Alpha power increase after transcranial alternating current stimulation at alpha frequency (α-tacs) reflects plastic changes rather than entrainment

Background: Periodic stimulation of occipital areas using transcranial alternating current stimulation (tACS) at alpha (α) frequency (8–12 Hz) enhances electroencephalographic (EEG) α-oscillation long after tACS-offset. Two mechanisms have been suggested to underlie these changes in oscillatory EEG activity: tACS-induced entrainment of brain oscillations and/or tACS-induced changes in oscillatory circuits by spike-timing dependent plasticity.<p></p> Objective: We tested to what extent plasticity can account for tACS-aftereffects when controlling for entrainment “echoes.” To this end, we used a novel, intermittent tACS protocol and investigated the strength of the aftereffect as a function of phase continuity between successive tACS episodes, as well as the match between stimulation frequency and endogenous α-frequency.<p></p> Methods: 12 healthy participants were stimulated at around individual α-frequency for 15–20 min in four sessions using intermittent tACS or sham. Successive tACS events were either phase-continuous or phase-discontinuous, and either 3 or 8 s long. EEG α-phase and power changes were compared after and between episodes of α-tACS across conditions and against sham.<p></p> Results: α-aftereffects were successfully replicated after intermittent stimulation using 8-s but not 3-s trains. These aftereffects did not reveal any of the characteristics of entrainment echoes in that they were independent of tACS phase-continuity and showed neither prolonged phase alignment nor frequency synchronization to the exact stimulation frequency.<p></p> Conclusion: Our results indicate that plasticity mechanisms are sufficient to explain α-aftereffects in response to α-tACS, and inform models of tACS-induced plasticity in oscillatory circuits. Modifying brain oscillations with tACS holds promise for clinical applications in disorders involving abnormal neural synchrony

Revealing networks from dynamics: an introduction

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.Comment: Topical review, 48 pages, 7 figure

Sixty GHz IMPATT diode development

The objective of this program is to develop 60 GHz GaAs IMPATT Diodes suitable for communications applications. The performance goal of the 60 GHz IMPATT is 1W CW output power with a conversion efficiency of 15 percent and 10 year life time. During the course of the program, double drift (DD) GaAs IMPATT Diodes have been developed resulting in the state of the art performance at V band frequencies. A CW output power of 1.12 W was demonstrated at 51.9 GHz with 9.7 percent efficiency. The best conversion efficiency achieved was 15.3 percent. V band DD GaAs IMPATTs were developed using both small signal and large signal analyses. GaAs wafers of DD flat, DD hybrid, and DD Read profiles using molecular beam epitaxy (MBE) were developed with excellent doping profile control. Wafer evaluation was routinely made by the capacitance versus voltage (C-V) measurement. Ion mass spectrometry (SIMS) analysis was also used for more detailed profile evaluation

Pulsed electromagnetic gas acceleration Semiannual progress report, 1 Jul. - 31 Dec. 1968

Pulsed electromagnetic gas acceleratio

Rafael Lorente de No, 1946

Rafael Lorente de Nó. Correlation of Nerve Activity with Polarization Phenomena Lecture delivered October 24th, 1946 Posted with permissionhttps://digitalcommons.rockefeller.edu/harvey-lectures/1035/thumbnail.jp

Neuromorphic systems based on memristive devices - From the material science perspective to bio-inspired learning hardware

Hardware computation is facing in the present age a deep transformation of its own paradigms. Silicon based computation is reaching its limit due to the physical constraints of transistor technology. As predicted by the Moore’s law, downscaling of transistor dimensions doubled each year since the 60s, leading nowadays to the extreme of 16-nm channel width of the present state-of-the-art technology. No further improvement is possible, since laws of physics impose a different electrical behavior when lower dimensions are attempted. Multiple solutions are then envisaged, spanning the range from quantum computing to neuromorphic computing. The present dissertation wants to be a preliminary study for understanding the opportunities enabled by neuromorphic computing based on resistive switching memories. In particular, brain inspires technology and architecture of new generation processors because of its unique properties: parallel and distributed computation, superposition of processing and memory unit, low power consumption, to cite only some of them. Such features make brain particularly efficient and robust against degraded data, further than particularly suitable to process and store in memory new nformation. Despite many research projects and some commercial products are already proposing brain-like computing processors, like spiNNaker or IBM’s Bluenorth, they only mimic the brain functioning with standard Silicon technology, that is inherently serial and distinguish between processing and memory unit. Resistive switching technology on the other hand, would allow to overcome many of these issues, enabling a far better match between biological and artificial neuromorphic computation. Resistive switching are, generally speaking, Metal-Insulator-Metal structures able to change their electrical conductance as a consequence of the history of applied electric signal. In such sense, they behave exactly as synapses do in a biological neural networks. For this reason, resistive switching when modeled as memristor, i.e. memory-resistor, can act as artificial synapses and, moreover, are particularly suitable to be interfaced with artificial Silicon neurons that are designed to replicate the biological behavior when excited with electric pulses. Anyhow, from the technological standpoint, there is still no standard on the design and fabrication of resistive switching, so that multiple structure and materials are investigated. In this dissertation, it is reported an analysis of multiple resistive switching devices, based on various materials, i.e. TiO2, ZnO and HfO, and device architectures, i.e. thin film and nanostructured devices, with the scope of both characterizing and comprehending the physics behind resistive switching phenomena. Furthermore, numerical simulations of artificial spiking neural networks, embedding Silicon neurons and HfO-based resistive switching are designed and performed, in order to give a systematic analysis of the performances reached by this new kind of computing paradigm

Effects of ionic concentration dynamics on neuronal activity

Neuronen sind bei der Informationsübertragung des zentralen Nervensystems von entscheidender Bedeutung. Ihre Aktivität liegt der Signalverarbeitung und höheren kognitiven Prozessen zugrunde. Neuronen sind in den extrazellulären Raum eingebettet, der mehrere Teilchen, darunter auch Ionen, enthält. Ionenkonzentrationen sind nicht statisch. Intensive neuronale Aktivität kann intrazelluläre und extrazelluläre Ionenkonzentrationen verändern. In dieser Arbeit untersuche ich das Wechselspiel zwischen neuronaler Aktivität und der Dynamik der Ionenkonzentrationen. Dabei konzentriere ich mich hauptsächlich auf extrazelluläre Kalium- und intrazelluläre Natriumkonzentrationen. Mit Hilfe der Theorie dynamischer Systeme zeige ich, wie moderate Änderungen dieser Ionenkonzentrationen die neuronale Aktivität qualitativ verändern können, wodurch sich möglicherweise die Signalverarbeitung verändert. Dann modelliere ich ein leitfähigkeitsbasiertes neuronales Netzwerk mit Spikes. Das Modell sagt voraus, dass eine moderate Änderung der Konzentrationen, die einen Mikroschaltkreis von Neuronen umgeben, die Leistungsspektraldichte der Populationsaktivität verändern könnte. Insgesamt unterstreicht diese Arbeit die Bedeutung der Dynamik der Ionenkonzentrationen für das Verständnis neuronaler Aktivität auf langen Zeitskalen und liefert technische Erkenntnisse darüber, wie das Zusammenspiel zwischen ihnen modelliert und analysiert werden kann.Neurons are essential in the information transfer mechanisms of the central nervous system. Their activity underlies both basic signal processing, and higher cognitive processes. Neurons are embedded in the extracellular space, which contains multiple particles, including ions which are vital to their functioning. Ionic concentrations are not static, intense neuronal activity alters the intracellular and extracellular ionic concentrations which in turn affect neuronal functioning. In this thesis, I study the interplay between neuronal activity and ionic concentration dynamics. I focus specifically on the extracellular potassium and intracellular sodium concentrations. Using dynamical systems theory, I illustrate how moderate changes in these ionic concentrations can qualitatively change neuronal activity, potentially altering signal processing. I then model a conductance-based spiking neural network. The model predicts that a moderate change in the concentrations surrounding a microcircuit of neurons could modify the power spectral density of the population activity. Altogether, this work highlights the need to consider ionic concentration dynamics to understand neuronal activity on long time scales and provides technical insights on how to model and analyze the interplay between them