Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring

A method for online decorrelation of chemical sensor signals from the effects of environmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by processing data from simultaneous readings of environmental humidity and temperature. The electronic nose setup built for this study included eight metal-oxide sensors, temperature and humidity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor the air for two years in the residence of one of the authors and it collected data continuously during 537 days with a sampling rate of 1 sample per second. To estimate the effects of variations in air humidity and temperature on the chemical sensors' signals, we used a standard energy band model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the model is that variations in sensor conductivity can be expressed as a nonlinear function of changes in the semiconductor energy bands in the presence of external humidity and temperature variations. Fitting this model to the collected data, we confirmed that the most statistically significant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coefficient of determination R-2 close to 1. To show how the humidity-temperature correction model works for gas discrimination, we constructed a model for online discrimination among banana, wine and baseline response. This shows that pattern recognition algorithms improve performance and reliability by including the filtered signal of the chemical sensors. (C) 2016 Elsevier B.V. All rights reserved

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Information processing in sensory neural networks

Com os avanços em eletrônica analógica e digital dos últimos 50 anos, a neurociência ganhou grande momentum e nasceu uma de suas áreas que atualmente mais recebe financiamento: neurociência computacional. Estudos nessa área, ainda considerada recente, vão desde estudos moleculares de trocas iônicas por canais iônicos (escala nanométrica), até influências de populações neurais no comportamento de grandes mamíferos (escala de até metros). O coração da neurociência computacional compreende técnicas inter- e multidisciplinares, envolvendo biologia de sistemas, bioquímica, modelagem matemática, estatística, termodinâmica, física estatística, etc. O impacto em áreas de grande interesse, como o desenvolvimento de fármacos e dispositivos militares, é a grande força motriz desta área. Especificamente para este último, a compreensão do código neural e como informação sensorial é trabalhada por populações de neurônios é essencial. E ainda estamos num estágio muito inicial de desvendar todo o funcionamento de muitos dos sistemas sensoriais mais complexos. Um exemplo é de um dos sentidos que parece existir desde as formas mais primitivas de vida: o olfato. Em mamíferos, o número de estudos parece sempre crescer com os anos. Ainda estamos, no entanto, longe de um consenso sobre o funcionamento de muitos dos mecanismos básicos do olfato. A literatura é extensa em termos bioquímicos e comportamental, mas reunir tudo em um único modelo é talvez o grande desafio atual. Nesta tese discuto, em duas partes, sistemas sensoriais seguindo uma linha bastante ligada ao sistema olfativo. Na primeira parte, um modelo formal que lembra o bulbo olfativo (de mamíferos) é considerado para investigar a relação entre a performance da codificação neural e a existência de uma dinâmica crítica. Em especial, discuto sobre últimos experimentos baseados em observações de leis de potência como evidências da existência de criticalidade e ótima performance em populações neurais. Mostro que, apesar de a performance das redes estar, sim, ligada ao ponto crítico do sistema, a existência de leis de potência não está ligada nem com tal ponto crítico, nem com a ótima performance. Experimentos recentes confirmam estas observações. Na segunda parte, discuto e proponho uma modelagem inicial para o órgão central do sentido olfativo em insetos: o Corpo Cogumelar. A novidade deste modelo está na integração temporal, além de conseguir tanto fazer reconhecimento de padrões (qual odor) e estimativa de concentrações de odores. Com este modelo, proponho uma explicação para uma recente observação de antecipação neural no Corpo Cogumelar, em que sua última camada paradoxalmente parece antecipar a primeira camada. Proponho a existência de um balanço entre agilidade do código neural contra acurácia no reconhecimento de padrões. Este balanço pode ser empiricamente testado. Também proponho a existência de um controle de ganho no Corpo Cogumelar que seria responsável pela manutenção dos ingredientes principais para reconhecimento de padrões e aprendizado. Ambas estas partes contribuem para o compreendimento de como sistemas sensoriais operam e quais os mecanismos fundamentais que os fornecem performance invejável.With the advances in digital and analogical electronics in the last 50 years, neuroscience gained great momentum and one of its most well-financed sub-areas was born: computational neuroscience. Studies in this area, still considered recent by many, range from the ionic balance in the molecular level (scale of few nanometers), up to how neural populations influence behavior of large mammalians (scale of meters). The computational neuroscience core is highly based on inter- and multi-disciplinary techniques, involving systems biology, biochemistry, mathematical modeling, thermodynamics, statistical physics, etc. The impact in areas of current great interest, like in pharmaceutical drugs development and military devices, is its major flagship. Specifically for the later, deep understanding of neural code and how sensory information is filtered by neural populations is essential. And we are still grasping at the surface of really understanding many of the complex sensory systems we know. An example of such sensory modality that coexisted among all kinds of life forms is olfaction. In mammalians, the number of studies in this area seems to be growing steadily. However, we are still far from a complete agreement on how the basic mechanisms in olfaction work. There is a large literature of biochemical and behavioral studies, yet there is not a single model that comprises all this information and reproduces any olfactory system completely. In this thesis, I discuss in two parts sensory systems following a general line of argument based on olfaction. In the first part, a formal model that resembles the olfactory bulb (mammalians) is considered to investigate the relationship between performance in information coding and the existence of a critical dynamics. I show that, while the performance of neural networks may be intrinsically linked to a critical point, power laws are not exactly linked to neither critical points or performance optimization. Recent experiments corroborate this observation. In the second part, I discuss and propose a first dynamical model to the central organ responsible for olfactory learning in insects: the Mushroom Bodies. The novelty in this model is in the time integration, besides being able of pattern recognition (which odor) and concentration estimation at the same time. With this model, I propose an explanation for a seemingly paradoxical observation of coding anticipation in the Mushroom Bodies, where the last neural layer seems to trail the input layer. I propose the existence of a balance between accuracy and speed of pattern recognition in the Mushroom Bodies based on its fundamental morphological structure. I also propose the existence of a robust gain-control structure that sustain the key ingredients for pattern recognition and learning. This balance can be empirically tested. Both parts contribute to the understanding of the basic mechanisms behind sensory systems

Optical transitions in Zincblende semiconductors heterostructures with two sub-bands

Apresento neste trabalho uma derivação alternativa da hamiltoniana efetiva para um elétron na banda de condução de uma heteroestrutura semicondutora de rede Zincblende. Partindo do modelo de Kane 8 × 8 e da aproximação das funções envelope, esta hamiltoniana efetiva foi obtida com a linearização dos denominadores (dependentes das autoenergias) presentes na equação para a banda de condução, sob a hipótese de que o gap de energia seja muito maior que todas as demais diferenças de energia envolvidas (verdade para a maioria das estruturas Zincblende). A partir de um procedimento introduzido previamente1,3, desenvolvi um procedimento mais geral que implementa sistematicamente esta linearização até segunda ordem no inverso do gap de energia e que corrige a normalização do spinor da banda de condução usando as bandas de valência. Este procedimento é idêntico à expansão em série de potência no inverso da velocidade da luz utilizada para se obter aproximações relativísticas da equação de Dirac. Uma vantagem deste procedimento é a arbitrariedade na forma dos potenciais, o que implica na validade da hamiltoniana resultante para poços, fios e pontos quânticos. Evidencio também as consequências de cada termo desta hamiltoniana efetiva para os autoestados eletrônicos em poços retangulares, incluindo termos independentes de spin inéditos (Darwin e interação momento linearcampo elétrico). Estes resultados estão de acordo com os estudos anteriores4. A fim de estudar transições ópticas dentro da banda de condução, mostro que o acoplamento mínimo pode ser realizado diretamente na hamiltoniana de Kane se os campos externos variam tão lentamente quanto as funções envelope. Repetindo a linearização dos denominadores de energia, derivo uma hamiltoniana efetiva para a banda de condução com acoplamentos elétron-fótons. Um destes acoplamentos, induzido exclusivamente pela banda de valência, origina transições mediadas pelo spin eletrônico. Estas transições assistidas por spin possibilitam mudanças, opticamente induzidas, na orientação do spin eletrônico, um fato que talvez possa ser útil na construção de dispositivos spintrônicos. Por fim, as taxas de transição deste acoplamento apresentam saturação e linhas de máximos e mínimos no espaço recíproco. Espero que estas acoplamentos ópticos possam auxiliar na observação dos efeitos dos acoplamentos spin-órbita intra (Rashba) e intersubbandas.In this work, I present an alternative derivation of the conduction band effective hamiltonian for Zincblende semiconductor heterostructures. Starting from the 8×8 Kane model and envelope function approximation, this effective hamiltonian was obtained by means of a linearization in the eigenenergy-dependent denominators present the conduction band equation, under the hypothesis that the energy gap is bigger than any other energy difference in the system (true for mostly every Zincblende semiconductor bulk or heterostructure). Based on a previous procedure1,3, I have developed a more general procedure that implements sistematicaly (i) this linearization and (ii) renormalizes the conduction band spinor using the valence bands, both (i) and (ii) up to second order in the inverse of the energy gap. This procedure is identical to the expansion in power series of the inverse of the light speed used to derive non-relativistic approximations of the Dirac equation. One advantage of this procedure is the generality of the potentials adopted in our derivation: the same results hold for quantum wells, wires and dots. I show the consequences of each term of this hamiltonian for the electron eigenstates in retangular wells, including novel spin-independent terms (Darwin and linear momentumelectric field couplings). These resulties agree with previous works4. In order to study conduction band optical transitions, I show that the minimal substitution can be performed directly in the Kane hamiltonian if the external fields vary slowly (at least, as slowly as the envelope functions). Repeating the linearization of the energy denominators, I derive a new effective hamiltonian, up to second order in the inverse of the energy gap, with electron-photons couplings. One of these couplings, induced exclusively by the valence bands, gives rise to optical transitions mediated by the electron spin. This spin-assisted coupling enable optically-induced spin flipps in conduction subband transitions, which can be useful in the construction of spintronic devices. Finaly, the spin-assisted transitions rates show saturation and lines of maxima and minima in the reciprocal lattice. I hope that these optical couplings can be of any help in the observation of interesting effects induced by the intra and intersubband spin-orbit coupling

Optimal channel efficiency in a sensory network

Spontaneous neural activity has been increasingly recognized as a subject of key relevance in neuroscience. It exhibits nontrivial spatiotemporal structure reflecting the organization of the underlying neural network and has proved to be closely intertwined with stimulus-induced activity patterns. As an additional contribution in this regard, we report computational studies that strongly suggest that a stimulus-free feature rules the behavior of an important psychophysical measure of the sensibility of a sensory system to a stimulus, the so-called dynamic range. Indeed in this paper we show that the entropy of the distribution of avalanche lifetimes (information efficiency, since it can be interpreted as the efficiency of the network seen as a communication channel) always accompanies the dynamic range in the benchmark model for sensory systems. Specifically, by simulating the Kinouchi-Copelli (KC) model on two broad families of model networks, we generically observed that both quantities always increase or decrease together as functions of the average branching ratio (the control parameter of the KC model) and that the information efficiency typically exhibits critical optimization jointly with the dynamic range (i.e., both quantities are optimized at the same value of that control parameter, that turns out to be the critical point of a nonequilibrium phase transition). In contrast with the practice of taking power laws to identify critical points in most studies describing measured neuronal avalanches, we rely on data collapses as more robust signatures of criticality to claim that critical optimization may happen even when the distribution of avalanche lifetimes is not a power law, as suggested by a recent experiment. Finally, we note that the entropy of the size distribution of avalanches (information capacity) does not always follow the dynamic range and the information efficiency when they are critically optimized, despite being more widely used than the latter to describe the computational capabilities of a neural network. This strongly suggests that dynamical rules allowing a proper temporal matching of the states of the interacting neurons is the key for achieving good performance in information processing, rather than increasing the number of available units.CAPESFAPESP (10/20446-5

Predicting Synchronization of Three Mutually Inhibiting Groups of Oscillators with Strong Resetting

Neural populations encode sensory information, memory and motor patterns through electro-chemical firings, which propagate throughout the nervous system via synapses, a structure that couples neurons together. A powerful tool to investigate synchronization issues in such systems are the Phase Resetting curves. However these are best suited for brief and small perturbations. Motivated by the observation of strong inhibition in some neural circuits, we investigate a resetting model with similar features to a known neural population called striatum, in which three groups of neurons inhibit themselves. The model is intrinsically based on Kuramoto oscillators, and is analytically treatable. We derive a synchronization threshold in this model, and show numerically an unexpected complex dynamics

Non-parametric change point detection for spike trains

Mosqueiro T, Strube-Bloss M, Tuma R, Pinto R, Smith BH, Huerta R. Non-parametric change point detection for spike trains. In: 2016 Annual Conference on Information Science and Systems (CISS). Piscataway, NJ: IEEE; 2016

Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring

A method for online decorrelation of chemical sensor signals from the effects of environmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by processing data from simultaneous readings of environmental humidity and temperature. The electronic nose setup built for this study included eight metal-oxide sensors, temperature and humidity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor the air for two years in the residence of one of the authors and it collected data continuously during 537 days with a sampling rate of 1 sample per second. To estimate the effects of variations in air humidity and temperature on the chemical sensors' signals, we used a standard energy band model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the model is that variations in sensor conductivity can be expressed as a nonlinear function of changes in the semiconductor energy bands in the presence of external humidity and temperature variations. Fitting this model to the collected data, we confirmed that the most statistically significant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coefficient of determination R2 close to 1. To show how the humidity–temperature correction model works for gas discrimination, we constructed a model for online discrimination among banana, wine and baseline response. This shows that pattern recognition algorithms improve performance and reliability by including the filtered signal of the chemical sensors