4 citations · 8 across the 7 of their papers we have counts for
7 papers
Benchmarking local Hebbian learning rules for memory storage and prototype extraction
Anders Lansner, Andreas Knoblauch, Naresh B Ravichandran +1
Associative memory or content-addressable memory is an important component function in computer science and information processing, and at the same time a key concept in cognitive…
Dynamic Heuristic Neuromorphic Solver for the Edge User Allocation Problem with Bayesian Confidence Propagation Neural Network
Kecheng Zhang, Anders Lansner, Ahsan Javed Awan +2
We propose a neuromorphic solver for the NP-hard Edge User Allocation problem using an attractor network with Winner-Takes-All (WTA) mechanism implemented with the Bayesian Confide…
Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference
Muhammad Ihsan Al Hafiz, Naresh Ravichandran, Anders Lansner +2
Edge AI applications increasingly require models that can learn and adapt on-device with minimal energy budget. Traditional deep learning models, while powerful, are often overpara…
A Reconfigurable Stream-Based FPGA Accelerator for Bayesian Confidence Propagation Neural Networks
Muhammad Ihsan Al Hafiz, Naresh Ravichandran, Anders Lansner +2
Brain-inspired algorithms are attractive and emerging alternatives to classical deep learning methods for use in various machine learning applications. Brain-inspired systems can f…
Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks
Naresh Ravichandran, Anders Lansner, Pawel Herman
Neural networks that can capture key principles underlying brain computation offer exciting new opportunities for developing artificial intelligence and brain-like computing algori…
Spiking representation learning for associative memories
Naresh Ravichandran, Anders Lansner, Pawel Herman
Networks of interconnected neurons communicating through spiking signals offer the bedrock of neural computations. Our brains spiking neural networks have the computational capacit…
Benchmarking Hebbian learning rules for associative memory
Anders Lansner, Naresh B Ravichandran, Pawel Herman
Associative memory or content addressable memory is an important component function in computer science and information processing and is a key concept in cognitive and computation…