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Toward One-Shot Learning in Neuroscience-Inspired Deep Spiking Neural Networks

https://doi.org/10.1101/829556
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-662-57715-8_12openalex-snapshot
Audio- and Visual Information Processing in the Brain and Its Modelling with Evolving SNN
inferredcites →https://doi.org/10.1007/s12035-015-9280-5openalex-snapshot
Emerging Trends in Retrograde Signaling
inferredcites →https://doi.org/10.1016/j.bandc.2018.09.007openalex-snapshot
A network view on brain regions involved in experts’ object and pattern recognition: Implications for the neural mechanisms of skilled visual perception
inferredcites →https://doi.org/10.1016/j.neunet.2016.11.002openalex-snapshot
A computational model of conditioning inspired by Drosophila olfactory system
inferredcites →https://doi.org/10.1016/j.neunet.2018.03.019openalex-snapshot
Spiking neural networks for handwritten digit recognition—Supervised learning and network optimization
inferredcites →https://doi.org/10.1016/j.neunet.2018.12.002openalex-snapshot
Deep learning in spiking neural networks
inferredcites →https://doi.org/10.1016/j.neuron.2017.06.011openalex-snapshot
Neuroscience-Inspired Artificial Intelligence
inferredcites →https://doi.org/10.1016/j.neuron.2018.09.040openalex-snapshot
Towards an Understanding of Synapse Formation
inferredcites →https://doi.org/10.1016/j.tics.2017.09.012openalex-snapshot
Network Design and the Brain
inferredcites →https://doi.org/10.1016/j.tics.2018.12.005openalex-snapshot
Theories of Error Back-Propagation in the Brain
inferredcites →https://doi.org/10.1016/s0925-2312(01)00658-0openalex-snapshot
Error-backpropagation in temporally encoded networks of spiking neurons
inferredcites →https://doi.org/10.1038/14819openalex-snapshot
Hierarchical models of object recognition in cortex
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1038/ncomms13033openalex-snapshot
Analogue modulation of back-propagating action potentials enables dendritic hybrid signalling
inferredcites →https://doi.org/10.1038/nmeth.4549openalex-snapshot
Machine learning in neuroscience
inferredcites →https://doi.org/10.1109/jproc.2014.2346153openalex-snapshot
Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output
inferredcites →https://doi.org/10.1109/msp.2011.943660openalex-snapshot
Bridging Social and Data Networks [Applications Corner]
inferredcites →https://doi.org/10.1109/msp.2012.2211477openalex-snapshot
The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
inferredcites →https://doi.org/10.1109/tcds.2019.2897618openalex-snapshot
Combined Sensing, Cognition, Learning, and Control for Developing Future Neuro-Robotics Systems: A Survey
inferredcites →https://doi.org/10.1109/tnnls.2019.2919662openalex-snapshot
Training Spiking Neural Networks for Cognitive Tasks: A Versatile Framework Compatible With Various Temporal Codes
inferredcites →https://doi.org/10.1126/science.1202529openalex-snapshot
Synaptic Pruning by Microglia Is Necessary for Normal Brain Development
inferredcites →https://doi.org/10.1126/science.aau6595openalex-snapshot
Using neuroscience to develop artificial intelligence
inferredcites →https://doi.org/10.1152/jn.00037.2018openalex-snapshot
Primary sensorimotor cortex exhibits complex dependencies of spike-field coherence on neuronal firing rates, field power, and behavior
inferredcites →https://doi.org/10.1155/2019/4182639openalex-snapshot
Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems
inferredcites →https://doi.org/10.1162/089976698300017124openalex-snapshot
Synaptic Pruning in Development: A Computational Account
inferredcites →https://doi.org/10.1162/neco_a_00052openalex-snapshot
Deep, Big, Simple Neural Nets for Handwritten Digit Recognition
inferredcites →https://doi.org/10.1186/s40537-014-0007-7openalex-snapshot
Deep learning applications and challenges in big data analytics
inferredcites →https://doi.org/10.1371/journal.pcbi.1003024openalex-snapshot
Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons
inferredcites →https://doi.org/10.14569/ijarai.2015.040701openalex-snapshot
A Minimal Spiking Neural Network to Rapidly Train and Classify Handwritten Digits in Binary and 10-Digit Tasks
inferredcites →https://doi.org/10.1523/jneurosci.2152-06.2006openalex-snapshot
Background Synaptic Activity Is Sparse in Neocortex
inferredcites →https://doi.org/10.17509/ijost.v3i1.10795openalex-snapshot
Handwritten Digit Recognition Using Machine Learning Algorithms
inferredcites →https://doi.org/10.3389/fnbot.2017.00063openalex-snapshot
Combined Computational Systems Biology and Computational Neuroscience Approaches Help Develop of Future “Cognitive Developmental Robotics”
inferredcites →https://doi.org/10.3389/fncom.2015.00099openalex-snapshot
Unsupervised learning of digit recognition using spike-timing-dependent plasticity
inferredcites →https://doi.org/10.3389/fncom.2018.00046openalex-snapshot
Event-Based, Timescale Invariant Unsupervised Online Deep Learning With STDP
inferredcites →https://doi.org/10.3389/fnins.2016.00508openalex-snapshot
Training Deep Spiking Neural Networks Using Backpropagation
inferredcites →https://doi.org/10.3389/fnins.2018.00435openalex-snapshot
Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning
inferredcites →https://doi.org/10.3389/fnins.2018.00774openalex-snapshot
Deep Learning With Spiking Neurons: Opportunities and Challenges
inferredcites →https://doi.org/10.3389/fnsys.2015.00042openalex-snapshot
A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia
inferredcites →https://doi.org/10.4161/cib.1.2.7058openalex-snapshot
Back-propagating action potential
inferredcites →https://doi.org/10.48550/arxiv.1803.09574openalex-snapshot
Long short-term memory and learning-to-learn in networks of spiking neurons
inferredcites →https://doi.org/10.7554/elife.22901openalex-snapshot
Towards deep learning with segregated dendrites
inferredcites →https://doi.org/10.7554/elife.37349openalex-snapshot
Sparse recurrent excitatory connectivity in the microcircuit of the adult mouse and human cortex

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s10462-021-10130-zopenalex-snapshot
Brain-inspired models for visual object recognition: an overview
inferred← citeshttps://doi.org/10.1016/j.neucom.2020.07.109openalex-snapshot
An ensemble unsupervised spiking neural network for objective recognition
inferred← citeshttps://doi.org/10.1109/icmla52953.2021.00132openalex-snapshot
Resiliency of SNN on Black-Box Adversarial Attacks
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