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Found 28 papers, 9 papers with code
How can neuromorphic hardware attain brain-like functional capabilities?
But current architectures and training methods for networks of spiking neurons in NMHW are largely copied from artificial neural networks.
A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware
Spike-based neuromorphic hardware holds the promise to provide more energy efficient implementations of Deep Neural Networks (DNNs) than standard hardware such as GPUs.
Pairing Conceptual Modeling with Machine Learning
We then examine how conceptual modeling can be applied to machine learning and propose a framework for incorporating conceptual modeling into data science projects.
Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent neural networks.
Online Spatio-Temporal Learning in Deep Neural Networks
For shallow networks, OSTL is gradient-equivalent to BPTT enabling for the first time online training of SNNs with BPTT-equivalent gradients.
Embodied Synaptic Plasticity with Online Reinforcement learning
We demonstrate this framework to evaluate Synaptic Plasticity with Online REinforcement learning (SPORE), a reward-learning rule based on synaptic sampling, on two visuomotor tasks: reaching and lane following.
Optimized spiking neurons classify images with high accuracy through temporal coding with two spikes
Spike-based neuromorphic hardware promises to reduce the energy consumption of image classification and other deep learning applications, particularly on mobile phones or other edge devices.
Recognizing Images with at most one Spike per Neuron
We introduce a new conversion method where a gate in the ANN, which can basically be of any type, is emulated by a small circuit of spiking neurons, with At Most One Spike (AMOS) per neuron.
Reservoirs learn to learn
We wondered whether the performance of liquid state machines can be improved if the recurrent weights are chosen with a purpose, rather than randomly.
Eligibility traces provide a data-inspired alternative to backpropagation through time
Learning in recurrent neural networks (RNNs) is most often implemented by gradient descent using backpropagation through time (BPTT), but BPTT does not model accurately how the brain learns.
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources.
Neuromorphic Hardware learns to learn
In contrast, the hyperparameters and learning algorithms of networks of neurons in the brain, which they aim to emulate, have been optimized through extensive evolutionary and developmental processes for specific ranges of computing and learning tasks.
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets
This lack of understanding is linked to a lack of learning algorithms for recurrent networks of spiking neurons (RSNNs) that are both functionally powerful and can be implemented by known biological mechanisms.
Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons
We give an application to recovering assemblies of neurons.
Long short-term memory and learning-to-learn in networks of spiking neurons
Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and learning capabilities of the brain.
Ranked #22 on
Speech Recognition
on TIMIT
Deep Rewiring: Training very sparse deep networks
Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them.
A dynamic connectome supports the emergence of stable computational function of neural circuits through reward-based learning
These data are inconsistent with common models for network plasticity, and raise the questions how neural circuits can maintain a stable computational function in spite of these continuously ongoing processes, and what functional uses these ongoing processes might have.
Pattern representation and recognition with accelerated analog neuromorphic systems
Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks.
Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System
In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate.
CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling
Our theoretical analysis shows that stochastic search could in principle even attain optimal network configurations by emulating one of the most well-known nonlinear optimization methods, simulated annealing.
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring
We reexamine in this article the conceptual and mathematical framework for understanding the organization of plasticity in spiking neural networks.
Network Plasticity as Bayesian Inference
General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference.
A theoretical basis for efficient computations with noisy spiking neurons
Furthermore, one can demonstrate for the Traveling Salesman Problem a surprising computational advantage of networks of spiking neurons compared with traditional artificial neural networks and Gibbs sampling.
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning
It was recently shown that tuning properties of neurons in monkey motor cortex are adapted selectively in order to compensate for an erroneous interpretation of their activity.
Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks
Many models for computations in recurrent networks of neurons assume that the network state moves from some initial state to some fixed point attractor or limit cycle that represents the output of the computation.
STDP enables spiking neurons to detect hidden causes of their inputs
We show here that STDP, in conjunction with a stochastic soft winner-take-all (WTA) circuit, induces spiking neurons to generate through their synaptic weights implicit internal models for subclasses (or causes") of the high-dimensional spike patterns of hundreds of pre-synaptic neurons.
Hebbian Learning of Bayes Optimal Decisions
Uncertainty is omnipresent when we perceive or interact with our environment, and the Bayesian framework provides computational methods for dealing with it.
Simplified Rules and Theoretical Analysis for Information Bottleneck Optimization and PCA with Spiking Neurons
We show that under suitable assumptions (primarily linearization) a simple and perspicuous online learning rule for Information Bottleneck optimization with spiking neurons can be derived.
