UC San Diego

Neuromorphic engineering pursues the design of electronic systems emulating function and structural organization of biological neural systems in silicon integrated circuits that embody similar physical principles.

The work in this dissertation extends neuromorphic engineering to neural interfaces that directly couple biological neurons to their equivalents in silicon integrated circuits, dynamically probing their function through silicon emulation of biophysical chemical and electrical synapses. Our aim in this work is to enable study of hybrid networks of biological and silicon neurons with highly configurable topology and biophysically based properties, providing windows on the inner workings of biological neural circuits from the cellular to the network levels, and hence promoting new synergies between theory in computational neuroscience and experimentation in systems neuroscience.

In the first part, membrane dynamics and ion channel kinetics of biological neurons, obtained from experimental electrophysiological data, were accurately mapped onto equivalent continuous-time analog dynamics in NeuroDyn, a highly reconfigurable neuromorphic silicon microchip. To this end, songbird individual neuron dynamics from intracellular neural recordings were extracted, modeled, and then mapped onto silicon neurons in NeuroDyn by data assimilation to estimate and configure biophysical parameters.

Further, the NeuroDyn framework was extended to serve as a versatile tool for biophysical dynamic clamp electrophysiology, connecting biological and silicon neurons through synthetic virtual chemical synapses. To this end, the response properties of five different types of chemical synapses, including both excitatory (AMPA, NMDA) and inhibitory (GABAA, GABAC, Glycine) ionotropic receptors were reproduced with neuromorphic integrated circuits. In addition, electrical synapses (gap junctions) were emulated in a network of four silicon neurons.

The second part entails the design, implementation and functional validation of high-density multi-channel neural interfaces, establishing bidirectional electrical communication between silicon artificial neurons and biological neurons at very large scale. Our work produced a neural interface system-on-chip (NISoC) with 1,024-channels of simultaneous electrical recording and stimulation at record noise-energy efficiency, with sub-µW power consumption per channel at 6 µVrms input referred voltage noise over 12.5 kHz signal bandwidth. Integrating an array of 32 × 32 electrodes on a 2mm × 2mm chip in 65nm CMOS, the NISoC supports both voltage and current clamping through a programmable interface, ranging 100~dB in voltage, and 120~dB in current, for high-resolution high-throughput electrophysiology.

Further, we demonstrated extended functionality for scalable multichannel in vitro intracellular electrophysiology in a second 256-channel hybridized NiSoC with sharp-tipped Pt nanowire electrodes deposited on the silicon top-metal surface, recording action potentials from rat cortical neurons cultured directly on top of the chip.

These advances combine to enable bidirectional communication between artificial neurons and biological neurons in vitro, with precise probing of neural function and flexible control over synaptic interactions ranging from intracellular dynamics of individual cells to network dynamics comprising potentially thousands of neurons. In addition to applications in closed-loop electrophysiology, in vitro neuromorphic neural interface can be used as testbed for prototyping the next generation of neuroprosthetics.