8 citations · 13 across the 5 of their papers we have counts for
7 papers
Hippocluster: an efficient, hippocampus-inspired algorithm for graph clustering
Eric Chalmers, Artur Luczak
Random walks can reveal communities or clusters in networks, because they are more likely to stay within a cluster than leave it. Thus, one family of community detection algorithms…
Reinforcement Learning with Brain-Inspired Modulation can Improve Adaptation to Environmental Changes
Eric Chalmers, Artur Luczak
Developments in reinforcement learning (RL) have allowed algorithms to achieve impressive performance in highly complex, but largely static problems. In contrast, biological learni…
Biologically-inspired neuronal adaptation improves learning in neural networks
Yoshimasa Kubo, Eric Chalmers, Artur Luczak
Since humans still outperform artificial neural networks on many tasks, drawing inspiration from the brain may help to improve current machine learning algorithms. Contrastive Hebb…
A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe
Volodymyr Turchenko, Eric Chalmers, Artur Luczak
This paper presents the development of several models of a deep convolutional auto-encoder in the Caffe deep learning framework and their experimental evaluation on the example of…
Creation of a Deep Convolutional Auto-Encoder in Caffe
Volodymyr Turchenko, Artur Luczak
The development of a deep (stacked) convolutional auto-encoder in the Caffe deep learning framework is presented in this paper. We describe simple principles which we used to creat…
Spatial embedding of neuronal trees modeled by diffusive growth
Artur Luczak
The relative importance of the intrinsic and extrinsic factors determining the variety of geometric shapes exhibited by dendritic trees remains unclear. This question was addressed…