Closed-loop approaches for innovative neuroprostheses

The goal of this thesis is to study new ways to interact with the nervous system in case of damage or pathology. In particular, I focused my effort towards the development of innovative, closed-loop stimulation protocols in various scenarios: in vitro, ex vivo, in vivo

Network dynamics in the neural control of birdsong

Sequences of stereotyped actions are central to the everyday lives of humans and animals, from the kingfisher's dive to the performance of a piano concerto. Lashley asked how neural circuits managed this feat nearly 6 decades ago, and to this day it remains a fundamental question in neuroscience. Toward answering this question, vocal performance in the songbird was used as a model to study the performance of learned, stereotyped motor sequences. The first component of this work considers the song motor cortical zone HVC in the zebra finch, an area that sends precise timing signals to both the descending motor pathway, responsible for stereotyped vocal performance in the adult, and the basal ganglia, which is responsible for both motor variability and song learning. Despite intense interest in HVC, previous research has exclusively focused on describing the activity of small numbers of neurons recorded serially as the bird sings. To better understand HVC network dynamics, both single units and local field potentials were sampled across multiple electrodes simultaneously in awake behaving zebra finches. The local field potential and spiking data reveal a stereotyped spatio-temporal pattern of inhibition operating on a 30 ms time-scale that coordinates the neural sequences in principal cells underlying song. The second component addresses the resilience of the song circuit through cutting the motor cortical zone HVC in half along one axis. Despite this large-scale perturbation, the finch quickly recovers and sings a near-perfect song within a single day. These first two studies suggest that HVC is functionally organized to robustly generate neural dynamics that enable vocal performance. The final component concerns a statistical study of the complex, flexible songs of the domesticated canary. This study revealed that canary song is characterized by specific long-range correlations up to 7 seconds long-a time-scale more typical of human music than animal vocalizations. Thus, the neural sequences underlying birdsong must be capable of generating more structure and complexity than previously thought

Unveiling the frontiers of deep learning: innovations shaping diverse domains

Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

Neural Network Dynamics of Visual Processing in the Higher-Order Visual System

Vision is one of the most important human senses that facilitate rich interaction with the external environment. For example, optimal spatial localization and subsequent motor contact with a specific physical object amongst others requires a combination of visual attention, discrimination, and sensory-motor coordination. The mammalian brain has evolved to elegantly solve this problem of transforming visual input into an efficient motor output to interact with an object of interest. The frontal and parietal cortices are two higher-order (i.e. processes information beyond simple sensory transformations) brain areas that are intimately involved in assessing how an animal’s internal state or prior experiences should influence cognitive-behavioral output. It is well known that activity within each region and functional interactions between both regions are correlated with visual attention, decision-making, and memory performance. Therefore, it is not surprising that impairment in the fronto-parietal circuit is often observed in many psychiatric disorders. Network- and circuit-level fronto-parietal involvement in sensory-based behavior is well studied; however, comparatively less is known about how single neuron activity in each of these areas can give rise to such macroscopic activity. The goal of the studies in this dissertation is to address this gap in knowledge through simultaneous recordings of cellular and population activity during sensory processing and behavioral paradigms. Together, the combined narrative builds on several themes in neuroscience: variability of single cell function, population-level encoding of stimulus properties, and state and context-dependent neural dynamics.Doctor of Philosoph

The temporal pattern of impulses in primary afferents analogously encodes touch and hearing information

An open question in neuroscience is the contribution of temporal relations between individual impulses in primary afferents in conveying sensory information. We investigated this question in touch and hearing, while looking for any shared coding scheme. In both systems, we artificially induced temporally diverse afferent impulse trains and probed the evoked perceptions in human subjects using psychophysical techniques. First, we investigated whether the temporal structure of a fixed number of impulses conveys information about the magnitude of tactile intensity. We found that clustering the impulses into periodic bursts elicited graded increases of intensity as a function of burst impulse count, even though fewer afferents were recruited throughout the longer bursts. The interval between successive bursts of peripheral neural activity (the burst-gap) has been demonstrated in our lab to be the most prominent temporal feature for coding skin vibration frequency, as opposed to either spike rate or periodicity. Given the similarities between tactile and auditory systems, second, we explored the auditory system for an equivalent neural coding strategy. By using brief acoustic pulses, we showed that the burst-gap is a shared temporal code for pitch perception between the modalities. Following this evidence of parallels in temporal frequency processing, we next assessed the perceptual frequency equivalence between the two modalities using auditory and tactile pulse stimuli of simple and complex temporal features in cross-sensory frequency discrimination experiments. Identical temporal stimulation patterns in tactile and auditory afferents produced equivalent perceived frequencies, suggesting an analogous temporal frequency computation mechanism. The new insights into encoding tactile intensity through clustering of fixed charge electric pulses into bursts suggest a novel approach to convey varying contact forces to neural interface users, requiring no modulation of either stimulation current or base pulse frequency. Increasing control of the temporal patterning of pulses in cochlear implant users might improve pitch perception and speech comprehension. The perceptual correspondence between touch and hearing not only suggests the possibility of establishing cross-modal comparison standards for robust psychophysical investigations, but also supports the plausibility of cross-sensory substitution devices

A MODELING PERSPECTIVE ON DEVELOPING NATURALISTIC NEUROPROSTHETICS USING ELECTRICAL STIMULATION

Direct electrical stimulation of neurons has been an important tool for understanding the brain and neurons, since the field of neuroscience began. Electrical stimulation was used to first understand sensation, the mapping of the brain, and more recently function, and, as our understanding of neurological disorders has advanced, it has become an increasingly important tool for interacting with neurons to design and carry out treatments. The hardware for electrical stimulation has greatly improved during the last century, allowing smaller scale, implantable treatments for a variety of disorders, from loss of sensations (hearing, vision, balance) to Parkinson’s disease and depression. Due to the clinical success of these treatments for a variety of impairments today, there are millions of neural implant users around the globe, and interest in medical implants and implants for human-enhancement are only growing. However, present neural implant treatments restore only limited function compared to natural systems. A limiting factor in the advancement of electrical stimulation-based treatments has been the restriction of using charge-balanced and typically short sub-millisecond pulses in order to safely interact with the brain, due to a reliance on durable, metal electrodes. Material science developments have led to more flexible electrodes that are capable of delivering more charge safely, but a focus has been on density of electrodes implanted over changing the waveform of electrical stimulation delivery. Recently, the Fridman lab at Johns Hopkins University developed the Freeform Stimulation (FS)– an implantable device that uses a microfluidic H-bridge architecture to safely deliver current for prolonged periods of time and that is not restricted to charge-balanced waveforms. In this work, we refer to these non-restricted waveforms as galvanic stimulation, which is used as an umbrella term that encompasses direct current, sinusoidal current, or alternative forms of non-charge-balanced current. The invention of the FS has opened the door to usage of galvanic stimulation in neural implants, begging an exploration of the effects of local galvanic stimulation on neural function. Galvanic stimulation has been used in the field of neuroscience, prior to concerns about safe long-term interaction with neurons. Unlike many systems, it had been historically used in the vestibular system internally and in the form of transcutaneous stimulation to this day. Historic and recent studies confirm that galvanic stimulation of the vestibular system has more naturalistic effects on neural spike timing and on induced behavior (eye velocities) than pulsatile stimulation, the standard in neural implants now. Recent vestibular stimulation studies with pulses also show evidence of suboptimal responses of neurons to pulsatile stimulation in which suprathreshold pulses only induce about half as many action potentials as pulses. This combination of results prompted an investigation of differences between galvanic and pulsatile electrical stimulation in the vestibular system. The research in this dissertation uses detailed biophysical modeling of single vestibular neurons to investigate the differences in the biophysical mechanism of galvanic and pulsatile stimulation. In Chapter 2, a more accurate model of a vestibular afferent is constructed from an existing model, and it is used to provide a theory for how galvanic stimulation produces a number of known effects on vestibular afferents. In Chapter 3, the same model is used to explain why pulsatile stimulation produces fewer action potentials than expected, and the results show that pulse amplitude, pulse rate, and the spontaneous activity of neurons at the axon have a number of interactions that lead to several non-monotonic relationships between pulse parameters and induced firing rate. Equations are created to correct for these non-monotonic relationships and produce intended firing rates. Chapter 4 focuses on how to create a neural implant that induces more naturalistic firing using the scientific understanding from Chapters 2 and 3 and machine learning. The work concludes by describing the implications of these findings for interacting with neurons and population and network scales and how this may make electrical stimulation increasingly more suited for treating complex network-level and psychiatric disorders

Tracking Sound Dynamics in Human Auditory Cortex: New macroscopic perspectives from MEG

Both the external world and our internal world are full of changing activities , and the question of how these two dynamic systems are linked constitutes the most intriguing and fundamental question in neuroscience and cognitive science. This study specifically investigates the processing and representation of sound dynamic information in human auditory cortex using magnetoencephalography (MEG), a non-invasive brain imaging technique whose high temporal resolution (on the order of ~1ms) makes it an appropriate tool for studying the neural correlates of dynamic auditory information. The other goal of this study is to understand the essence of the macroscopic activities reflected in non-invasive brain imaging experiments, specifically focusing on MEG. Invasive single-cell recordings in animals have yielded a large amount of information about how the brain works at a microscopic level. However, there still exist large gaps in our understanding of the relationship between the activities recorded at the microscopic level in animals and at the macroscopic level in humans, which have yet to be reconciled in terms of their different spatial scales and activities format, making a unified knowledge framework still unsuccessful. In this study, natural speech sentences and sounds containing speech-like temporal dynamic features are employed to probe the human auditory system. The recorded MEG signal is found to be well correlated with the stimulus dynamics via amplitude modulation (AM) and/or phase modulation (PM) mechanisms. Specifically, oscillations at various frequency bands are found to be the main information-carrying elements of the MEG signal, and the two major parameters of these endogenous brain rhythms, amplitude and phase, are modulated by incoming sensory stimulus dynamics, corresponding to AM and PM mechanism, to track sound dynamics. Crucially, such modulation tracking is found to be correlated with human perception and behavior. This study suggests that these two dynamic and complex systems, the external and internal worlds, systematically communicate and are coupled via modulation mechanism, leading to a reverberating flow of information embedded in oscillating waves in human cortex. The results also have implications for brain imaging studies, suggesting that these recorded macroscopic activities reflect brain state, the more close neural correlate of high-level cognitive behavior