[HTML][HTML] Roadmap to neuromorphic computing with emerging technologies
The growing adoption of data-driven applications, such as artificial intelligence (AI), is
transforming the way we interact with technology. Currently, the deployment of AI and …
transforming the way we interact with technology. Currently, the deployment of AI and …
A Fixed-Time Projection Neural Network for Solving L₁-Minimization Problem
In this article, a new projection neural network (PNN) for solving-minimization problem is
proposed, which is based on classic PNN and sliding mode control technique. Furthermore …
proposed, which is based on classic PNN and sliding mode control technique. Furthermore …
In-memory analog solution of compressed sensing recovery in one step
S Wang, Y Luo, P Zuo, L Pan, Y Li, Z Sun - Science Advances, 2023 - science.org
Modern analog computing, by gaining momentum from nonvolatile resistive memory
devices, deals with matrix computations. In-memory analog computing has been …
devices, deals with matrix computations. In-memory analog computing has been …
Visual nonclassical receptive field effects emerge from sparse coding in a dynamical system
M Zhu, CJ Rozell - PLoS computational biology, 2013 - journals.plos.org
Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear
response properties to stimuli within their classical receptive field (CRF) and receive …
response properties to stimuli within their classical receptive field (CRF) and receive …
Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices
The number of neurons in mammalian cortex varies by multiple orders of magnitude across
different species. In contrast, the ratio of excitatory to inhibitory neurons (E: I ratio) varies in a …
different species. In contrast, the ratio of excitatory to inhibitory neurons (E: I ratio) varies in a …
Dynamic Filtering of Time-Varying Sparse Signals via Minimization
AS Charles, A Balavoine… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Despite the importance of sparsity signal models and the increasing prevalence of high-
dimensional streaming data, there are relatively few algorithms for dynamic filtering of …
dimensional streaming data, there are relatively few algorithms for dynamic filtering of …
Optimal sparse approximation with integrate and fire neurons
Sparse approximation is a hypothesized coding strategy where a population of sensory
neurons (eg V1) encodes a stimulus using as few active neurons as possible. We present …
neurons (eg V1) encodes a stimulus using as few active neurons as possible. We present …
Dynamical sparse signal recovery with fixed-time convergence
Arising in a large number of application areas, sparse recovery (SR) has been exhaustively
investigated and many algorithms have been proposed. Different from the numerical …
investigated and many algorithms have been proposed. Different from the numerical …
Convergence speed of a dynamical system for sparse recovery
A Balavoine, CJ Rozell… - IEEE transactions on signal …, 2013 - ieeexplore.ieee.org
This paper studies the convergence rate of a continuous-time dynamical system for l 1-
minimization, known as the Locally Competitive Algorithm (LCA). Solving l 1-minimization …
minimization, known as the Locally Competitive Algorithm (LCA). Solving l 1-minimization …
Opportunities in physical computing driven by analog realization
J Hasler - 2016 IEEE international conference on rebooting …, 2016 - ieeexplore.ieee.org
In the past, discussions on the capability of analog or physical computing were only of
theoretical interest. Digital computation's 80 year history starts from the Turings original …
theoretical interest. Digital computation's 80 year history starts from the Turings original …