Connecting the Brain to Itself through an Emulation.

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions

Perspectives on the Neuroscience of Cognition and Consciousness

The origin and current use of the concepts of computation, representation and information in Neuroscience are examined and conceptual flaws are identified which vitiate their usefulness for addressing problems of the neural basis of Cognition and Consciousness. In contrast, a convergence of views is presented to support the characterization of the Nervous System as a complex dynamical system operating in the metastable regime, and capable of evolving to configurations and transitions in phase space with potential relevance for Cognition and Consciousness

Roadmap on semiconductor-cell biointerfaces.

This roadmap outlines the role semiconductor-based materials play in understanding the complex biophysical dynamics at multiple length scales, as well as the design and implementation of next-generation electronic, optoelectronic, and mechanical devices for biointerfaces. The roadmap emphasizes the advantages of semiconductor building blocks in interfacing, monitoring, and manipulating the activity of biological components, and discusses the possibility of using active semiconductor-cell interfaces for discovering new signaling processes in the biological world

OPTIMIZATION OF TIME-RESPONSE AND AMPLIFICATION FEATURES OF EGOTs FOR NEUROPHYSIOLOGICAL APPLICATIONS

In device engineering, basic neuron-to-neuron communication has recently inspired the development of increasingly structured and efficient brain-mimicking setups in which the information flow can be processed with strategies resembling physiological ones. This is possible thanks to the use of organic neuromorphic devices, which can share the same electrolytic medium and adjust reciprocal connection weights according to temporal features of the input signals. In a parallel - although conceptually deeply interconnected - fashion, device engineers are directing their efforts towards novel tools to interface the brain and to decipher its signalling strategies. This led to several technological advances which allow scientists to transduce brain activity and, piece by piece, to create a detailed map of its functions. This effort extends over a wide spectrum of length-scales, zooming out from neuron-to-neuron communication up to global activity of neural populations. Both these scientific endeavours, namely mimicking neural communication and transducing brain activity, can benefit from the technology of Electrolyte-Gated Organic Transistors (EGOTs). Electrolyte-Gated Organic Transistors (EGOTs) are low-power electronic devices that functionally integrate the electrolytic environment through the exploitation of organic mixed ionic-electronic conductors. This enables the conversion of ionic signals into electronic ones, making such architectures ideal building blocks for neuroelectronics. This has driven extensive scientific and technological investigation on EGOTs. Such devices have been successfully demonstrated both as transducers and amplifiers of electrophysiological activity and as neuromorphic units. These promising results arise from the fact that EGOTs are active devices, which widely extend their applicability window over the capabilities of passive electronics (i.e. electrodes) but pose major integration hurdles. Being transistors, EGOTs need two driving voltages to be operated. If, on the one hand, the presence of two voltages becomes an advantage for the modulation of the device response (e.g. for devising EGOT-based neuromorphic circuitry), on the other hand it can become detrimental in brain interfaces, since it may result in a non-null bias directly applied on the brain. If such voltage exceeds the electrochemical stability window of water, undesired faradic reactions may lead to critical tissue and/or device damage. This work addresses EGOTs applications in neuroelectronics from the above-described dual perspective, spanning from neuromorphic device engineering to in vivo brain-device interfaces implementation. The advantages of using three-terminal architectures for neuromorphic devices, achieving reversible fine-tuning of their response plasticity, are highlighted. Jointly, the possibility of obtaining a multilevel memory unit by acting on the gate potential is discussed. Additionally, a novel mode of operation for EGOTs is introduced, enabling full retention of amplification capability while, at the same time, avoiding the application of a bias in the brain. Starting on these premises, a novel set of ultra-conformable active micro-epicortical arrays is presented, which fully integrate in situ fabricated EGOT recording sites onto medical-grade polyimide substrates. Finally, a whole organic circuitry for signal processing is presented, exploiting ad-hoc designed organic passive components coupled with EGOT devices. This unprecedented approach provides the possibility to sort complex signals into their constitutive frequency components in real time, thereby delineating innovative strategies to devise organic-based functional building-blocks for brain-machine interfaces.Nell’ingegneria elettronica, la comunicazione di base tra neuroni ha recentemente ispirato lo sviluppo di configurazioni sempre più articolate ed efficienti che imitano il cervello, in cui il flusso di informazioni può essere elaborato con strategie simili a quelle fisiologiche. Ciò è reso possibile grazie all'uso di dispositivi neuromorfici organici, che possono condividere lo stesso mezzo elettrolitico e regolare i pesi delle connessioni reciproche in base alle caratteristiche temporali dei segnali in ingresso. In modo parallelo, gli ingegneri elettronici stanno dirigendo i loro sforzi verso nuovi strumenti per interfacciare il cervello e decifrare le sue strategie di comunicazione. Si è giunti così a diversi progressi tecnologici che consentono agli scienziati di trasdurre l'attività cerebrale e, pezzo per pezzo, di creare una mappa dettagliata delle sue funzioni. Entrambi questi ambiti scientifici, ovvero imitare la comunicazione neurale e trasdurre l'attività cerebrale, possono trarre vantaggio dalla tecnologia dei transistor organici a base elettrolitica (EGOT). I transistor organici a base elettrolitica (EGOT) sono dispositivi elettronici a bassa potenza che integrano funzionalmente l'ambiente elettrolitico attraverso lo sfruttamento di conduttori organici misti ionici-elettronici, i quali consentono di convertire i segnali ionici in segnali elettronici, rendendo tali dispositivi ideali per la neuroelettronica. Gli EGOT sono stati dimostrati con successo sia come trasduttori e amplificatori dell'attività elettrofisiologica e sia come unità neuromorfiche. Tali risultati derivano dal fatto che gli EGOT sono dispositivi attivi, al contrario dell'elettronica passiva (ad esempio gli elettrodi), ma pongono comunque qualche ostacolo alla loro integrazione in ambiente biologico. In quanto transistor, gli EGOT necessitano l'applicazione di due tensioni tra i suoi terminali. Se, da un lato, la presenza di due tensioni diventa un vantaggio per la modulazione della risposta del dispositivo (ad esempio, per l'ideazione di circuiti neuromorfici basati su EGOT), dall'altro può diventare dannosa quando gli EGOT vengono adoperati come sito di registrazione nelle interfacce cerebrali, poiché una tensione non nulla può essere applicata direttamente al cervello. Se tale tensione supera la finestra di stabilità elettrochimica dell'acqua, reazioni faradiche indesiderate possono manifestarsi, le quali potrebbero danneggiare i tessuti e/o il dispositivo. Questo lavoro affronta le applicazioni degli EGOT nella neuroelettronica dalla duplice prospettiva sopra descritta: ingegnerizzazione neuromorfica ed implementazione come interfacce neurali in applicazioni in vivo. Vengono evidenziati i vantaggi dell'utilizzo di architetture a tre terminali per i dispositivi neuromorfici, ottenendo una regolazione reversibile della loro plasticità di risposta. Si discute inoltre la possibilità di ottenere un'unità di memoria multilivello agendo sul potenziale di gate. Viene introdotta una nuova modalità di funzionamento per gli EGOT, che consente di mantenere la capacità di amplificazione e, allo stesso tempo, di evitare l'applicazione di una tensione all’interfaccia cervello-dispositivo. Partendo da queste premesse, viene presentata una nuova serie di array micro-epicorticali ultra-conformabili, che integrano completamente i siti di registrazione EGOT fabbricati in situ su substrati di poliimmide. Infine, viene proposto un circuito organico per l'elaborazione del segnale, sfruttando componenti passivi organici progettati ad hoc e accoppiati a dispositivi EGOT. Questo approccio senza precedenti offre la possibilità di filtrare e scomporre segnali complessi nelle loro componenti di frequenza costitutive in tempo reale, delineando così strategie innovative per concepire blocchi funzionali a base organica per le interfacce cervello-macchina

Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill posed problem that commonly arises when modelling real world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems

The Physics of Living Neural Networks

Improvements in technique in conjunction with an evolution of the theoretical and conceptual approach to neuronal networks provide a new perspective on living neurons in culture. Organization and connectivity are being measured quantitatively along with other physical quantities such as information, and are being related to function. In this review we first discuss some of these advances, which enable elucidation of structural aspects. We then discuss two recent experimental models that yield some conceptual simplicity. A one-dimensional network enables precise quantitative comparison to analytic models, for example of propagation and information transport. A two-dimensional percolating network gives quantitative information on connectivity of cultured neurons. The physical quantities that emerge as essential characteristics of the network in vitro are propagation speeds, synaptic transmission, information creation and capacity. Potential application to neuronal devices is discussed.Comment: PACS: 87.18.Sn, 87.19.La, 87.80.-y, 87.80.Xa, 64.60.Ak Keywords: complex systems, neuroscience, neural networks, transport of information, neural connectivity, percolation http://www.weizmann.ac.il/complex/tlusty/papers/PhysRep2007.pdf http://www.weizmann.ac.il/complex/EMoses/pdf/PhysRep-448-56.pd