Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning

Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a DVS/EMG hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system's topology is not fixed by the user but learned along the system's experience through self-organization.Comment: Preprin

An FPGA-based architecture to simulate cellular automata with large neighborhoods in real time

Summarization: In this paper we present a reconfigurable logic-based parallel architecture for the computation of 29X29 large-neighborhood cellular automata at 60 frames-per-second (FPS) real time update rate, using a small FPGA. The computation for each one of the n^2 elements of a two-dimensional input is O(κ2), where k is the size of the neighborhood in each dimension. All buffering and computation is performed internally in the FPGA. In terms of performance results, our architecture outperforms a general-purpose CPU running highly optimized software programmed in C by up to 51X; in neighborhoods up to 11X11 in which there are published results from GPUs our architecture has similar performance to GPUs at one-tenth the energy requirements, however, our architecture has the same performance for 29X29 neighborhoods whereas GPU performance drops as neighborhood grows. We expect this work to provide enabling new tools for the use of cellular automata models in the physical sciences.Presented on