Effects of Synaptic and Myelin Plasticity on Learning in a Network of Kuramoto Phase Oscillators

Models of learning typically focus on synaptic plasticity. However, learning is the result of both synaptic and myelin plasticity. Specifically, synaptic changes often co-occur and interact with myelin changes, leading to complex dynamic interactions between these processes. Here, we investigate the implications of these interactions for the coupling behavior of a system of Kuramoto oscillators. To that end, we construct a fully connected, one-dimensional ring network of phase oscillators whose coupling strength (reflecting synaptic strength) as well as conduction velocity (reflecting myelination) are each regulated by a Hebbian learning rule. We evaluate the behavior of the system in terms of structural (pairwise connection strength and conduction velocity) and functional connectivity (local and global synchronization behavior). We find that for conditions in which a system limited to synaptic plasticity develops two distinct clusters both structurally and functionally, additional adaptive myelination allows for functional communication across these structural clusters. Hence, dynamic conduction velocity permits the functional integration of structurally segregated clusters. Our results confirm that network states following learning may be different when myelin plasticity is considered in addition to synaptic plasticity, pointing towards the relevance of integrating both factors in computational models of learning.Comment: 39 pages, 15 figures This work is submitted in Chaos: An Interdisciplinary Journal of Nonlinear Scienc

Self-Organized Criticality model for Brain Plasticity

Networks of living neurons exhibit an avalanche mode of activity, experimentally found in organotypic cultures. Here we present a model based on self-organized criticality and taking into account brain plasticity, which is able to reproduce the spectrum of electroencephalograms (EEG). The model consists in an electrical network with threshold firing and activity-dependent synapse strenghts. The system exhibits an avalanche activity power law distributed. The analysis of the power spectra of the electrical signal reproduces very robustly the power law behaviour with the exponent 0.8, experimentally measured in EEG spectra. The same value of the exponent is found on small-world lattices and for leaky neurons, indicating that universality holds for a wide class of brain models.Comment: 4 pages, 3 figure

Behavior Classification Using Multi-site LFP and ECoG Signals

Abstract-Deep Brain Stimulation (DBS) is an effective therapy that alleviates the motor signs of Parkinson’s disease (PD). Existing DBS is open loop, providing a time invariant stimulation pulse train that may generate cognitive, speech, and balance side effects. A closed-loop DBS system that utilizes appropriate physiological control variables may improve therapeutic results, reduce stimulation side effects, and extend battery life of pulse generators. Furthermore, by customizing DBS to a patient’s behavioral goal, side effects of stimulation may arise only when they are non-detrimental to the patient’s current goals. Therefore, classification of human behavior using physiological signals is an important step in the design of the next generation of closed-loop DBS systems. Ten subjects who were undergoing DBS implantation were recruited for the study. DBS leads were used to record bilateral STN-LFP activity and an electrocorticography (ECoG) strip was used to record field potentials over left prefrontal cortex. Subjects were cued to perform voluntary behaviors including left and right hand movement, left and right arm movement, mouth movement, and speech. Two types of algorithms were used to classify the subjects’ behavior, support vector machine (SVM) using linear, polynomial, and RBF kernels as well as lp-norm multiple kernel learning (MKL). Behavioral classification was performed using only LFP channels, only ECoG channels, and both LFP and ECoG channels. Features were extracted from the time-frequency representation of the signals. Phase locking values (PLV) between ECoG and LFP channels were calculated to determine connectivity between sites and aid in feature selection. Classification performance improved when multi-site signals were used with either SVM or MKL algorithms. Our experiments further show that the lp-norm MKL outperforms single kernel SVM-based classifiers in classifying behavioral tasks. References [1] H. M. Golshan, A. O. Hebb, S. J. Hanrahan, J. Nedrud, and M. H. Mahoor, “A multiple kernel learning approach for human behavioral task classification using STN-LFP signal,” EMBC, 38th IEEE International Conference on., pp.1030-1033, 2016. [2] H. M. Golshan, A. O. Hebb, S. J. Hanrahan, J. Nedrud, and M. H. Mahoor, “An FFT-based synchronization approach to recognize human behaviors using STN-LFP signal,” To appear in ICASSP, 42nd IEEE International Conference on., 2017

Human Behavior Recognition Ssing Brain LFP Signal in the Presence of the Stimulation Pulse

Design and Methodology This study concentrates on human behavior classification task using local field potential (LFP) signals recorded from three subjects with Parkinson’s disease (PD). Existing approaches mainly employ the LFP signals acquired under the stimulation/off condition. In practical situations, however, it is necessary to design a classification method capable of recognizing different human activities under the stimulation/on condition, where the classification task is more complicated due to the artifacts imposed by the high amplitude stimulation pulse (~1-3volts). We utilize the time-frequency representation of the acquired LFPs in the Beta frequency range (~10-30Hz) to develop a feature space based on which the classification is efficiently performed while the high frequency stimulation pulse (~130-180Hz) has no/limited impact on the classification performance. Original Data and Results All three participants had undergone DBS surgery with implanted DBS leads (Medtronic 3389, Minneapolis, MN, USA) in the subthalamic nucleus of the brain. The recording sessions required the participants to do several repetitions of designed “button press” and “reach” trials under the condition of stimulation on/off. On average, 60 recordings were performed for each trial. Our analysis on the power spectral density (PSD) of the data showed that the stimulation pulse mostly impacts the frequency components around the stimulation frequency (~140Hz). Using a linear-kernel SVM classifier for classifying the aforementioned trials based on the proposed feature space, we obtained a classification accuracy of ~88% and ~87% respectively for stimulation off and on cases. Conclusion PD incidence increases with advancing age and peaks among people in their 60s and 70s. The cost of PD in the United States is estimated to be $25 billion per year. Thus, advanced techniques to improve the performance of existing devices are highly demanded. Human behavior classification from brain signals is essential in developing the next generation of closed-loop deep brain stimulation (DBS) systems. A closed-loop DBS system that utilizes appropriate physiological control variables may improve therapeutic results, reduce stimulation side effects, and extend battery life of pulse generators

The continued optical to mid-IR evolution of V838 Monocerotis

The eruptive variable V838 Monocerotis gained notoriety in 2002 when it brightened nine magnitudes in a series of three outbursts and then rapidly evolved into an extremely cool supergiant. We present optical, near-IR, and mid-IR spectroscopic and photometric observations of V838 Monocerotis obtained between 2008 and 2012 at the Apache Point Observatory 3.5m, NASA IRTF 3m, and Gemini South 8m telescopes. We contemporaneously analyze the optical & IR spectroscopic properties of V838 Monocerotis to arrive at a revised spectral type L3 supergiant and effective temperature Teff~2000--2200 K. Because there are no existing optical observational data for L supergiants in the optical, we speculate that V838 Monocerotis may represent the prototype for L supergiants in this wavelength regime. We find a low level of Halpha emission present in the system, consistent with interaction between V838 Monocerotis and its B3V binary; however, we cannot rule out a stellar collision as the genesis event, which could result in the observed Halpha activity. Based upon a two-component blackbody fit to all wavelengths of our data, we conclude that, as of 2009, a shell of ejecta surrounded V838 Monocerotis at a radius of R=263+/-10 AU with a temperature of T=285+/-2 K. This result is consistent with IR interferometric observations from the same era and predictions from the Lynch et al. model of the expanding system, which provides a simple framework for understanding this complicated system.Comment: 6 pages, 2 tables, 6 figures; accepted to A

System size resonance in an attractor neural network

We study the response of an attractor neural network, in the ferromagnetic phase, to an external, time-dependent stimulus, which drives the system periodically two different attractors. We demonstrate a non-trivial dependance of the system via a system size resonance, by showing a signal amplification maximum at a certain finite size.Comment: 7 pages, 9 figures, submitted to Europhys. Let

Role of homeostasis in learning sparse representations

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components

Detecting Transits in Sparsely Sampled Surveys

The small sizes of low mass stars in principle provide an opportunity to find Earth-like planets and "super-Earths" in habitable zones via transits. Large area synoptic surveys like Pan-STARRS and LSST will observe large numbers of low mass stars, albeit with widely spaced (sparse) time sampling relative to the planets' periods and transit durations. We present simple analytical equations that can be used to estimate the feasibility of a survey by setting upper limits to the number of transiting planets that will be detected. We use Monte Carlo simulations to find upper limits for the number of transiting planets that may be discovered in the Pan-STARRS Medium Deep and 3-pi surveys. Our search for transiting planets and M-dwarf eclipsing binaries in the SDSS-II supernova data is used to illustrate the problems (and successes) in using sparsely sampled surveys.Comment: 7 pages, 2 figures, published in Proceedings of the Conference on Classification and Discovery in Large Astronomical Surveys, 200

The TRAPPIST survey of southern transiting planets. I. Thirty eclipses of the ultra-short period planet WASP-43 b

We present twenty-three transit light curves and seven occultation light curves for the ultra-short period planet WASP-43 b, in addition to eight new measurements of the radial velocity of the star. Thanks to this extensive data set, we improve significantly the parameters of the system. Notably, the largely improved precision on the stellar density (2.41+-0.08 rho_sun) combined with constraining the age to be younger than a Hubble time allows us to break the degeneracy of the stellar solution mentioned in the discovery paper. The resulting stellar mass and size are 0.717+-0.025 M_sun and 0.667+-0.011 R_sun. Our deduced physical parameters for the planet are 2.034+-0.052 M_jup and 1.036+-0.019 R_jup. Taking into account its level of irradiation, the high density of the planet favors an old age and a massive core. Our deduced orbital eccentricity, 0.0035(-0.0025,+0.0060), is consistent with a fully circularized orbit. We detect the emission of the planet at 2.09 microns at better than 11-sigma, the deduced occultation depth being 1560+-140 ppm. Our detection of the occultation at 1.19 microns is marginal (790+-320 ppm) and more observations are needed to confirm it. We place a 3-sigma upper limit of 850 ppm on the depth of the occultation at ~0.9 microns. Together, these results strongly favor a poor redistribution of the heat to the night-side of the planet, and marginally favor a model with no day-side temperature inversion.Comment: 14 pages, 6 tables, 11 figures. Accepted for publication in A&