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The growing number of Internet-of-Things (IoT) based artificial intelligence (AI) applications deployed at resource-constrained network edge call for ultra-reliable and low-latency data processing pipelines from distributed front-end…
Real-time simulation of a large-scale biologically representative spiking neural network is presented, through the use of a heterogeneous parallelisation scheme and SpiNNaker neuromorphic hardware. A published cortical microcircuit model is…
Noninvasive brain-computer interface (BCI) is widely used to recognize users' intentions. Especially, BCI related to tactile and sensation decoding could provide various effects on many industrial fields such as manufacturing advanced touch…
Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power…
Spiking Neural Networks (SNNs) are gaining widespread momentum in the field of neuromorphic computing. These network systems integrated with neurons and synapses provide computational efficiency by mimicking the human brain. It is desired…
Human cognition emerges from coordinated spiking dynamics in distributed neural circuits, where information is encoded via both firing rates and precise spike timing determined by brain rhythms. Inspired by this notion, we propose a…
Living systems process sensory data to facilitate adaptive behaviour. A given sensor can be stimulated as the result of internally driven activity, or by purely external (environmental) sources. It is clear that these inputs are processed…
Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We…
The process of teaching has been greatly changed by the COVID-19 pandemic. It is possible that studying will not resemble anymore the process known by the previous generations of students. As the current generations learn by doing and use…
Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic…
Neuromorphic computing seeks to replicate the spiking dynamics of biological neurons for brain-inspired computation. While electronic implementations of artificial spiking neurons have dominated to date, photonic approaches are attracting…
The stringent memory and power constraints required in edge-computing sensory-processing applications have made event-driven neuromorphic systems a promising technology. On-chip online learning provides such systems the ability to learn the…
Nowadays, the use of embedded operating systems in different embedded projects is subject to a tremendous growth. Embedded Linux is becoming one of those most popular EOSs due to its modularity, efficiency, reliability, and cost. One way to…
We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a…
Neurons communicate with downstream systems via sparse and incredibly brief electrical pulses, or spikes. Using these events, they control various targets such as neuromuscular units, neurosecretory systems, and other neurons in connected…
We present an approach to constructing a neuromorphic device that responds to language input by producing neuron spikes in proportion to the strength of the appropriate positive or negative emotional response. Specifically, we perform a…
Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact…
Two major initiatives to accelerate research in the brain sciences have focused attention on developing a new generation of scientific instruments for neuroscience. These instruments will be used to record static (structural) and dynamic…
Neuroprosthesis, as one type of precision medicine device, is aiming for manipulating neuronal signals of the brain in a closed-loop fashion, together with receiving stimulus from the environment and controlling some part of our brain/body.…
Real-time analysis and classification of bio-signals measured using wearable devices is computationally costly and requires dedicated low-power hardware. One promising approach is to use spiking neural networks implemented using in-memory…