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…
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…
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…