The computational role of short-term plasticity and the balance of excitation and inhibition in neural microcircuits: experimental and theoretical analysis

The computations performed by the brain ultimately rely on the functional connectivity between neurons embedded in complex networks. It is well known that the neuronal connections, the synapses, are plastic, i.e. the contribution of each presynaptic neuron to the firing of a postsynaptic neuron can be independently adjusted. The modulation of effective synaptic strength can occur on time scales that range from tens or hundreds of milliseconds, to tens of minutes or hours, to days, and may involve pre- and/or post-synaptic modifications. The collection of these mechanisms is generally believed to underlie learning and memory and, hence, it is fundamental to understand their consequences in the behavior of neurons.(...)

Neural correlations during brain activation in arithmetical tasks – an approach using electroencephalographic data

Dissertação apresentada na Faculdade de Ciências e Tecnologiea da Universidade Nova de Lisboa, para obtenção do Grau de Mestre em Engenharia Biomédica

Neural mechanisms of stimulus generalization in auditory fear conditioning

Dissertation presented to obtain the Ph.D degree in Neuroscience Instituto de Tecnologia Química e Biológica, Universidade Nova de Lisboa

Metabolic flux analysis of neural cell metabolism in primary cultures

Dissertation presented to obtain the Ph.D degree in Biochemistry, Neuroscience

Overexpressing BDNF in Neural Stem Cells using non-viral gene delivery strategies

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Tbx1 and Bmp2 in the development of the ear, neural crest and pharyngeal system in mice

Dissertation presented to obtain the Ph.D degree in Biology

Uncertainty, generalization, and neural representation of relevant variables for decision making

Dissertation presented to obtain the Ph.D degree in Biology, Computational Biology.

Nanostructuring silicon probes via electrodeposition: characterization of electrode coatings for acute in vivo neural recordings

Understanding how the brain works will require tools capable of measuring neuron elec-trical activity at a network scale. However, considerable progress is still necessary to reliably increase the number of neurons that are recorded and identified simultaneously with existing mi-croelectrode arrays. This project aims to evaluate how different materials can modify the effi-ciency of signal transfer from the neural tissue to the electrode. Therefore, various coating materials (gold, PEDOT, tungsten oxide and carbon nano-tubes) are characterized in terms of their underlying electrochemical processes and recording ef-ficacy. Iridium electrodes (177-706 μm2) are coated using galvanostatic deposition under different charge densities. By performing electrochemical impedance spectroscopy in phosphate buffered saline it is determined that the impedance modulus at 1 kHz depends on the coating material and decreased up to a maximum of two orders of magnitude for PEDOT (from 1 MΩ to 25 kΩ). The electrodes are furthermore characterized by cyclic voltammetry showing that charge storage capacity is im-proved by one order of magnitude reaching a maximum of 84.1 mC/cm2 for the PEDOT: gold nanoparticles composite (38 times the capacity of the pristine). Neural recording of spontaneous activity within the cortex was performed in anesthetized rodents to evaluate electrode coating performance.

Evolutionary dynamics of Chlamydia trachomatis genome and identification of molecular patterns of hypothetical protein coding genes

The obligate intracellular bacterium Chlamydia trachomatis is a human pathogen of major public health significance. Strains can be classified into 15 main serovars (A to L3) that preferentially cause ocular infections (A-C), genital infections (D-K) or lymphogranuloma venereum (LGV) (L1-L3), but the molecular basis behind their distinct tropism, ecological success and pathogenicity is not welldefined. Most chlamydial research demands culture in eukaryotic cell lines, but it is not known if stains become laboratory adapted. By essentially using genomics and transcriptomics, we aimed to investigate the evolutionary patterns underlying the adaptation of C. trachomatis to the different human tissues, given emphasis to the identification of molecular patterns of genes encoding hypothetical proteins, and to understand the adaptive process behind the C. trachomatis in vivo to in vitro transition. Our results highlight a positive selection-driven evolution of C. trachomatis towards nichespecific adaptation, essentially targeting host-interacting proteins, namely effectors and inclusion membrane proteins, where some of them also displayed niche-specific expression patterns. We also identified potential "ocular-specific" pseudogenes, and pointed out the major gene targets of adaptive mutations associated with LGV infections. We further observed that the in vivo-derived genetic makeup of C. trachomatis is not significantly compromised by its long-term laboratory propagation. In opposition, its introduction in vitro has the potential to affect the phenotype, likely yielding virulence attenuation. In fact, we observed a "genital-specific" rampant inactivation of the virulence gene CT135, which may impact the interpretation of data derived from studies requiring culture. Globally, the findings presented in this Ph.D. thesis contribute for the understanding of C.trachomatis adaptive evolution and provides new insights into the biological role of C. trachomatishypothetical proteins. They also launch research questions for future functional studies aiming toclarify the determinants of tissue tropism, virulence or pathogenic dissimilarities among C. trachomatisstrains.

Predicting market movement with Artificial Neural Networks

This paper presents an application of an Artificial Neural Network (ANN) to the prediction of stock market direction in the US. Using a multilayer perceptron neural network and a backpropagation algorithm for the training process, the model aims at learning the hidden patterns in the daily movement of the S&P500 to correctly identify if the market will be in a Trend Following or Mean Reversion behavior. The ANN is able to produce a successful investment strategy which outperforms the buy and hold strategy, but presents instability in its overall results which compromises its practical application in real life investment decisions.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.