A reconfigurable visual-programming library for real-time closed-loop cellular electrophysiology

Most of the software platforms for cellular electrophysiology are limited in terms of flexibility, hardware support, ease of use, or re-configuration and adaptation for non-expert users. Moreover, advanced experimental protocols requiring real-time closed-loop operation to investigate excitability, plasticity, dynamics, are largely inaccessible to users with out moderate to substantial computer proficiency. Here we present an approach based on MATLAB/Simulink, exploiting the benefits of LEGO-like visual programming and configuration, combined to a small, but easily extendible library of functional software components. We provide and validate several examples, implementing conventional and more sophisticated experimental protocols such as dynamic-clamp or the combined use of intracellular and extracellular methods, involving closed-loop real-time control. The functionality of each of these examples is demonstrated with relevant experiments. These can be used as a starting point to create and support a larger variety of electrophysiological tools and methods, hope fully extending the range of default techniques and protocols currently employed in experimental labs across the world

Closed-loop approaches for innovative neuroprostheses

The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo

Interfaces neuronales CMOS haute résolution pour l'électrophysiologie et l'optogénétique en boucle fermée

L’avenir de la recherche sur les maladies du cerveau repose sur le développement de nouvelles technologies qui permettront de comprendre comment cet organe si complexe traite, intègre et transfère l’information. Parmi celles-ci, l’optogénétique est une technologie révolutionnaire qui permet d’utiliser de la lumière afin d’activer sélectivement les neurones du cortex d’animaux transgéniques pour observer leur effet dans un vaste réseau biologique. Ce cadre expérimental repose typiquement sur l’observation de l’activité neuronale de souris transgéniques, car elles peuvent exprimer une grande variété de gènes et de maladies et qu’elles sont peu couteuses. Toutefois, la plupart des appareils de mesure ou de stimulation optogénétique disponible ne sont pas appropriés, car ils sont câblés, trop lourds et/ou trop simplistes. Malheureusement, peu de systèmes sans fil existent, et ces derniers sont grandement limités par la bande passante requise pour transmettre les données neuronales, et ils ne fournissent pas de stimulation optogénétique multicanal afin de stimuler et observer plusieurs régions du cerveau. Dans les dispositifs actuels, l’interprétation des données neuronales est effectuée ex situ, alors que la recherche bénéficierait grandement de systèmes sans fil assez intelligents pour interpréter et stimuler les neurones en boucle fermée, in situ. Le but de ce projet de recherche est de concevoir des circuits analogiques-numériques d’acquisition et de traitement des signaux neuronaux, des algorithmes d’analyse et de traitement de ces signaux et des systèmes electro-optiques miniatures et sans fil pour : i) Mener des expériences combinant l’enregistrement neuronal et l’optogénétique multicanal haute résolution avec des animaux libres de leurs mouvements. ii) Mener des expériences optogénétiques synchronisées avec l’observation, c.-à-d. en boucle fermée, chez des animaux libres de leurs mouvements. iii) Réduire la taille, le poids et la consommation énergétique des systèmes optogénétiques sans fil afin de minimiser l’impact de la recherche chez de petits animaux. Ce projet est en 3 phases, et ses principales contributions ont été rapportées dans dix conférences internationales (ISSCC, ISCAS, EMBC, etc.) et quatre articles de journaux publiés ou soumis, ainsi que dans un brevet et deux divulgations. La conception d’un système optogénétique haute résolution pose plusieurs défis importants. Notamment, puisque les signaux neuronaux ont un contenu fréquentiel élevé (_10 kHz), le nombre de canaux sous observation est limité par la bande passante des transmetteurs sans fil (2-4 canaux en général). Ainsi, la première phase du projet a visé le développement d’algorithmes de compression des signaux neuronaux et leur intégration dans un système optogénétique sans fil miniature et léger (2.8 g) haute résolution possédant 32 canaux d’acquisition et 32 canaux de stimulation optique. Le système détecte, compresse et transmet les formes d’onde des potentiels d’action (PA) produits par les neurones avec un field programmable gate array (FPGA) embarqué à faible consommation énergétique. Ce processeur implémente un algorithme de détection des PAs basé sur un seuillage adaptatif, ce qui permet de compresser les signaux en transmettant seulement les formes détectées. Chaque PA est davantage compressé par une transformée en ondelette discrète (DWT) de type Symmlet-2 suivie d’une technique de discrimination et de requantification dynamique des coefficients. Les résultats obtenus démontrent que cet algorithme est plus robuste que les méthodes existantes tout en permettant de reconstruire les signaux compressés avec une meilleure qualité (SNDR moyen de 25 dB _ 5% pour un taux de compression (CR) de 4.2). Avec la détection, des CR supérieurs à 500 sont rapportés lors de la validation in vivo. L’utilisation de composantes commerciales dans des systèmes optogénétiques sans fil augmentela taille et la consommation énergétique, en plus de ne pas être optimisée pour cette application. La seconde phase du projet a permis de concevoir un système sur puce (SoC) complementary metal oxide semiconductor (CMOS) pour faire de l’enregistrement neuronal et de optogénétique multicanal, permettant de réduire significativement la taille et la consommation énergétique comparativement aux alternatives commerciales. Ceci est une contribution importante, car c’est la première puce à être doté de ces deux fonctionnalités. Le SoC possède 10 canaux d’enregistrement et 4 canaux de stimulation optogénétique. La conception du bioamplificateur inclut une bande passante programmable (0.5 Hz - 7 kHz) et un faible bruit referré à l’entré (IRN de 3.2 μVrms), ce qui permet de cibler différents types de signaux biologiques (PA, LFP, etc.). Le convertisseur analogique numérique (ADC) de type Delta- Sigma (DS) MASH 1-1-1 est conçu pour fonctionner de faibles taux de sur-échantillonnage (OSR _50) pour réduire sa consommation et possède une résolution programmable (ENOB de 9.75 Bits avec un OSR de 25). Cet ADC exploite une nouvelle technique réduisant la taille du circuit en soustrayant la sortie de chaque branche du DS dans le domaine numérique, comparativement à la méthode analogique classique. La consommation totale d’un canal d’enregistrement est de 11.2 μW. Le SoC implémente un nouveau circuit de stimulation optique basé sur une source de courant de type cascode avec rétroaction, ce qui permet d’accommoder une large gamme de LED et de tensions de batterie comparativement aux circuits existants. Le SoC est intégré dans un système optogénétique sans fil et validé in vivo. À ce jour et en excluant ce projet, aucun système sans-fil ne fait de l’optogénétique en boucle fermée simultanément au suivi temps réel de l’activité neuronale. Une contribution importante de ce travail est d’avoir développé le premier système optogénétique multicanal qui est capable de fonctionner en boucle fermée et le premier à être validé lors d’expériences in vivo impliquant des animaux libres de leurs mouvements. Pour ce faire, la troisième phase du projet a visé la conception d’un SoC CMOS numérique, appelé neural decoder integrated circuit (ND-IC). Le ND-IC et le SoC développé lors de la phase 2 ont été intégrés dans un système optogénétique sans fil. Le ND-IC possède 3 modules : 1) le détecteur de PA adaptatif, 2) le module de compression possédant un nouvel arbre de tri pour discriminer les coefficients, et 3) le module de classement automatique des PA qui réutilise les données générées par le module de détection et de compression pour réduire sa complexité. Un lien entre un canal d’enregistrement et un canal de stimulation est établi selon l’association de chaque PA à un neurone, grâce à la classification, et selon l’activité de ce neurone dans le temps. Le ND-IC consomme 56.9 μW et occupe 0.08 mm2 par canal. Le système pèse 1.05 g, occupe un volume de 1.12 cm3, possède une autonomie de 3h, et est validé in vivo.The future of brain research lies in the development of new technologies that will help understand how this complex organ processes, integrates and transfers information. Among these, optogenetics is a recent technology that allows the use of light to selectively activate neurons in the cortex of transgenic animals to observe their effect in a large biological network. This experimental setting is typically based on observing the neuronal activity of transgenic mice, as they express a wide variety of genes and diseases, while being inexpensive. However, most available neural recording or optogenetic devices are not suitable, because they are hard-wired, too heavy and/or too simplistic. Unfortunately, few wireless systems exist, and they are greatly limited by the required bandwidth to transmit neural data, while not providing simultaneous multi-channel neural recording and optogenetic, a must for stimulating and observing several areas of the brain. In current devices, the analysis of the neuronal data is performed ex situ, while the research would greatly benefit from wireless systems that are smart enough to interpret and stimulate the neurons in closed-loop, in situ. The goal of this project is to design analog-digital circuits for acquisition and processing of neural signals, algorithms for analysis and processing of these signals and miniature electrooptical wireless systems for: i) Conducting experiments combining high-resolution multi-channel neuronal recording and high-resolution multi-channel optogenetics with freely-moving animals. ii) Conduct optogenetic experiments synchronized with the neural recording, i.e. in closed loop, with freely-moving animals. iii) Increase the resolution while reducing the size, weight and energy consumption of the wireless optogenetic systems to minimize the impact of research with small animals. This project is in 3 phases, and its main contributions have been reported in ten conferences (ISSCC, ISCAS, EMBC, etc.) and four published journal papers, or submitted, as well as in a patent and two disclosures. The design of a high resolution optogenetic system poses several challenges. In particular, since the neuronal signals have a high frequency content (10 kHz), the number of chanv nels under observation is limited by the bandwidth of the wireless transmitters (2-4 channels in general). Thus, the first phase of the project focused on the development of neural signal compression algorithms and their integration into a high-resolution miniature and lightweight wireless optogenetics system (2.8g), having 32 recording channels and 32 optical stimulation channels. This system detects, compresses and transmits the waveforms of the signals produced by the neurons, i.e. action potentials (AP), in real time, via an embedded low-power field programmable gate array (FPGA). This processor implements an AP detector algorithm based on adaptive thresholding, which allows to compress the signals by transmitting only the detected waveforms. Each AP is further compressed by a Symmlet-2 discrete wavelet transform (DWT) followed dynamic discrimination and requantification of the DWT coefficients, making it possible to achieve high compression ratios with a good reconstruction quality. Results demonstrate that this algorithm is more robust than existing approach, while allowing to reconstruct the compressed signals with better quality (average SNDR of 25 dB 5% for a compression ratio (CR) of 4.2). With detection, CRs greater than 500 are reported during the in vivo validation. The use of commercial components in wireless optogenetic systems increases the size and power consumption, while not being optimized for this application. The second phase of the project consisted in designing a complementary metal oxide semiconductor (CMOS) system-on-chip (SoC) for neural recording and multi-channel optogenetics, which significantly reduces the size and energy consumption compared to commercial alternatives. This is important contribution, since it’s the first chip to integrate both features. This SoC has 10 recording channels and 4 optogenetic stimulation channels. The bioamplifier design includes a programmable bandwidth (0.5 Hz -7 kHz) and a low input-referred noise (IRN of 3.2 μVrms), which allows targeting different biological signals (AP, LFP, etc.). The Delta-Sigma (DS) MASH 1-1-1 low-power analog-to-digital converter (ADC) is designed to work with low OSR (50), as to reduce its power consumption, and has a programmable resolution (ENOB of 9.75 bits with an OSR of 25). This ADC uses a new technique to reduce its circuit size by subtracting the output of each DS branch in the digital domain, rather than in the analog domain, as done conventionally. A recording channel, including the bioamplifier, the DS and the decimation filter, consumes 11.2 μW. Optical stimulation is performed with an on-chip LED driver using a regulated cascode current source with feedback, which accommodates a wide range of LED parameters and battery voltages. The SoC is integrated into a wireless optogenetic platform and validated in vivo.To date and excluding this project, no wireless system is making closed-loop optogenetics simultaneously to real-time monitoring of neuronal activity. An important contribution of this work is to have developed the first multi-channel optogenetic system that is able to work in closed-loop, and the first to be validated during in vivo experiments involving freely-moving animals. To do so, the third phase of the project aimed to design a digital CMOS chip, called neural decoder integrated circuit (ND-IC). The ND-IC and the SoC developed in Phase 2 are integrated within a wireless optogenetic system. The ND-IC has 3 main cores: 1) the adaptive AP detector core, 2) the compression core with a new sorting tree for discriminating the DWT coefficients, and 3 ) the AP automatic classification core that reuses the data generated by the detection and compression cores to reduce its complexity. A link between a recording channel and a stimulation channel is established according to the association of each AP with a neuron, thanks to the classification, and according to the bursting activity of this neuron. The ND-IC consumes 56.9 μW and occupies 0.08 mm2 per channel. The system weighs 1.05 g, occupies a volume of 1.12 cm3, has an autonomy of 3h, and is validated in vivo

Advanced photonic and electronic systems - WILGA 2017

WILGA annual symposium on advanced photonic and electronic systems has been organized by young scientist for young scientists since two decades. It traditionally gathers more than 350 young researchers and their tutors. Ph.D students and graduates present their recent achievements during well attended oral sessions. Wilga is a very good digest of Ph.D. works carried out at technical universities in electronics and photonics, as well as information sciences throughout Poland and some neighboring countries. Publishing patronage over Wilga keep Elektronika technical journal by SEP, IJET by PAN and Proceedings of SPIE. The latter world editorial series publishes annually more than 200 papers from Wilga. Wilga 2017 was the XL edition of this meeting. The following topical tracks were distinguished: photonics, electronics, information technologies and system research. The article is a digest of some chosen works presented during Wilga 2017 symposium. WILGA 2017 works were published in Proc. SPIE vol.10445

Exploiting All-Programmable System on Chips for Closed-Loop Real-Time Neural Interfaces

High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes in a single chip with an area of few square millimeters. The obtained electrode density is comparable and even higher than the typical density of neuronal cells in cortical cultures. Commercially available HDMEA-based acquisition systems are able to record the neural activity from the whole array at the same time with submillisecond resolution. These devices are a very promising tool and are increasingly used in neuroscience to tackle fundamental questions regarding the complex dynamics of neural networks. Even if electrical or optical stimulation is generally an available feature of such systems, they lack the capability of creating a closed-loop between the biological neural activity and the artificial system. Stimuli are usually sent in an open-loop manner, thus violating the inherent working basis of neural circuits that in nature are constantly reacting to the external environment. This forbids to unravel the real mechanisms behind the behavior of neural networks. The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable processing system capable of providing real-time feedback to the ongoing neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable System on Chip (APSoC) has been used. The device features reconfigurable logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of these components allows to achieve high elaboration performances while maintaining a high level of flexibility and adaptivity. The developed system has been embedded in an acquisition and stimulation setup featuring the following platforms: \u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of recording in parallel from 4096 electrodes at 18 kHz per electrode. \u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with custom waveforms to 16 different output channels. \u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting 608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical stimulation. All the features of the system, such as band-pass filtering and spike detection of all the recorded channels, have been validated by means of ex vivo experiments. Very low-latency has been achieved while processing the whole input data stream in real-time. In the case of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed, instead, the total latency is a little higher, being 21 ms in the worst case. The final setup is ready to be used to infer cellular properties by means of closed-loop experiments. As a proof of this concept, it has been successfully used for the clustering and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells. In addition to the closed-loop system, a denoising prototype has been developed. The main idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased acquisition systems. The prototype is capable of processing in real-time all the input signals from the BioCam X, and it is currently being tested to evaluate the performance in terms of signal-to-noise-ratio improvement

Neural networks-on-chip for hybrid bio-electronic systems

PhD ThesisBy modelling the brains computation we can further our understanding of its function and develop novel treatments for neurological disorders. The brain is incredibly powerful and energy e cient, but its computation does not t well with the traditional computer architecture developed over the previous 70 years. Therefore, there is growing research focus in developing alternative computing technologies to enhance our neural modelling capability, with the expectation that the technology in itself will also bene t from increased awareness of neural computational paradigms. This thesis focuses upon developing a methodology to study the design of neural computing systems, with an emphasis on studying systems suitable for biomedical experiments. The methodology allows for the design to be optimized according to the application. For example, di erent case studies highlight how to reduce energy consumption, reduce silicon area, or to increase network throughput. High performance processing cores are presented for both Hodgkin-Huxley and Izhikevich neurons incorporating novel design features. Further, a complete energy/area model for a neural-network-on-chip is derived, which is used in two exemplar case-studies: a cortical neural circuit to benchmark typical system performance, illustrating how a 65,000 neuron network could be processed in real-time within a 100mW power budget; and a scalable highperformance processing platform for a cerebellar neural prosthesis. From these case-studies, the contribution of network granularity towards optimal neural-network-on-chip performance is explored

Real-time signal detection and classification algorithms for body-centered systems

El principal motivo por el cual los sistemas de comunicación en el entrono corporal se desean con el objetivo de poder obtener y procesar señales biométricas para monitorizar e incluso tratar una condición médica sea ésta causada por una enfermedad o el rendimiento de un atleta. Dado que la base de estos sistemas está en la sensorización y el procesado, los algoritmos de procesado de señal son una parte fundamental de los mismos. Esta tesis se centra en los algoritmos de tratamiento de señales en tiempo real que se utilizan tanto para monitorizar los parámetros como para obtener la información que resulta relevante de las señales obtenidas. En la primera parte se introduce los tipos de señales y sensores en los sistemas en el entrono corporal. A continuación se desarrollan dos aplicaciones concretas de los sistemas en el entorno corporal así como los algoritmos que en las mismas se utilizan. La primera aplicación es el control de glucosa en sangre en pacientes con diabetes. En esta parte se desarrolla un método de detección mediante clasificación de patronones de medidas erróneas obtenidas con el monitor contínuo comercial "Minimed CGMS". La segunda aplicacióin consiste en la monitorizacióni de señales neuronales. Descubrimientos recientes en este campo han demostrado enormes posibilidades terapéuticas (por ejemplo, pacientes con parálisis total que son capaces de comunicarse con el entrono gracias a la monitorizacióin e interpretación de señales provenientes de sus neuronas) y también de entretenimiento. En este trabajo, se han desarrollado algoritmos de detección, clasificación y compresión de impulsos neuronales y dichos algoritmos han sido evaluados junto con técnicas de transmisión inalámbricas que posibiliten una monitorización sin cables. Por último, se dedica un capítulo a la transmisión inalámbrica de señales en los sistemas en el entorno corporal. En esta parte se estudia las condiciones del canal que presenta el entorno corporal para la transmisión de sTraver Sebastiá, L. (2012). Real-time signal detection and classification algorithms for body-centered systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16188Palanci

Bladder Volume Decoding from Afferent Neural Activity

RÉSUMÉ Lorsque les fonctions de stockage et de miction de la vessie échouent à la suite de traumatismes médullaires, ou en raison d'autres maladies neurologiques, de conditions de santé ou au vieillissement, des complications graves pour la santé du patient se produisent. Actuellement, il est possible de restaurer partiellement les fonctions de la vessie chez les patients réfractaires aux médicaments à l'aide des neurostimulateurs implantables. Pour améliorer l'efficacité et la sécurité de ces neuroprothèses, il faut un capteur de la vessie capable de détecter l’urine stockée afin de mettre en place un système en boucle fermée qui applique la stimulation électrique uniquement lorsque nécessaire. Le capteur peut également servir à aviser les patients ayant des sensations affaiblies pour les aviser en temps opportun le moment où la vessie doit être vidée ou quand un volume résiduel postmictionnel anormalement élevé reste après une miction incomplète. Dans cette thèse, on présente de nouvelles méthodes de mesure, ainsi qu’un processeur de signal numérique dédié pour décoder en temps réel le volume de la vessie à partir des enregistrements neuronaux afférents provenant des récepteurs naturels présents dans la paroi de la vessie. Nos principales contributions sont rapportées dans trois articles de journaux avec comité de lecture. On présente d'abord une revue exhaustive de la littérature comprenant des articles de journaux, des brevets et les livres les plus réputés portant sur l'anatomie, la physiologie et la physiopathologie du tractus urinaire inférieur ainsi que sur la mesure du volume ou la pression de la vessie. Cette étude nous a permis d'identifier les besoins qu'un capteur de la vessie doit satisfaire pour être utilisé dans des applications chroniques telles que celles proposées dans cette thèse. On présente aussi le résultat d’une analyse exhaustive des caractéristiques anatomiques et physiologiques de la vessie que nous avons identifiées d’avoir exercé une influence, ou même d’avoir empêché, la réalisation d'un tel capteur dans des études faites au cours des dernières années. Sur la base de cette étude et de l'évaluation systématique des méthodes de mesure pour la vessie, on a conclu que le principe de mesure le mieux adapté pour la surveillance chronique du volume de la vessie était la détection, la discrimination et le décodage de l'activité neuronale afférente découlant des récepteurs spécialisés du volume (mécanorécepteurs), au sujet desquels certains auteurs ont émis l'hypothèse de leur existence dans la muqueuse interne de la vessie. Ensuite, on présente la méthode de mesure qui permet d'estimer en temps réel le volume de la vessie à partir de l'activité afférente des mécanorécepteurs. Notre méthode a été validée avec les----------ABSTRACT Failure of the storage and voiding functions of the urinary bladder due to spinal cord injury (SCI), neural diseases, health conditions, or aging, causes serious complications in a patient's health. Currently, it is possible to partially restore bladder functions in drug-refractory patients using implantable neurostimulators. Improving the efficacy and safety of these neuroprostheses used for bladder functions restoration requires a bladder sensor (BS) capable of detecting urine volume in real-time to implement a closed-loop system that applies electrical stimulation only when required. The BS can also trigger an early warning to advise patients with impaired sensations when the bladder should be voided or when an abnormally high post-voiding residual volume remains after an incomplete voiding. In this thesis, we present new measurement methods and a dedicated digital signal processor for real-time decoding of the bladder volume through afferent neural signals arising from natural receptors present in the bladder wall. The main contributions of this thesis have been reported in three peer-reviewed journal papers. We first present a comprehensive literature review, including papers, patents and mainstay books of bladder anatomy, physiology, and pathophysiology. This review allowed us to identify the requirements (user needs) that a BS must meet for chronic applications, such as those proposed in this thesis. An exhaustive analysis of the particular anatomical and physiological characteristics of the bladder, which we realized had influenced or prevented the achievement of a BS for monitoring the bladder volume or pressure in past studies, are also presented. Based on this study and on a systematic assessment of the measurement methods published in past years, we determined the best measurement principle for chronic bladder volume monitoring: the detection, discrimination and decoding of the afferent neural activity stemming from specialized volume receptors (mechanoreceptors), on which some authors had hypothesized about its existence in the bladder inner mucosa. Next, we present methods that allows for a real-time estimation of bladder volume through the afferent activity of the bladder mechanoreceptors. Our method was validated with data acquired from anesthetized rats in acute experiments. It was possible to qualitatively estimate three states of bladder fullness in 100% of trials when the recorded afferent activity exhibited a Spearman’s correlation coefficient of 0.6 or better. Furthermore, we could quantitatively estimate the bladder volume, and also its pressure, using time-windows of properly chosen duration. The mea

Innovative Techniques of Neuromodulation and Neuromodeling Based on Focal Non-Invasive Transcranial Magnetic Stimulation for Neurological Disorders

This dissertation aims to develop alternative technology that improves the current range of application of transcranial magnetic stimulation (TMS), on a scale that would permit defining specific non-invasive treatments for Parkinson’s disease and other neurological disorders. This is accomplished through three specific objectives. 1) The design of a neurostimulation system that increases the focality in TMS to regions of narrow target areas and variable depths in the brain cortex. 2) The assessment of the feasibility of novel high-frequency neuromodulation techniques that would allow increasing the focality in deeper areas beyond the cortical surface. 3) The development of a computational model of the motor pathway that allows studying the underlying mechanisms that originate PD symptoms, and the effects of TMS for the development of new treatments. The results successfully demonstrated the feasibility of using the novel high-frequency neuromodulation technique as an effective manner to reduce the necessary current in TMS coils. This reduction, which reached an order of magnitude of 100 times compared to commercial TMS technology, made it possible to reduce the coil sizes, making them more focal to targets (in the order of a few millimeters square). Finally, our innovative oscillatory model of the motor pathway allowed us to conclude that an internal regulatory mechanism that we believe neurons activate in advanced PD stages seems to be the pathological response of some neural subpopulations to dopamine depletion, trying to compensate for the downstream effects in the system. We also found that such a mechanism seems to the burstiness in PD