DOI: 10.25549/usctheses-c3-204254
title: Emulating variability in the behavior of artificial central neurons

description: The variability in the behavior of artificial central neurons is the topic of this thesis. Variable behavior has been observed in biological neurons, resulting in changes in neural behavior that might be useful to capture in neuromorphic circuits. This thesis presents a neuromorphic cortical neuron with two main sources of intrinsic variability; synaptic neurotransmitter release and ion-channel variability, designed to be used in neural networks as part of the BioRC Biomimetic Real-Time Cortex project. This neuron has been designed and simulated using carbon nanotube transistors, one of several nanotechnologies under consideration to meet the challenges of scale presented by the cortex. ❧ Research results suggest that some instances of variability are stochastic, while other studies indicate that some instances of variability are chaotic. In this thesis, both possible sources of variability are considered by embedding either Gaussian noise or a chaotic signal into the neuromorphic or synaptic circuit and observing the results. ❧ Our overarching goal for the BioRC project is to demonstrate complex neural networks that possess memory and learning capability.  To this end, we believe the behavior of such networks would be enhanced by the addition of variability.  This thesis describes neurotransmitter-release variability and ion-channel variability modeled at the circuit level using carbon nanotube circuit elements. We include two different types of signal variabilities in the circuit, a signal with Gaussian noise and a chaotic signal. For neurotransmitter-release variability these signals are simulated as if they were generated internally in a synapse circuit to vary the neurotransmitter release in an unpredictable manner. Variation in neurotransmitter concentration in the synaptic cleft causes a change in the peak magnitude and duration of the postsynaptic potential. For ion-channel variability, these signals are simulated as if they were generated internally in an axon hillock circuit to change the firing mechanism. The variable signal could force the neuron to fire if the variability strength were sufficient or could prevent the neuron from firing. The variable signal is independent of the post-synaptic potential.  When there is no post-synaptic potential applied to the axon hillock (the cell membrane is at resting potential), the variable signal forcing the neuron to fire in fact models spontaneous firing of the neuron. ❧ For Gaussian noise, we include a file in our SPICE simulation consisting of random voltage samples that control neurotransmitter release volume. For chaotic signals, we present a chaotic signal generator circuit design and simulation using carbon nanotube transistor SPICE models, the output of which would likewise control neurotransmitter release. The circuit uses a chaotic piecewise linear one-dimensional map, implemented with switched-current circuits that can operate at high frequencies to generate a chaotic output current. ❧ The results presented in this thesis illustrate that neurotransmitter-release variability plays a beneficial role in the reliability of spike generation. In an examination of the reliability of spike generation, the precision of spike timing in the carbon nanotube circuit simulations was found to be dependent on stimulus (postsynaptic potential) transients. Postsynaptic potentials with low neurotransmitter release variability or without neurotransmitter release variability produced imprecise spike trains, whereas postsynaptic potentials with high neurotransmitter-release variability produce spike trains with reproducible timing. ❧ In simulation experiments, spontaneous firing of neurons due to ion-channel variability was demonstrated.  The thesis illustrates how, in one particular case, ion-channel variability could play a beneficial role in the reliability of transferring a train of spikes. The thesis also shows how ion-channel variability can halt infinite looping behavior with positive feedback in a neural network such as those thought to occur in obsessive-compulsive disorder. ❧ The design was simulated using carbon nanotube transistors and a SPICE simulation.