10 citations · 21 across the 13 of their papers we have counts for
13 papers
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…
Incremental Attractor Neural Network Modelling of the Lifespan Retrieval Curve
Patrícia Pereira, Anders Lansner, Pawel Herman
The human lifespan retrieval curve describes the proportion of recalled memories from each year of life. It exhibits a reminiscence bump - a tendency for aged people to better reca…
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…
Spiking neural networks with Hebbian plasticity for unsupervised representation learning
Naresh Ravichandran, Anders Lansner, Pawel Herman
We introduce a novel spiking neural network model for learning distributed internal representations from data in an unsupervised procedure. We achieved this by transforming the non…
Hebbian fast plasticity and working memory
Anders Lansner, Florian Fiebig, Pawel Herman
Theories and models of working memory (WM) were at least since the mid-1990s dominated by the persistent activity hypothesis. The past decade has seen rising concerns about the sho…
Brain-like combination of feedforward and recurrent network components achieves prototype extraction and robust pattern recognition
Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman
Associative memory has been a prominent candidate for the computation performed by the massively recurrent neocortical networks. Attractor networks implementing associative memory…
Semi-supervised learning with Bayesian Confidence Propagation Neural Network
Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman
Learning internal representations from data using no or few labels is useful for machine learning research, as it allows using massive amounts of unlabeled data. In this work, we u…
Brain-like approaches to unsupervised learning of hidden representations -- a comparative study
Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman
Unsupervised learning of hidden representations has been one of the most vibrant research directions in machine learning in recent years. In this work we study the brain-like Bayes…
Learning representations in Bayesian Confidence Propagation neural networks
Naresh Balaji Ravichandran, Anders Lansner, Pawel Herman
Unsupervised learning of hierarchical representations has been one of the most vibrant research directions in deep learning during recent years. In this work we study biologically…