59 research outputs found
Information processing in neural systems: oscillations, network topologies and optimal representations
Tesis doctoral inédita leÃda en la Universidad Autónoma de Madrid. Escuela Politécnica Superior, Departamento de IngenierÃa informática. Fecha de lectura: 1-07-200
Neuronal oscillations: from single-unit activity to emergent dynamics and back
L’objectiu principal d’aquesta tesi és avançar en la comprensió del processament d’informació en xarxes neuronals en presència d’oscil lacions subumbrals. La majoria de neurones propaguen la seva activitat elèctrica a través de sinapsis quÃmiques que són activades, exclusivament, quan el corrent elèctric que les travessa supera un cert llindar. És per aquest motiu que les descà rregues rà pides i intenses produïdes al soma neuronal, els anomenats potencials d’acció, són considerades la unitat bà sica d’informació neuronal, és a dir, el senyal mÃnim i necessari per a iniciar la comunicació entre dues neurones. El codi neuronal és entès, doncs, com un llenguatge binari que expressa qualsevol missatge (estÃmul sensorial, memòries, etc.) en un tren de potencials d’acció. Tanmateix, cap funció cognitiva rau en la dinà mica d’una única neurona. Circuits de milers de neurones connectades entre sà donen lloc a determinats ritmes, palesos en registres d’activitat colectiva com els electroencefalogrames (EEG) o els potencials de camp local (LFP). Si els potencials d’acció de cada cèl lula, desencadenats per fluctuacions estocà stiques de les corrents sinà ptiques, no assolissin un cert grau de sincronia, no apareixeria aquesta periodicitat a nivell de xarxa.
Per tal de poder entendre si aquests ritmes intervenen en el codi neuronal hem estudiat tres situacions. Primer, en el CapÃtol 2, hem mostrat com una
cadena oberta de neurones amb un potencial de membrana intrÃnsecament oscil latori filtra un senyal periòdic arribant per un dels extrems. La resposta
de cada neurona (pulsar o no pulsar) depèn de la seva fase, de forma que cada una d’elles rep un missatge filtrat per la precedent. A més, cada potencial
d’acció presinà ptic provoca un canvi de fase en la neurona postsinà ptica que depèn de la seva posició en l’espai de fases. Els perÃodes d’entrada capaços de sincronitzar les oscil lacions subumbrals són aquells que mantenen la fase d’arribada dels potencials d’acció fixa al llarg de la cadena. Per tal de què el missatge arribi intacte a la darrera neurona cal, a més a més, que aquesta fase permeti la descà rrega del voltatge transmembrana.
En segon cas, hem estudiat una xarxa neuronal amb connexions tant a veïns propers com de llarg abast, on les oscil lacions subumbrals emergeixen de
l’activitat col lectiva reflectida en els corrents sinà ptics (o equivalentment en el LFP). Les neurones inhibidores aporten un ritme a l’excitabilitat de la
xarxa, és a dir, que els episodis en què la inhibició és baixa, la probabilitat d’una descà rrega global de la població neuronal és alta. En el CapÃtol 3
mostrem com aquest ritme implica l’aparició d’una bretxa en la freqüència de descà rrega de les neurones: o bé polsen espaiadament en el temps o bé
en rà fegues d’elevada intensitat. La fase del LFP determina l’estat de la xarxa neuronal codificant l’activitat de la població: els mÃnims indiquen la
descà rrega simultà nia de moltes neurones que, ocasionalment, han superat el llindar d’excitabilitat degut a un decreixement global de la inhibició, mentre
que els mà xims indiquen la coexistència de rà fegues en diferents punts de la xarxa degut a decreixements locals de la inhibició en estats globals d’excitació. Aquesta dinà mica és possible grà cies al domini de la inhibició sobre l’excitació. En el CapÃtol 4 considerem acoblament entre dues xarxes neuronals per tal d’estudiar la interacció entre ritmes diferents. Les oscil lacions indiquen recurrència en la sincronització de l’activitat col lectiva, de manera que durant aquestes finestres temporals una població optimitza el seu impacte en una xarxa diana. Quan el ritme de la població receptora i el de l’emissora difereixen significativament, l’eficiència en la comunicació decreix, ja que les fases de mà xima resposta de cada senyal LFP no mantenen una diferència constant entre elles.
Finalment, en el CapÃtol 5 hem estudiat com les oscil lacions col lectives pròpies de l’estat de son donen lloc al fenomen de coherència estocà stica.
Per a una intensitat òptima del soroll, modulat per l’excitabilitat de la xarxa, el LFP assoleix una regularitat mà xima donant lloc a un perÃode refractari de
la població neuronal.
En resum, aquesta Tesi mostra escenaris d’interacció entre els potencials d’acció, caracterÃstics de la dinà mica de neurones individuals, i les oscil lacions
subumbrals, fruit de l’acoblament entre les cèl lules i ubiqües en la dinà mica de poblacions neuronals. Els resultats obtinguts aporten funcionalitat a aquests
ritmes emergents, agents sincronitzadors i moduladors de les descà rregues neuronals i reguladors de la comunicació entre xarxes neuronals.The main objective of this thesis is to better understand information processing in neuronal networks in the presence of subthreshold oscillations. Most neurons propagate their electrical activity via chemical synapses, which are only activated when the electric current that passes through them surpasses a certain threshold. Therefore, fast and intense discharges produced at the neuronal soma (the action potentials or spikes) are considered the basic unit of neuronal information. The neuronal code is understood, then, as a binary language that expresses any message (sensory stimulus, memories, etc.) in a train of action potentials. Circuits of thousands of interconnected neurons give rise to certain rhythms, revealed in collective activity measures such as electroencephalograms (EEG) and local field potentials (LFP). Synchronization of action potentials of each cell, triggered by stochastic fluctuations of the synaptic currents, cause this periodicity at the network level.To understand whether these rhythms are involved in the neuronal code we studied three situations. First, in Chapter 2, we showed how an open chain of neurons with an intrinsically oscillatory membrane potential filters a periodic signal coming from one of its ends. The response of each neuron (to spike or not) depends on its phase, so that each cell receives a message filtered by the preceding one. Each presynaptic action potential causes a phase change in the postsynaptic neuron, which depends on its position in the phase space. Those incoming periods that are able to synchronize the subthreshold oscillations, keep the phase of arrival of action potentials fixed along the chain. The original message reaches intact the last neuron provided that this phase allows the discharge of the transmembrane voltage.I the second case, we studied a neuronal network with connections to both long range and close neighbors, in which the subthreshold oscillations emerge from the collective activity apparent in the synaptic currents. The inhibitory neurons provide a rhythm to the excitability of the network. When inhibition is low, the likelihood of a global discharge of the neuronal population is high. In Chapter 3 we show how this rhythm causes a gap in the discharge frequency of neurons: either they pulse single spikes or they fire bursts of high intensity. The LFP phase determines the state of the neuronal network, coding the activity of the population: its minima indicate the simultaneous discharge of many neurons, while its maxima indicate the coexistence of bursts due to local decreases of inhibition at global states of excitation. In Chapter 4 we consider coupling between two neural networks in order to study the interaction between different rhythms. The oscillations indicate recurrence in the synchronization of collective activity, so that during these time windows a population optimizes its impact on a target network. When the rhythm of the emitter and receiver population differ significantly, the communication efficiency decreases as the phases of maximum response of each LFP signal do not maintain a constant difference between them.Finally, in Chapter 5 we studied how oscillations typical of the collective sleep state give rise to stochastic coherence. For an optimal noise intensity, modulated by the excitability of the network, the LFP reaches a maximal regularity leading to a refractory period of the neuronal population.In summary, this Thesis shows scenarios of interaction between action potentials, characteristics of the dynamics of individual neurons, and the subthreshold oscillations, outcome of the coupling between the cells and ubiquitous in the dynamics of neuronal populations . The results obtained provide functionality to these emerging rhythms, triggers of synchronization and modulator agents of the neuronal discharges and regulators of the communication between neuronal networks
An analogue approach for the processing of biomedical signals
Constant device scaling has signifcantly boosted electronic systems design in the digital domain enabling incorporation of more functionality within small silicon area and at the same time allows high-speed computation. This trend has been exploited for developing high-performance miniaturised systems in a number of application areas like communication, sensor network, main frame computers, biomedical information processing etc. Although successful, the associated cost comes in the form of high leakage power dissipation and systems reliability. With the increase of customer demands for smarter and faster technologies and with the advent of pervasive information processing, these issues may prove to be limiting factors for application of traditional digital design techniques. Furthermore, as the limit of device scaling is nearing, performance enhancement for the conventional digital system design methodology cannot be achieved any further unless innovations in new materials and new transistor design are made. To this end, an alternative design methodology that may enable performance enhancement without depending on device scaling is much sought today.Analogue design technique is one of these alternative techniques that have recently gained considerable interests. Although it is well understood that there are several roadblocks still to be overcome for making analogue-based system design for information processing as the main-stream design technique (e.g., lack of automated design tool, noise performance, efficient passive components implementation on silicon etc.), it may offer a faster way of realising a system with very few components and therefore may have a positive implication on systems performance enhancement. The main aim of this thesis is to explore possible ways of information processing using analogue design techniques in particular in the field of biomedical systems
18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems: Proceedings
Proceedings of the 18th IEEE Workshop on Nonlinear Dynamics of Electronic Systems, which took place in Dresden, Germany, 26 – 28 May 2010.:Welcome Address ........................ Page I
Table of Contents ........................ Page III
Symposium Committees .............. Page IV
Special Thanks ............................. Page V
Conference program (incl. page numbers of papers)
................... Page VI
Conference papers
Invited talks ................................ Page 1
Regular Papers ........................... Page 14
Wednesday, May 26th, 2010 ......... Page 15
Thursday, May 27th, 2010 .......... Page 110
Friday, May 28th, 2010 ............... Page 210
Author index ............................... Page XII
A Method for Evaluating Chimeric Synchronization of Coupled Oscillators and Its Application for Creating a Neural Network Information Converter
This paper presents a new method for evaluating the synchronization of
quasi-periodic oscillations of two oscillators, termed "chimeric
synchronization". The family of metrics is proposed to create a neural network
information converter based on a network of pulsed oscillators. In addition to
transforming input information from digital to analogue, the converter can
perform information processing after training the network by selecting control
parameters. In the proposed neural network scheme, the data arrives at the
input layer in the form of current levels of the oscillators and is converted
into a set of non-repeating states of the chimeric synchronization of the
output oscillator. By modelling a thermally coupled VO2-oscillator circuit, the
network setup is demonstrated through the selection of coupling strength, power
supply levels, and the synchronization efficiency parameter. The distribution
of solutions depending on the operating mode of the oscillators, sub-threshold
mode, or generation mode are revealed. Technological approaches for the
implementation of a neural network information converter are proposed, and
examples of its application for image filtering are demonstrated. The proposed
method helps to significantly expand the capabilities of neuromorphic and
logical devices based on synchronization effects.Comment: 25 pages, 20 figure
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