNs, called Spike Activation Map (SAM). SAM yields a heatmap (i.e., localization map) corresponding to each time-step of input data by highlighting neurons with short inter-spike interval activity. Interestingly, without the use of gradients and ground truth, SAM produces a temporal localization map highlighting the region of interest in an image attributed to an SNN’s prediction at each time-step. Overall, SAM outsets the beginning of a new research area ‘explainable neuromorphic computing’ that will ultimately allow end-users to establish appropriate trust in predictions from SNNs.

Match with the search results: The first scientific model of a Spiking Neural Network was proposed by Alan Hodgkin and Andrew Huxley in 1952. The model described biological neurons’ action ……. read more

Author: www.nature.com

Date Submitted: 02/26/2020 01:10 AM

Average star voting: 3 ⭐ ( 19813 reviews)

Summary: Spiking neural networks become robust to various perturbations of their parameters if their voltages are confined to a lower-dimensional subspace, and both dynamics and robustness can be visualised in this voltage subspace.

Match with the search results: A spiking neural network is a two-layered feed-forward network with lateral connections in the second hidden layer that is heterogeneous in ……. read more

Author: arxiv.org

Date Submitted: 05/22/2019 09:28 AM

Average star voting: 4 ⭐ ( 57002 reviews)

Summary: Everyone who has been remotely tuned in to recent progress in machine learning has heard of the current 2nd generation artificial neural networks used for machine learning. These are generally fully…

Match with the search results: Spiking Neural Networks (SNNs)6,7,8,9,10,11 offer an alternative and bio-plausible manner for enabling low-power intelligence. SNNs emulate ……. read more

Author: elifesciences.org

Date Submitted: 04/01/2020 06:27 PM

Average star voting: 3 ⭐ ( 53248 reviews)

Summary: by Anil Ananthaswamy (Simons Institute Science Communicator in Residence)

Match with the search results: Spike trains in a network of spiking neurons are propagated through synaptic connections. A synapse can be either excitatory, which increases the neuron’s….. read more

Author: towardsdatascience.com

Date Submitted: 08/09/2019 08:29 AM

Average star voting: 5 ⭐ ( 31308 reviews)

Summary: Discorver how to formulate and train Spiking Neural Networks (SNNs) using the LIF model, and how to encode data so that it can be processed by SNNs

Match with the search results: Spiking neural networks become robust to various perturbations of their parameters if their voltages are confined to a lower-dimensional ……. read more

Author: simons.berkeley.edu

Date Submitted: 12/24/2021 05:04 PM

Average star voting: 3 ⭐ ( 72672 reviews)

Summary:

Match with the search results: Spiking neural networks (SNNs) are artificial neural networks that more closely mimic natural neural networks. … In addition to neuronal and synaptic state, ……. read more

Author: theaisummer.com

Date Submitted: 09/06/2020 10:36 AM

Average star voting: 3 ⭐ ( 83994 reviews)

Summary: Deep neural networks with rate-based neurons have exhibited tremendous progress in the last decade. However, the same level of progress has not been observed in research on spiking neural networks (SNN), despite their capability to handle temporal data, energy-efficiency and low latency. This could be because the benchmarking techniques for SNNs are based on the methods used for evaluating deep neural networks, which do not provide a clear evaluation of the capabilities of SNNs. Particularly, the benchmarking of SNN approaches with regards to energy efficiency and latency requires realization in suitable hardware, which imposes additional temporal and resource constraints upon ongoing projects. This review aims to provide an overview of the current real-world applications of SNNs and identifies steps to accelerate research involving SNNs in the future.

Match with the search results: Introduction to spiking neural networks: Information ……. read more

Author: www.sciencedirect.com

Date Submitted: 03/02/2019 03:04 AM

Average star voting: 5 ⭐ ( 27766 reviews)

Summary: Training a Spiking Neural Network with Equilibrium PropagationPeter O’Connor, Efstratios Gavves, Max WellingBackpropagation is almost universally u…

Match with the search results: A spiking neural network (SNN) is an artificial neural network constructed using the knowledge observed in biology, in which neurons communicate ……. read more

Author: www.worldscientific.com

Date Submitted: 04/29/2019 04:11 PM

Average star voting: 3 ⭐ ( 11988 reviews)

Summary:

Match with the search results: Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are ……. read more

Author: www.mdpi.com

Date Submitted: 11/15/2020 01:13 PM

Average star voting: 3 ⭐ ( 84230 reviews)

Summary: A spiking neural network simulation library. Contribute to michaelmelanson/spiking-neural-net development by creating an account on GitHub.

Match with the search results: In SNNs, such as in biological neural networks, neurons communicate with each other with discrete electrical signals called spikes and work in ……. read more

Author: proceedings.mlr.press

Date Submitted: 03/10/2020 09:48 PM

Average star voting: 3 ⭐ ( 56897 reviews)

Summary: Artificial Neural Networks (ANNs) are based on highly simplified brain dynamics and have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. Throughout their development, ANNs have been…

Match with the search results: The first scientific model of a Spiking Neural Network was proposed by Alan Hodgkin and Andrew Huxley in 1952. The model described biological neurons’ action ……. read more

Author: ieeexplore.ieee.org

Date Submitted: 05/30/2021 08:42 AM

Average star voting: 4 ⭐ ( 13353 reviews)

Summary: Author summary Artificial neural networks can achieve superhuman performance in many domains. Despite these advances, these networks fail in sequential learning; they achieve optimal performance on newer tasks at the expense of performance on previously learned tasks. Humans and animals on the other hand have a remarkable ability to learn continuously and incorporate new data into their corpus of existing knowledge. Sleep has been hypothesized to play an important role in memory and learning by enabling spontaneous reactivation of previously learned memory patterns. Here we use a spiking neural network model, simulating sensory processing and reinforcement learning in animal brain, to demonstrate that interleaving new task training with sleep-like activity optimizes the network’s memory representation in synaptic weight space to prevent forgetting old memories. Sleep makes this possible by replaying old memory traces without the explicit usage of the old task data.

Match with the search results: A spiking neural network is a two-layered feed-forward network with lateral connections in the second hidden layer that is heterogeneous in ……. read more

Author: snntorch.readthedocs.io

Date Submitted: 11/13/2020 08:57 PM

Average star voting: 4 ⭐ ( 20316 reviews)

Summary:

Match with the search results: Spiking Neural Networks (SNNs)6,7,8,9,10,11 offer an alternative and bio-plausible manner for enabling low-power intelligence. SNNs emulate ……. read more

Author: www.youtube.com

Date Submitted: 04/27/2021 08:14 PM

Average star voting: 4 ⭐ ( 45407 reviews)

Summary:

Match with the search results: Spike trains in a network of spiking neurons are propagated through synaptic connections. A synapse can be either excitatory, which increases the neuron’s….. read more

Author: norse.github.io

Date Submitted: 06/09/2021 05:02 PM

Average star voting: 5 ⭐ ( 65820 reviews)

Summary:

Match with the search results: Spiking neural networks become robust to various perturbations of their parameters if their voltages are confined to a lower-dimensional ……. read more