Emergence of synchronised and amplified oscillations in neuromorphic networks with long-range interactions

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Spiking Neural Networks Trained With Backpropagation For Low Power Neuromorphic Implementation Of Voice Activity Detection

Giorgia Dellaferrera, Flavio Martinelli

Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency. This can be achieved by neuromorphic hardware, which ...

IEEE2020

A Neuro-Inspired Computational Model for a Visually Guided Robotic Lamprey Using Frame and Event Based Cameras

Auke Ijspeert, Alessandro Crespi, Mehmet Hasan Mutlu, Simon Lukas Hauser, Alexandre Bernardino, Mohammadreza Bayat

The computational load associated with computer vision is often prohibitive, and limits the capacity for on-board image analysis in compact mobile robots. Replicating the kind of feature detection and neural processing that animals excel at remains a chall ...

2020

Membrane activity detection in cultured cells using phase-sensitive plasmonics

Johannes Gräff, Jose Vicente Sanchez Mut, Xiaokang Li, Yasaman Jahani, Liliane Glauser, Ershad Mohammadi, Foziyeh Sohrabi

Despite the existence of various neural recording and mapping techniques, there is an open territory for the emergence of novel techniques. The current neural imaging and recording techniques suffer from invasiveness, a time-consuming labeling process, poo ...

2020

Dynamic response of a compressible binary fluid mixture

Ignacio Pagonabarraga Mora

Binary fluids are present in a wide variety of systems at microscales, such as microfluidic devices containing drops, fluids with air bubbles trapped in them, and devices designed to mix fluids or to make two fluid substances react. Microfluidics devices a ...

2020

Multi-memristive synaptic architectures for training neural networks

Irem Boybat Kara

Highly data-centric AI workloads require that new computing paradigms be adopted because the performance of traditional CPU- and GPU-based systems are limited by data access and transfer. Training deep neural networks with millions of tunable parameters ta ...

EPFL2020

An efficient analytical reduction of detailed nonlinear neuron models

Felix Schürmann, Idan Segev, Pramod Shivaji Kumbhar, Michael Emiel Gevaert

Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these ...

Nature Research2020

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Wulfram Gerstner, Tilo Schwalger, Samuel Pavio Muscinelli

While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurr ...

2019

Time-Frequency Characterization of Resting-State Brain Function Reveals Overlapping Components with Specific Topology and Frequency Content

Dimitri Nestor Alice Van De Ville, Thomas William Arthur Bolton

Even at rest, functional magnetic resonance imaging (fMRI) data displays exquisitely complex temporal dynamics. Here, we deployed a time-frequency analysis to track the modulus of fMRI signals over time, across space (a set of 341 brain areas) and frequenc ...

ASSOC COMPUTING MACHINERY2019

Increased Neural Strength and Reliability to Audiovisual Stimuli at the Boundary of Peripersonal Space

Andrea Serino

The actionable space surrounding the body, referred to as peripersonal space (PPS), has been the subject of significant interest of late within the broader framework of embodied cognition. Neurophysiological and neuroimaging studies have shown the represen ...

2019