Dynamic clamp with StdpC software

Dynamic clamp is a powerful method that allows the introduction of artificial electrical components into target cells to simulate ionic conductances and synaptic inputs. This method is based on a fast cycle of measuring the membrane potential of a cell, calculating the current of a desired simulated component using an appropriate model and injecting this current into the cell. Here we present a dynamic clamp protocol using free, fully integrated, open-source software (StdpC, for spike timing-dependent plasticity clamp). Use of this protocol does not require specialist hardware, costly commercial software, experience in real-time operating systems or a strong programming background. The software enables the configuration and operation of a wide range of complex and fully automated dynamic clamp experiments through an intuitive and powerful interface with a minimal initial lead time of a few hours. After initial configuration, experimental results can be generated within minutes of establishing cell recording

Duty Cycle Maintenance in an Artificial Neuron

Neuroprosthetics is at the intersection of neuroscience, biomedical engineering, and physics. A biocompatible neuroprosthesis contains artificial neurons exhibiting biophysically plausible dynamics. Hybrid systems analysis could be used to prototype such artificial neurons. Biohybrid systems are composed of artificial and living neurons coupled via real-time computing and dynamic clamp. Model neurons must be thoroughly tested before coupled with a living cell. We use bifurcation theory to identify hazardous regimes of activity that may compromise biocompatibility and to identify control strategies for regimes of activity desirable for functional behavior. We construct real-time artificial neurons for the analysis of hybrid systems and demonstrate a mechanism through which an artificial neuron could maintain duty cycle independent of variations in period

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

Neural networks-on-chip for hybrid bio-electronic systems

PhD ThesisBy modelling the brains computation we can further our understanding of its function and develop novel treatments for neurological disorders. The brain is incredibly powerful and energy e cient, but its computation does not t well with the traditional computer architecture developed over the previous 70 years. Therefore, there is growing research focus in developing alternative computing technologies to enhance our neural modelling capability, with the expectation that the technology in itself will also bene t from increased awareness of neural computational paradigms. This thesis focuses upon developing a methodology to study the design of neural computing systems, with an emphasis on studying systems suitable for biomedical experiments. The methodology allows for the design to be optimized according to the application. For example, di erent case studies highlight how to reduce energy consumption, reduce silicon area, or to increase network throughput. High performance processing cores are presented for both Hodgkin-Huxley and Izhikevich neurons incorporating novel design features. Further, a complete energy/area model for a neural-network-on-chip is derived, which is used in two exemplar case-studies: a cortical neural circuit to benchmark typical system performance, illustrating how a 65,000 neuron network could be processed in real-time within a 100mW power budget; and a scalable highperformance processing platform for a cerebellar neural prosthesis. From these case-studies, the contribution of network granularity towards optimal neural-network-on-chip performance is explored

Approaches for unveiling the kinetic mechanisms of voltage gated ion channels in neurons

Includes vitaIonic currents drive cellular function within all living cells to perform highly specific tasks. For excitable cells, such as muscle and neurons, voltage-gated ion channels have finely tuned kinetics that allow the transduction of Action potentials to other cells. Voltage-gated ion channels are molecular machines that open and close depending on electrical potential. Neuronal firing rates are largely determined by the overall availability of voltage-gated Na+ and K+ currents.This work describes new approaches for collecting and analyzing experimental data that can be used to streamline experiments. Ion channels are composed of multimeric complexes regulated by intracellular factors producing complex kinetics. The stochastic behavior of thousands of individual ion hannels coordinates to produce cellular activity. To describe their activity and test hypotheses about the channel, experimenters often fit Markov models to a set of experimental data. Markov models are defined by a set of states, whose transitions described by rate constants. To improve the modeling process, we have developed computational approaches to introduce kinetic constraints that reduces the parameter search space. This work describes the implementation and mathematical transformations required to describe linear and non-linear parameter constraints that govern rate constants. Not all ion channel behaviors can easily be described by rate constants. Therefore, we developed and implemented a penalty-based mechanism that can be used to guide the optimization engine to produce a model with a desired behavior, such as single-channel open probability and use dependent effects. To streamline data collection for experiments in brain slice preparations, we developed a 3D virtual software environment that incorporates data from micro-positioning motors and scientific cameras in real-time. This environment provides positional feedback to the investigator and allows for the creation of data maps including both images and electrical recordings. We have also produced semi-automatic targeting procedures that simplifies the overall experimental experience. Experimentally, this work also examines how the kinetic mechanism of voltage gated Na channels regulates the neuronal firing of brainstem respiratory neurons. These raphe neurons are intrinsic pacemakers that do not rely on synaptic connections to elicit activity. I explored how intracellular calcium regulates the kinetics of TTX-sensitive Na+ currents using whole-cell patch clamp electrophysiology. Established with intracellular Ca2+ buffers, high [Ca2+] levels greater than ~7 [micro]M did not change the voltage dependence of steady-state activation and inactivation, but slightly slowed inactivation time course. However, the recovery from inactivation and use dependence inactivation is slowed by high intracellular [Ca2+]. Overall, these approaches described in this work have improved data acquisition and data analysis to create better ion channel models and enhance the electrophysiology experience.Includes bibliographical reference