Controlling a mobile robot with a biological brain

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots

Entraining and copying of temporal correlations in dissociated cultured neurons

Here we used multi-electrode array technology to examine the encoding of temporal information in dissociated hippocampal networks. We demonstrate that two connected populations of neurons can be trained to encode a defined time interval, and this memory trace persists for several hours. We also investigate whether the spontaneous firing activity of a trained network, can act as a template for copying the encoded time interval to a naive network. Such findings are of general significance for understanding fundamental principles of information storage and replicatio

Development of multi-depth probing 3D microelectrode array to record electrophysiological activity within neural cultures

Microelectrode arrays (MEAs) play a crucial role in investigating the electrophysiological activities of neuronal populations. Although two-dimensional neuronal cell cultures have predominated in neurophysiology in monitoring in-vitro the electrophysiological activity, recent research shifted toward culture using three-dimensional (3D) neuronal network structures for developing more sophisticated and realistic neuronal models. Nevertheless, many challenges remain in the electrophysiological analysis of 3D neuron cultures, among them the development of robust platforms for investigating the electrophysiological signal at multiple depths of the 3D neurons’ networks. While various 3D MEAs have been developed to probe specific depths within the layered nervous system, the fabrication of microelectrodes with different heights, capable of probing neural activity from the surface as well as from the different layers within the neural construct, remains challenging. This study presents a novel 3D MEA with microelectrodes of different heights, realized through a multi-stage mold-assisted electrodeposition process. Our pioneering platform allows meticulous control over the height of individual microelectrodes as well as the array topology, paving the way for the fabrication of 3D MEAs consisting of electrodes with multiple heights that could be tailored for specific applications and experiments. The device performance was characterized by measuring electrochemical impedance, and noise, and capturing spontaneous electrophysiological activity from neurospheroids derived from human induced pluripotent stem cells. These evaluations unequivocally validated the significant potential of our innovative multi-height 3D MEA as an avant-garde platform for in vitro 3D neuronal studies

Development of multi-depth probing 3D microelectrode array to record electrophysiological activity within neural cultures

Microelectrode arrays (MEAs) play a crucial role in investigating the electrophysiological activities of neuronal populations. Although two-dimensional neuronal cell cultures have predominated in neurophysiology in monitoring in-vitro the electrophysiological activity, recent research shifted toward culture using three-dimensional (3D) neuronal network structures for developing more sophisticated and realistic neuronal models. Nevertheless, many challenges remain in the electrophysiological analysis of 3D neuron cultures, among them the development of robust platforms for investigating the electrophysiological signal at multiple depths of the 3D neurons' networks. While various 3D MEAs have been developed to probe specific depths within the layered nervous system, the fabrication of microelectrodes with different heights, capable of probing neural activity from the surface as well as from the different layers within the neural construct, remains challenging. This study presents a novel 3D MEA with microelectrodes of different heights, realized through a multi-stage mold-assisted electrodeposition process. Our pioneering platform allows meticulous control over the height of individual microelectrodes as well as the array topology, paving the way for the fabrication of 3D MEAs consisting of electrodes with multiple heights that could be tailored for specific applications and experiments. The device performance was characterized by measuring electrochemical impedance, and noise, and capturing spontaneous electrophysiological activity from neurospheroids derived from human induced pluripotent stem cells. These evaluations unequivocally validated the significant potential of our innovative multi-height 3D MEA as an avant-garde platform for in vitro 3D neuronal studies

On the effect of long-term electrical stimulation on three-dimensional cell cultures: Hen embryo brain spheroids

A comprehensive dataset of multielectrode array recordings was collected from three-dimensional hen embryo brain cell cultures, termed spheroids, under long-term electrical stimulation. The aim is to understand the ongoing changes in the spiking activity under electrical stimulation within the lifetime of 14-72DIV of the neuronal networks contained therein. The spiking dynamics were analyzed and behavioral characteristics derived. Some effects on spiking patterns and exhaustion were followed in culture lifetime. With respect to the culture development, two main types of spiking exhaustion were found: one which materializes in the form of a drop in the sporadic (tonic) spiking frequency at the later maturation stages; and another associated with decreasing spiking train appearance throughout an experimental period. © 2008 Uroukov and Bull

Encoding temporal regularities and information copying in hippocampal circuits

Discriminating, extracting and encoding temporal regularities is a critical requirement in the brain, relevant to sensory-motor processing and learning. However, the cellular mechanisms responsible remain enigmatic; for example, whether such abilities require specific, elaborately organized neural networks or arise from more fundamental, inherent properties of neurons. Here, using multi-electrode array technology, and focusing on interval learning, we demonstrate that sparse reconstituted rat hippocampal neural circuits are intrinsically capable of encoding and storing sub-second-order time intervals for over an hour timescale, represented in changes in the spatial-temporal architecture of firing relationships among populations of neurons. This learning is accompanied by increases in mutual information and transfer entropy, formal measures related to information storage and flow. Moreover, temporal relationships derived from previously trained circuits can act as templates for copying intervals into untrained networks, suggesting the possibility of circuit-to-circuit information transfer. Our findings illustrate that dynamic encoding and stable copying of temporal relationships are fundamental properties of simple in vitro networks, with general significance for understanding elemental principles of information processing, storage and replication

Investigating computational properties of a neurorobotic closed loop system

This work arises as an attempt to increase and deepen the knowledge of the encoding method of the information by the nervous system. In particular, this study focuses on computational properties of neuronal cultures grown in vitro. Through a neuro-robotic close-loop system composed of either cortical or hippocampal cultures (plated on micro-electrode arrays) on one side and of a robot controlled by the cultures on the other side, it has been possible to analyze experimental dataopenEmbargo per motivi di segretezza e/o di proprietà dei risultati e/o informazioni sensibil

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