Dynamics of embodied dissociated cortical cultures for the control of hybrid biological robots.

The thesis presents a new paradigm for studying the importance of interactions between an organism and its environment using a combination of biology and technology: embodying cultured cortical neurons via robotics. From this platform, explanations of the emergent neural network properties leading to cognition are sought through detailed electrical observation of neural activity. By growing the networks of neurons and glia over multi-electrode arrays (MEA), which can be used to both stimulate and record the activity of multiple neurons in parallel over months, a long-term real-time 2-way communication with the neural network becomes possible. A better understanding of the processes leading to biological cognition can, in turn, facilitate progress in understanding neural pathologies, designing neural prosthetics, and creating fundamentally different types of artificial cognition. Here, methods were first developed to reliably induce and detect neural plasticity using MEAs. This knowledge was then applied to construct sensory-motor mappings and training algorithms that produced adaptive goal-directed behavior. To paraphrase the results, most any stimulation could induce neural plasticity, while the inclusion of temporal and/or spatial information about neural activity was needed to identify plasticity. Interestingly, the plasticity of action potential propagation in axons was observed. This is a notion counter to the dominant theories of neural plasticity that focus on synaptic efficacies and is suggestive of a vast and novel computational mechanism for learning and memory in the brain. Adaptive goal-directed behavior was achieved by using patterned training stimuli, contingent on behavioral performance, to sculpt the network into behaviorally appropriate functional states: network plasticity was not only induced, but could be customized. Clinically, understanding the relationships between electrical stimulation, neural activity, and the functional expression of neural plasticity could assist neuro-rehabilitation and the design of neuroprosthetics. In a broader context, the networks were also embodied with a robotic drawing machine exhibited in galleries throughout the world. This provided a forum to educate the public and critically discuss neuroscience, robotics, neural interfaces, cybernetics, bio-art, and the ethics of biotechnology.Ph.D.Committee Chair: Steve M. Potter; Committee Member: Eric Schumacher; Committee Member: Robert J. Butera; Committee Member: Stephan P. DeWeerth; Committee Member: Thomas D. DeMars

Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli

Statistical characterization of cultured neural networks activity recorded via MEA

In questa tesi si sono analizzate le attività di neuroni in coltura accoppiati con un Multi-Electrode Array (MEA) a 60 canali. Le reti neurali in vitro sono influenzate da diversi fattori, come la densità di coltura, l'età e l'animale di provenienza dei neuroni. Lo scopo di questa tesi è quello di caratterizzare questa variabilità attraverso varie analisi statistiche. Utilizzando un software sviluppato in Python si sono estratte 40 misure per descrivere un ampio spettro delle attività delle cellule, come lo spiking e i network bursts. Sono state caratterizzate le distribuzioni statistiche di queste feature, ed una analisi PCA per vedere la segregazione dei dati nelle classi considerate. Sono poi state implementate una analisi con K-Means Clustering e una classificazione mediante Random Forest, per caratterizzare la separazione spontanea e mediante label dei vari tipi di misurazioni. É stato studiato anche l'effetto del sottocampionamento temporale sulle misure, determinando che anche con un quarto della serie temporale disponibile si sono potute estrarre misure significative. Un altro tipo di sottocampionamento preso in considerazione è stato quello spaziale. Anche qui le misure si sono dimostrate robuste riducendo il numero di elettrodi da cui estrarre il segnale, fino ad un minimo di 10 elettrodi. Questa analisi della robustezza alla variabilità e al sottocampionamento delle misure di attività spontanea di reti di neuroni in vitro mediante setup MEA é utile per studiare gli effetti di sostanze chimiche, stimoli elettrici o malattie sulle colture nervose

Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience

This essay is presented with two principal objectives in mind: first, to document the prevalence of fractals at all levels of the nervous system, giving credence to the notion of their functional relevance; and second, to draw attention to the as yet still unresolved issues of the detailed relationships among power law scaling, self-similarity, and self-organized criticality. As regards criticality, I will document that it has become a pivotal reference point in Neurodynamics. Furthermore, I will emphasize the not yet fully appreciated significance of allometric control processes. For dynamic fractals, I will assemble reasons for attributing to them the capacity to adapt task execution to contextual changes across a range of scales. The final Section consists of general reflections on the implications of the reviewed data, and identifies what appear to be issues of fundamental importance for future research in the rapidly evolving topic of this review

A Survey on Different Deep Learning Model for Human Activity Recognition based on Application

The field of human activity recognition (HAR) seeks to identify and classify an individual's unique movements or activities. However, recognizing human activity from video is a challenging task that requires careful attention to individuals, their behaviors, and relevant body parts. Multimodal activity recognition systems are necessary for many applications, including video surveillance systems, human-computer interfaces, and robots that analyze human behavior. This study provides a comprehensive analysis of recent breakthroughs in human activity classification, including different approaches, methodologies, applications, and limitations. Additionally, the study identifies several challenges that require further investigation and improvements. The specifications for an ideal human activity recognition dataset are also discussed, along with a thorough examination of the publicly available human activity classification datasets

Plasticity and Adaptation in Neuromorphic Biohybrid Systems

Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel \u201cbiohybrid\u201d experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering. \ua9 2020 The Author(s

Neuronal assembly dynamics in supervised and unsupervised learning scenarios

The dynamic formation of groups of neurons—neuronal assemblies—is believed to mediate cognitive phenomena at many levels, but their detailed operation and mechanisms of interaction are still to be uncovered. One hypothesis suggests that synchronized oscillations underpin their formation and functioning, with a focus on the temporal structure of neuronal signals. In this context, we investigate neuronal assembly dynamics in two complementary scenarios: the first, a supervised spike pattern classification task, in which noisy variations of a collection of spikes have to be correctly labeled; the second, an unsupervised, minimally cognitive evolutionary robotics tasks, in which an evolved agent has to cope with multiple, possibly conflicting, objectives. In both cases, the more traditional dynamical analysis of the system’s variables is paired with information-theoretic techniques in order to get a broader picture of the ongoing interactions with and within the network. The neural network model is inspired by the Kuramoto model of coupled phase oscillators and allows one to fine-tune the network synchronization dynamics and assembly configuration. The experiments explore the computational power, redundancy, and generalization capability of neuronal circuits, demonstrating that performance depends nonlinearly on the number of assemblies and neurons in the network and showing that the framework can be exploited to generate minimally cognitive behaviors, with dynamic assembly formation accounting for varying degrees of stimuli modulation of the sensorimotor interactions