Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Neural correlates and mechanisms of sounds localization in everyday reverberant settings

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 161-176).Nearly all listening environments-indoors and outdoors alike-are full of boundary surfaces (e.g., walls, trees, and rocks) that produce acoustic reflections. These reflections interfere with the direct sound arriving at a listener's ears, distorting the binaural cues for sound localization. Yet, human listeners have little difficulty localizing sounds in most settings. This thesis addresses fundamental questions regarding the neural basis of sound localization in everyday reverberant environments. In the first set of experiments, we investigate the effects of reverberation on the directional sensitivity of low-frequency auditory neurons sensitive to interaural time differences (ITD), the principal cue for localizing sound containing low frequency energy. Because reverberant energy builds up over time, the source location is represented relatively faithfully during the early portion of a sound, but this representation becomes increasingly degraded later in the stimulus. We show that the directional sensitivity of ITD-sensitive neurons in the auditory midbrain of anesthetized cats and awake rabbits follows a similar time course. However, the tendency of neurons to fire preferentially at the onset of a stimulus results in more robust directional sensitivity than expected, suggesting a simple mechanism for improving directional sensitivity in reverberation. To probe the role of temporal response dynamics, we use a conditioning paradigm to systematically alter temporal response patterns of single neurons. Results suggest that making temporal response patterns less onset-dominated typically leads to poorer directional sensitivity in reverberation. In parallel behavioral experiments, we show that human lateralization judgments are consistent with predictions from a population rate model for decoding the observed midbrain responses, suggesting a subcortical origin for robust sound localization in reverberant environments. In the second part of the thesis we examine the effects of reverberation on directional sensitivity of neurons across the tonotopic axis in the awake rabbit auditory midbrain. We find that reverberation degrades the directional sensitivity of single neurons, although the amount of degradation depends on the characteristic frequency and the type of binaural cues available. When ITD is the only available directional cue, low frequency neurons sensitive to ITD in the fine-time structure maintain better directional sensitivity in reverberation than high frequency neurons sensitive to ITD in the envelope. On the other hand, when both ITD and interaural level differences (ILD) cues are available, directional sensitivity is comparable throughout the tonotopic axis, suggesting that, at high frequencies, ILDs provide better directional information than envelope ITDs in reverberation. These findings can account for results from human psychophysical studies of spatial hearing in reverberant environments. This thesis marks fundamental progress towards elucidating the neural basis for spatial hearing in everyday settings. Overall, our results suggest that the information contained in the rate responses of neurons in the auditory midbrain is sufficient to account for human sound localization in reverberant environments.by Sasha Devore.Ph.D

Fourier Transforms

The 21st century ushered in a new era of technology that has been reshaping everyday life, simplifying outdated processes, and even giving rise to entirely new business sectors. Today, contemporary users of products and services expect more and more personalized products and services that can meet their unique needs. In that sense, it is necessary to further develop existing methods, adapt them to new applications, or even discover new methods. This book provides a thorough review of some methods that have an increasing impact on humanity today and that can solve different types of problems even in specific industries. Upgrading with Fourier Transformation gives a different meaning to these methods that support the development of new technologies and have a good projected acceleration in the future

Graphene and Beyond: Recent Advances in Two-Dimensional Materials Synthesis, Properties, and Devices

Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moir\ue9 pattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theory-guided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moir\ue9 engineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field

Programming the cerebellum

It is argued that large-scale neural network simulations of cerebellar cortex and nuclei, based on realistic compartmental models of me major cell populations, are necessary before the problem of motor learning in the cerebellum can be solved, [HOUK et al.; SIMPSON et al.

Brain Computations and Connectivity [2nd edition]

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

Timing and Time Perception: Procedures, Measures, and Applications

Timing and Time Perception: Procedures, Measures, and Applications is a one-of-a-kind, collective effort to present the most utilized and known methods on timing and time perception. Specifically, it covers methods and analysis on circadian timing, synchrony perception, reaction/response time, time estimation, and alternative methods for clinical/developmental research. The book includes experimental protocols, programming code, and sample results and the content ranges from very introductory to more advanced so as to cover the needs of both junior and senior researchers. We hope that this will be the first step in future efforts to document experimental methods and analysis both in a theoretical and in a practical manner