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Polyaniline-based atomic switches are material building blocks whose nanoscale structure and resultant neuromorphic character provide a new physical substrate for the development next-generation, nanoarchitectonic-enabled computing systems. Metal ion-doped devices consisting of a Ag/metal ion doped polyaniline/Pt sandwich structure were fabricated using an in situ wet process. The devices exhibited repeatable resistive switching between high (ON) and low (OFF) conductance states in both Ag+ and Cu2+ ion-doped devices. The threshold voltage for switching was>0.8 V and average ON/OFF conductance ratios (30 cycles for 3 samples) were 13 and 16 for Ag+ and Cu2+ devices, respectively. The ON state duration was determined by the decay to an OFF state after pulsed voltages of differing amplitude and frequency. The switching behaviour is analagous to short-term (STM) and long-term (LTM) memories of biological synapses. Memristive behaviour and evidence of quantized conductance were also observed and interpreted in terms of metal filament formation bridging the metal doped polymer layer. The successful realization of these properties within physical material systems indicate polyaniline frameworks as suitable neuromorphic substrates for in materia computing.

Reservoir computing (RC) is possible using physical systems. We have previously proposed an RC for ideal atomic switches. When temporal current fluctuations (noise) from the measurement of actual atomic switches are introduced into the proposed RC, performance degrades significantly. To address this issue, we propose novel methods for increasing the operating current range and observing the atomic switch several times to determine the average noise. Consequently, the memory capacity of the RC model increased, despite the presence of noise. To improve the precision of RC, we investigated the capacity and showed that changing the time constant of atomic switches results in an improvement.

Reservoir computing (RC) is possible using physical systems. We have previously proposed an RC for ideal atomic switches. When temporal current fluctuations (noise) from the measurement of actual atomic switches are introduced into the proposed RC, performance degrades significantly. To address this issue, we propose novel methods for increasing the operating current range and observing the atomic switch several times to determine the average noise. Consequently, the memory capacity of the RC model increased, despite the presence of noise. To improve the precision of RC, we investigated the capacity and showed that changing the time constant of atomic switches results in an improvement.

Reservoir computing (RC) is possible using physical systems. We have previously proposed an RC for ideal atomic switches. When temporal current fluctuations (noise) from the measurement of actual atomic switches are introduced into the proposed RC, performance degrades significantly. To address this issue, we propose novel methods for increasing the operating current range and observing the atomic switch several times to determine the average noise. Consequently, the memory capacity of the RC model increased, despite the presence of noise. To improve the precision of RC, we investigated the capacity and showed that changing the time constant of atomic switches results in an improvement.

Reservoir computing (RC) is possible using physical systems. We have previously proposed an RC for ideal atomic switches. When temporal current fluctuations (noise) from the measurement of actual atomic switches are introduced into the proposed RC, performance degrades significantly. To address this issue, we propose novel methods for increasing the operating current range and observing the atomic switch several times to determine the average noise. Consequently, the memory capacity of the RC model increased, despite the presence of noise. To improve the precision of RC, we investigated the capacity and showed that changing the time constant of atomic switches results in an improvement.

The invention of the internet began the age of information as well as exponentially increased the number of complex systems in our world. As the age of information comes to an end, so does the persevering trend known as Moore's Law. This means that the number of circuit elements on an integrated chip will no longer double every two years, nor will the processing speed of computers. Personal computers utilize the Von Neumann architecture which separates storage from processing. This separation causes information transfer lags as a computer processes information much faster than it can be fetched from information storage. Thus, to circumvent both the limitations on elemental packing, and areal density a movement into neuromorphic hardware has occurred. Neuromorphic chips seek to emulate brain-like processing of information through low-power, highly parallel, densely interconnected, and closely packed individual elements which have a non-intuitive entangled relationship. This work explored the potential of atomic switch networks (ASNs) for reservoir, natural, and unconventional computing, provides evidence for ASNs as complex adaptive systems operating in and around the edge of chaos, presents a new material for use in ASNs, and evaluates spoken digit recognition using reservoir computing. A second project herein explores the maturation of human pluripotent stem cell-derived cardiomyocytes for use in studying heart disease, which is the leading cause of death in the world. Maturation of these cells is significant to the field. Via a chemically defined differentiation regimen with a monolayer cell culture technique on top of a multi-electrode array for real-time measurements of electrophysiological properties, in vivo development was reproduced. Both systems described above required data science analysis of time-series multi-electrode array information.

The invention of the internet began the age of information as well as exponentially increased the number of complex systems in our world. As the age of information comes to an end, so does the persevering trend known as Moore's Law. This means that the number of circuit elements on an integrated chip will no longer double every two years, nor will the processing speed of computers. Personal computers utilize the Von Neumann architecture which separates storage from processing. This separation causes information transfer lags as a computer processes information much faster than it can be fetched from information storage. Thus, to circumvent both the limitations on elemental packing, and areal density a movement into neuromorphic hardware has occurred. Neuromorphic chips seek to emulate brain-like processing of information through low-power, highly parallel, densely interconnected, and closely packed individual elements which have a non-intuitive entangled relationship. This work explored the potential of atomic switch networks (ASNs) for reservoir, natural, and unconventional computing, provides evidence for ASNs as complex adaptive systems operating in and around the edge of chaos, presents a new material for use in ASNs, and evaluates spoken digit recognition using reservoir computing. A second project herein explores the maturation of human pluripotent stem cell-derived cardiomyocytes for use in studying heart disease, which is the leading cause of death in the world. Maturation of these cells is significant to the field. Via a chemically defined differentiation regimen with a monolayer cell culture technique on top of a multi-electrode array for real-time measurements of electrophysiological properties, in vivo development was reproduced. Both systems described above required data science analysis of time-series multi-electrode array information.

The emergent dynamical behaviors of biological neuronal networks and other natural, complex systems point towards new computing paradigms which can overcome limitations of digital computers. This work catalogues the development and characterization of an electronic circuit purpose built to exhibit emergent behaviors intended for use in neuromorphic computation. These circuits, atomic switch networks (ASNs), are fabricated through a self-assembly process that yields a highly interconnected network of silver nanowires with embedded inorganic synapses known as atomic switches. When stimulated with external bias voltage, ASNs are shown to possess the synaptic and memory properties of individual atomic switches, as well as network-specific behavior consisting of distributed, system wide switching events. These emergent behaviors exhibit striking similarity to those observed in many natural systems, including biological neural networks. Experiment and numerical simulations have provided proof of principle that ASNs are complex systems whose emergent behaviors may be used in implementations of neuromorphic computing paradigms such as reservoir computing. Furthermore, they demonstrate the utility of ASNs as a uniquely scalable physical platform useful for exploring complexity, neuroscience, and engineering.

The emergent dynamical behaviors of biological neuronal networks and other natural, complex systems point towards new computing paradigms which can overcome limitations of digital computers. This work catalogues the development and characterization of an electronic circuit purpose built to exhibit emergent behaviors intended for use in neuromorphic computation. These circuits, atomic switch networks (ASNs), are fabricated through a self-assembly process that yields a highly interconnected network of silver nanowires with embedded inorganic synapses known as atomic switches. When stimulated with external bias voltage, ASNs are shown to possess the synaptic and memory properties of individual atomic switches, as well as network-specific behavior consisting of distributed, system wide switching events. These emergent behaviors exhibit striking similarity to those observed in many natural systems, including biological neural networks. Experiment and numerical simulations have provided proof of principle that ASNs are complex systems whose emergent behaviors may be used in implementations of neuromorphic computing paradigms such as reservoir computing. Furthermore, they demonstrate the utility of ASNs as a uniquely scalable physical platform useful for exploring complexity, neuroscience, and engineering.

The emergent dynamical behaviors of biological neuronal networks and other natural, complex systems point towards new computing paradigms which can overcome limitations of digital computers. This work catalogues the development and characterization of an electronic circuit purpose built to exhibit emergent behaviors intended for use in neuromorphic computation. These circuits, atomic switch networks (ASNs), are fabricated through a self-assembly process that yields a highly interconnected network of silver nanowires with embedded inorganic synapses known as atomic switches. When stimulated with external bias voltage, ASNs are shown to possess the synaptic and memory properties of individual atomic switches, as well as network-specific behavior consisting of distributed, system wide switching events. These emergent behaviors exhibit striking similarity to those observed in many natural systems, including biological neural networks. Experiment and numerical simulations have provided proof of principle that ASNs are complex systems whose emergent behaviors may be used in implementations of neuromorphic computing paradigms such as reservoir computing. Furthermore, they demonstrate the utility of ASNs as a uniquely scalable physical platform useful for exploring complexity, neuroscience, and engineering.