@article{IOP, abstract = {We experimentally study Stochastic Resonance in an artificial neuron and demonstrate that pink noise amplifies the input signal considerably—up to twenty times more—compared to white noise. The experimental results are consistent with biological observations and theoretical calculations. Possible applications include the design of electro-optical devices. }, author = {Alexandra Pinto}, doi = {10.1088/2631-8695/ab8442}, openaccess = {https://doi.org/10.1088/2631-8695/ab8442}, pdf = {https://iopscience.iop.org/article/10.1088/2631-8695/ab8442}, title = {Pink noise amplifies stochastic resonance in neural circuits}, year = {2021} }

@article{adsabs, abstract = {Some systems cannot be predicted by classical theories and require the development of combined deterministic and stochastic theories that make use of noise for their dynamical prediction. Noise is not always an interfering signal which perturbs the system, it can also enhance its performance. This property can be observed through Stochastic Resonance (SR), To detect this phenomena it is necessary a system with bistable potential barrier with a threshold, the input of the system should be a weak periodic signal which amplitude is below threshold together with a stochastic signal. The behaviour of the SR is detected in a neural network and it is studied under noise color variations. Here it is found that Pink noise amplifies the sub-threshold input signal twenty times more in comparison to white noise. This could be evidence of the functionality of background noise in the brain, where neurons are naturally embedded in pink noise.}, author = {Pinto Alexandra}, doi = {10.13140/RG.2.2.25576.26887}, openaccess = {https://ui.adsabs.harvard.edu/abs/2018arXiv181006731P}, pdf = {https://arxiv.org/pdf/1810.06731.pdf}, title = {Stochastic Resonance in Neural Network, Noise Color Effects}, year = {2018} }

@article{physics.med-ph, abstract = {This paper describes the model for DNA in MATLAB taking into account all of component atoms. In this model, it is possible to generate sequences with length 10000 basis pairs available for introducing all types of sequences. Once the strands are generated, it is studied the DNA damage in the single strand and double strand. The damage are outcomes of ionising radiation of X-rays when interacting with the DNA immersed in water. This is a theoretical and experimental in-vitro study that quantifies the single strand and double strand damage for different doses of radiation. This can be useful to predict the exact risks of expositions to radiations. In simulations, it is taken into account the damage caused by free electrons generated by the effect of the interaction with the water molecules, this is different to the effect considered in radiobiology, where indirect damages are due to chemical reactions. The spatial distribution of the electrons is obtained from Geant4 and here this distribution is used for the creation of rays as three-dimensional random trajectories through Monte Carlo simulations. It is also presented the experimental DNA damage through radiating DNA samples immersed in water with a X-rays unit with Molybdenum target. The damage level is quantified through Atomic Force Microscopy (AFM). It is possible to conclude a direct relation between the damage and the radiation doses with the experimental results.}, author = {Pinto Alexandra}, doi = {10.13140/RG.2.2.25576.26887}, openaccess = {arXiv:1810.05815}, pdf = {https://ui.adsabs.harvard.edu/abs/2018arXiv181005815P}, title = {Simulation and experimental verification of the DNA damage due to X-rays interaction}, year = {2018} }

@article{adsabs, abstract = {The olfactory system is constantly solving pattern-recognition problems by the creation of a large space to codify odour representations and optimizing their distribution within it. A model of the Olfactory Bulb was developed by Z. Li and J. J. Hopfield Li and Hopfield (1989) based on anatomy and electrophysiology. They used nonlinear simulations observing that the collective behavior produce an oscillatory frequency. Here, we show that the Subthreshold hopf bifurcation is a good candidate for modeling the bulb and the Subthreshold subcritical hopf bifurcation is a good candidate for modeling the olfactory cortex. Network topology analysis of the subcritical regime is presented as a proof of the importance of synapse plasticity for memory functions in the olfactory cortex.}, author = {Pinto Alexandra}, doi = {10.13140/RG.2.2.25576.26887}, openaccess = {arXiv:1905.06307}, pdf = {https://ui.adsabs.harvard.edu/abs/2019arXiv190506307P}, title = {Coupled Oscillators as a model of Olfactory Network. Importance in Pattern Recognition and Classification tasks}, year = {2017} }

@article{physics.med-ph, abstract = {Neurons have the capability of transforming information from a digital signal at the dendrites of the presynaptic termi- nal to an analogous wave at the synaptic cleft and back to a digital pulse when they achieve the required voltage for the generation of an action potential at the postsynaptic neuron. The main question of this research is what processes are generating the oscillatory wave signal at the synaptic cleft and what is the best model for this phenomenon. Here, it is proposed a model of the synapse as an oscillatory system capable of synchronization taking into account conservation of information and consequently of frequency at the interior of the synaptic cleft. Trains of action potentials certainly encode and transmit information along the nervous system but most of the time neurons are not transmitting action potentials, 99 percent of their time neurons are in the sub threshold regime were only small signals without the energy to emanate an action potential are carrying the majority of information. The proposed model for a synapse, smooths the train of action potential and keeps its frequency. Synapses are presented here as a system composed of an input wave that is transformed through interferometry. The collective synaptic interference pattern of waves will reflect the points of maximum amplitude for the density wave synaptic function were the location of the "particle" in our case action potential, has its highest probability.}, author = {Pinto Alexandra}, doi = {2018arXiv181008056P}, openaccess = {arXiv:1810.08056}, pdf = {https://ui.adsabs.harvard.edu/abs/2018arXiv181008056P}, title = {Wave to pulse generation. From oscillatory synapse to train of action potentials}, year = {2018} }