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

Drosophila fasciclinII Is Required for the Formation of Odor Memories and for Normal Sensitivity to Alcohol

AbstractDrosophila fasciclinII (fasII) mutants perform poorly after olfactory conditioning due to a defect in encoding, stabilizing, or retrieving short-term memories. Performance was rescued by inducing the expression of a normal transgene just before training and immediate testing. Induction after training but before testing failed to rescue performance, showing that Fas II does not have an exclusive role in memory retrieval processes. The stability of odor memories in fasII mutants are indistinguishable from control animals when initial performance is normalized. Like several other mutants deficient in odor learning, fasII mutants exhibit a heightened sensitivity to ethanol vapors. A combination of behavioral and genetic strategies have therefore revealed a role for Fas II in the molecular operations of encoding short-term odor memories and conferring alcohol sensitivity. The preferential expression of Fas II in the axons of mushroom body neurons furthermore suggests that short-term odor memories are formed in these neurites

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

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

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

The role of structure and complexity on Reservoir Computing quality

We explore the effect of structure and connection complexity on the dynamical behaviour of Reservoir Computers (RC). At present, considerable effort is taken to design and hand-craft physical reservoir computers. Both structure and physical complexity are often pivotal to task performance, however, assessing their overall importance is challenging. Using a recently proposed framework, we evaluate and compare the dynamical freedom (referring to quality) of neural network structures, as an analogy for physical systems. The results quantify how structure affects the range of behaviours exhibited by these networks. It highlights that high quality reached by more complex structures is often also achievable in simpler structures with greater network size. Alternatively, quality is often improved in smaller networks by adding greater connection complexity. This work demonstrates the benefits of using abstract behaviour representation, rather than evaluation through benchmark tasks, to assess the quality of computing substrates, as the latter typically has biases, and often provides little insight into the complete computing quality of physical systems

A Drosophila screen identifies NKCC1 as a modifier of NGLY1 deficiency

N-Glycanase 1 (NGLY1) is a cytoplasmic deglycosylating enzyme. Loss-of-function mutations in the NGLY1 gene cause NGLY1 deficiency, which is characterized by developmental delay, seizures, and a lack of sweat and tears. To model the phenotypic variability observed among patients, we crossed a Drosophila model of NGLY1 deficiency onto a panel of genetically diverse strains. The resulting progeny showed a phenotypic spectrum from 0 to 100% lethality. Association analysis on the lethality phenotype, as well as an evolutionary rate covariation analysis, generated lists of modifying genes, providing insight into NGLY1 function and disease. The top association hit was Ncc69 (human NKCC1/2), a conserved ion transporter. Analyses in NGLY1-/- mouse cells demonstrated that NKCC1 has an altered average molecular weight and reduced function. The misregulation of this ion transporter may explain the observed defects in secretory epithelium function in NGLY1 deficiency patients

Social Cohesion, Structural Holes, and a Tale of Two Measures

EMBARGOED - author can archive pre-print or post-print on any open access repository after 12 months from publication. Publication date is May 2013 so embargoed until May 2014.This is an author’s accepted manuscript (deposited at arXiv arXiv:1211.0719v2 [physics.soc-ph] ), which was subsequently published in Journal of Statistical Physics May 2013, Volume 151, Issue 3-4, pp 745-764. The final publication is available at link.springer.com http://link.springer.com/article/10.1007/s10955-013-0722-