Decision time, slow inhibition, and theta rhythm

In this paper, we examine decision making in a spiking neuronal network and show that longer time constants for the inhibitory neurons can decrease the reaction times and produce theta rhythm.We analyze the mechanism and find that the spontaneous firing rate before the decision cues are applied can drift, and thereby influence the speed of the reaction time when the decision cues are applied. The drift of the firing rate in the population that will win the competition is larger if the time constant of the inhibitory interneurons is increased from 10 to 33 ms, and even larger if there are two populations of inhibitory neurons with time constants of 10 and 100 ms. Of considerable interest is that the decision that will be made can be influenced by the noise-influenced drift of the spontaneous firing rate over many seconds before the decision cues are applied. The theta rhythm associated with the longer time constant networks mirrors the greater integration in the firing rate drift produced by the recurrent connections over long time periods in the networks with slow inhibition. The mechanism for the effect of slow waves in the theta and delta range on decision times is suggested to be increased neuronal spiking produced by depolarization of the membrane potential on the positive part of the slow waves when the neuron’s membrane potential is close to the firing threshold

Analog readout for optical reservoir computers

Reservoir computing is a new, powerful and flexible machine learning technique that is easily implemented in hardware. Recently, by using a time-multiplexed architecture, hardware reservoir computers have reached performance comparable to digital implementations. Operating speeds allowing for real time information operation have been reached using optoelectronic systems. At present the main performance bottleneck is the readout layer which uses slow, digital postprocessing. We have designed an analog readout suitable for time-multiplexed optoelectronic reservoir computers, capable of working in real time. The readout has been built and tested experimentally on a standard benchmark task. Its performance is better than non-reservoir methods, with ample room for further improvement. The present work thereby overcomes one of the major limitations for the future development of hardware reservoir computers.Comment: to appear in NIPS 201

High performance photonic reservoir computer based on a coherently driven passive cavity

Reservoir computing is a recent bio-inspired approach for processing time-dependent signals. It has enabled a breakthrough in analog information processing, with several experiments, both electronic and optical, demonstrating state-of-the-art performances for hard tasks such as speech recognition, time series prediction and nonlinear channel equalization. A proof-of-principle experiment using a linear optical circuit on a photonic chip to process digital signals was recently reported. Here we present a photonic implementation of a reservoir computer based on a coherently driven passive fiber cavity processing analog signals. Our experiment has error rate as low or lower than previous experiments on a wide variety of tasks, and also has lower power consumption. Furthermore, the analytical model describing our experiment is also of interest, as it constitutes a very simple high performance reservoir computer algorithm. The present experiment, given its good performances, low energy consumption and conceptual simplicity, confirms the great potential of photonic reservoir computing for information processing applications ranging from artificial intelligence to telecommunicationsComment: non

An organic memristor as the building block for bio-inspired adaptive networks

This thesis reports the research path I followed during my PhD course, which i followed from January 2008 to December 2010 working at the University of Parma, in the Laboratory of Molecular Nanotechnologies, under the supervision of Prof. Marco P. Fontana and Dr. Victor Erokhin, within the framework of an interdisciplinary, international research project called BION – Biologically inspired Organized Networks. The keystone of my research is an organic memristor, a two terminal polymeric electronic device recently developed in our research group at the university of Parma. A memristor is a passive electronic device in which the electrical resistance depends on the electrical charge that has passed through it, and hence is adjustable by applying the appropriate electric potential or sequence of potentials. As of the beginning of my PhD, the device was in its early characterization stages, but it was already clear that it could be used to mimic the kind of plasticity found in synapses within neuronal circuits. In the thesis I show some further characterization work, used for engineering the device to maximize its more useful characteristics and to deepen our understanding of the functioning of the device, as well as the work done on. The knowledge of computational neuroscience acquired during this side project has proved very useful to better coordinate research in the material science side of the project, whose ultimate goal is the realization of a new, highly innovative technology for the production of functional molecular assemblies that can perform advanced tasks of information processing, involving learning and decision making, and that can be tailored down to the nanoscale.Questa tesi riporta il percorso di ricerca seguito durante il mio dottorato di ricerca, che ho svolto da gennaio 2008 a dicembre 2010 lavorando nel Laboratorio di Nanotecnologie Molecolari, presso l'Università di Parma, , sotto la supervisione del Prof. Marco P. Fontana e del Dott. Victor Erokhin, nel quadro di un approccio interdisciplinare, progetto di ricerca internazionale denominato BION - Biologically ispired Organized Networks . La chiave di svolta della mia ricerca è un memristor organico, un dispositivo a due terminali elettronici polimerici recentemente messo a punto nel nostro gruppo di ricerca presso l'università di Parma. Un memristor è un dispositivo elettronico passivo in cui la resistenza elettrica dipende dalla carica elettrica che è passata attraverso di essa, e quindi è regolabile applicando il potenziale elettrico appropriato o una sequenza di potenziali. A partire dall'inizio del mio dottorato di ricerca, il dispositivo è stato nelle sue fasi di caratterizzazione iniziale, ma era già chiaro che poteva essere usata per simulare il tipo di plasticità trovato in sinapsi all'interno di circuiti neuronali. Nella tesi ho mostrato un ulteriore lavoro di caratterizzazione, utilizzato per l'ingegneria del dispositivo al fine di massimizzare le sue caratteristiche più utili e di approfondire la nostra comprensione del funzionamento del dispositivo, così come il lavoro svolto. La conoscenza delle neuroscienze computazionali acquisite nel corso di questo progetto parallelo si è rivelato molto utile per meglio coordinare la ricerca per quanto riguarda il materiale scientifico del progetto, il cui scopo ultimo è la realizzazione di una nuova tecnologia altamente innovativa per la produzione di composti molecolari funzionali in grado di eseguire attività avanzate di elaborazione delle informazioni, che coinvolgano l'apprendimento e il processo decisionale, e che può essere adattata fino alla scala nanometrica

Gli strumi mediastinici

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Blunt abdominal trauma in children: Duodenal burst injury management.

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Sindrome di Boerhaave. Considerazioni sulla rottura spontanea dell'esofago. Contributo casistico

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All-optical Reservoir Computing

Reservoir Computing is a novel computing paradigm which uses a nonlinear recurrent dynamical system to carry out information processing. Recent electronic and optoelectronic Reservoir Computers based on an architecture with a single nonlinear node and a delay loop have shown performance on standardized tasks comparable to state-of-the-art digital implementations. Here we report an all-optical implementation of a Reservoir Computer, made of off-the-shelf components for optical telecommunications. It uses the saturation of a semiconductor optical amplifier as nonlinearity. The present work shows that, within the Reservoir Computing paradigm, all-optical computing with state-of-the-art performance is possible