Optoelectronic Reservoir Computing

Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an opto-electronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.Comment: Contains main paper and two Supplementary Material

VAC14 Gene‐Related Parkinsonism‐Dystonia With Response to Deep Brain Stimulation

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150504/1/mdc312797-sup-0001-TableS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150504/2/mdc312797.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150504/3/mdc312797_am.pd

Author Correction: CHD3 helicase domain mutations cause a neurodevelopmental syndrome with macrocephaly and impaired speech and language.

© 2019, The Author(s). The original version of this Article contained an error in the spelling of the author Laurence Faivre, which was incorrectly given as Laurence Faive. This has now been corrected in both the PDF and HTML versions of the Article

Reservoir Topology in Deep Echo State Networks

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the architectural design of deep reservoirs, through numerical experiments on several RC benchmarks. The major outcome of our investigation is to show the remarkable effect, in terms of predictive performance gain, achieved by the synergy between a deep reservoir construction and a structured organization of the recurrent units in each layer. Our results also indicate that a particularly advantageous architectural setting is obtained in correspondence of DeepESNs where reservoir units are structured according to a permutation recurrent matrix.Comment: Preprint of the paper published in the proceedings of ICANN 201

Reservoir Computing Approach to Robust Computation using Unreliable Nanoscale Networks

As we approach the physical limits of CMOS technology, advances in materials science and nanotechnology are making available a variety of unconventional computing substrates that can potentially replace top-down-designed silicon-based computing devices. Inherent stochasticity in the fabrication process and nanometer scale of these substrates inevitably lead to design variations, defects, faults, and noise in the resulting devices. A key challenge is how to harness such devices to perform robust computation. We propose reservoir computing as a solution. In reservoir computing, computation takes place by translating the dynamics of an excited medium, called a reservoir, into a desired output. This approach eliminates the need for external control and redundancy, and the programming is done using a closed-form regression problem on the output, which also allows concurrent programming using a single device. Using a theoretical model, we show that both regular and irregular reservoirs are intrinsically robust to structural noise as they perform computation

Online Training of an Opto-Electronic Reservoir Computer

Reservoir Computing is a bio-inspired computing paradigm for processing time dependent signals. Its analog implementations equal and sometimes outperform other digital algorithms on a series of benchmark tasks. Their performance can be increased by switching from offline to online training method. Here we present the first online trained opto-electronic reservoir computer. The system is tested on a channel equalisation task and the algorithm is executed by an FPGA chip. We report performances close to previous implementations and demonstrate the benefits of online training on a non-stationary task that could not be easily solved using offline methods.info:eu-repo/semantics/publishe

A Pair of Dopamine Neurons Target the D1-Like Dopamine Receptor DopR in the Central Complex to Promote Ethanol-Stimulated Locomotion in Drosophila

Dopamine is a mediator of the stimulant properties of drugs of abuse, including ethanol, in mammals and in the fruit fly Drosophila. The neural substrates for the stimulant actions of ethanol in flies are not known. We show that a subset of dopamine neurons and their targets, through the action of the D1-like dopamine receptor DopR, promote locomotor activation in response to acute ethanol exposure. A bilateral pair of dopaminergic neurons in the fly brain mediates the enhanced locomotor activity induced by ethanol exposure, and promotes locomotion when directly activated. These neurons project to the central complex ellipsoid body, a structure implicated in regulating motor behaviors. Ellipsoid body neurons are required for ethanol-induced locomotor activity and they express DopR. Elimination of DopR blunts the locomotor activating effects of ethanol, and this behavior can be restored by selective expression of DopR in the ellipsoid body. These data tie the activity of defined dopamine neurons to D1-like DopR-expressing neurons to form a neural circuit that governs acute responding to ethanol

Emerging role of the calcium-activated, small conductance, SK3 K ⁺ channel in distal tubule function: Regulation by TRPV4

The Ca2+-activated, maxi-K (BK) K+ channel, with low Ca2+-binding affinity, is expressed in the distal tubule of the nephron and contributes to flow-dependent K+ secretion. In the present study we demonstrate that the Ca2+-activated, SK3 (KCa2.3) K + channel, with high Ca2+-binding affinity, is also expressed in the mouse kidney (RT-PCR, immunoblots). Immunohistochemical evaluations using tubule specific markers demonstrate significant expression of SK3 in the distal tubule and the entire collecting duct system, including the connecting tubule (CNT) and cortical collecting duct (CCD). In CNT and CCD, main sites for K+ secretion, the highest levels of expression were along the apical (luminal) cell membranes, including for both principal cells (PCs) and intercalated cells (ICs), posturing the channel for Ca2+- dependent K+ secretion. Fluorescent assessment of cell membrane potential in native, split-opened CCD, demonstrated that selective activation of the Ca2+-permeable TRPV4 channel, thereby inducing Ca2+ influx and elevating intracellular Ca2+ levels, activated both the SK3 channel and the BK channel leading to hyperpolarization of the cell membrane. The hyperpolarization response was decreased to a similar extent by either inhibition of SK3 channel with the selective SK antagonist, apamin, or by inhibition of the BK channel with the selective antagonist, iberiotoxin (IbTX). Addition of both inhibitors produced a further depolarization, indicating cooperative effects of the two channels on Vm. It is concluded that SK3 is functionally expressed in the distal nephron and collecting ducts where induction of TRPV4-mediated Ca2+ influx, leading to elevated intracellular Ca2+ levels, activates this high Ca2+- affinity K+ channel. Further, with sites of expression localized to the apical cell membrane, especially in the CNT and CCD, SK3 is poised to be a key pathway for Ca2+-dependent regulation of membrane potential and K+ secretion. © 2014 Berrout et al