Bat azimuthal echolocation using interaural level differences: modeling and implementation by a VLSI-based hardware system

Bats have long fascinated both scientists and engineers due to their superb ability to use echolocation to fly with speed and agility through complex natural environments in complete darkness. This dissertation presents a neuromorphic VLSI circuit model of bat azimuthal echolocation. Interaural level differences (ILDs) are the cues for bat azimuthal echolocation and are also the primary cues used by other mammals to localize high frequency sounds. The fact that neurons in bats respond to short echoes by one or two spikes strongly suggests that the conventionally used firing rate is an unlikely code. The operation of first spike latency in ILD computation and transformation is investigated in a network of spiking neurons linking the lateral superior olive (LSO), dorsal nucleus of the lateral lemniscus (DNLL), and inferior colliculus (IC). The results of the investigation suggest that spatially distributed first spike latencies can serve as a fast code for azimuth that can be ``read-out'' by ascending stages. With the hardware echolocation model that uses spike timing representation, we study how multiple echoes can affect bat echolocation and demonstrate that the response to multiple sounds is not a simple linear addition of the response to single sounds. By developing functional models of the bat echolocation system, we can study the efficient implementation demonstrated by nature. For example, variations among analog VLSI circuit units due to the unavoidable transistor mismatch - traditionally thought of as a hurdle to overcome - have been found beneficial in generating the desired diversity of response that is similar to their neural counterparts. This work advocates the use and design of summating and exponentially decaying synapses. A compact and easily controllable synapse circuit has found an application in achieving a linear temporal spike summation by operating with a very short time constants. It has also been applied in modeling a nonlinear intensity-latency trading by working with a long synaptic time constant. We propose a new synapse circuit model that is compatible with those used in computational models and implementable by CMOS transistors operating in the subthreshold region

A Biologically Inspired Model of Bat Echolocation In A Cluttered Environment With Inputs Designed From Field Recordings

Thesis advisor: Willie J. PadillaThesis advisor: David C. MountainBat echolocation strategies and neural processing of acoustic information, with a focus on cluttered environments, is investigated in this study. How a bat processes the dense field of echoes received while navigating and foraging in the dark is not well understood. While several models have been developed to describe the mechanisms behind bat echolocation, most are based in mathematics rather than biology, and focus on either peripheral or neural processing--not exploring how these two levels of processing are vitally connected. Current echolocation models also do not use habitat specific acoustic input, or account for field observations of echolocation strategies. Here, a new approach to echolocation modeling is described capturing the full picture of echolocation from signal generation to a neural picture of the acoustic scene. A biologically inspired echolocation model is developed using field research measurements of the interpulse interval timing used by a frequency modulating (FM) bat in the wild, with a whole method approach to modeling echolocation including habitat specific acoustic inputs, a biologically accurate peripheral model of sound processing by the outer, middle, and inner ear, and finally a neural model incorporating established auditory pathways and neuron types with echolocation adaptations. Field recordings analyzed underscore bat sonar design differences observed in the laboratory and wild, and suggest a correlation between interpulse interval groupings and increased clutter. The scenario model provides habitat and behavior specific echoes and is a useful tool for both modeling and behavioral studies, and the peripheral and neural model show that spike-time information and echolocation specific neuron types can produce target localization in the midbrain.Thesis (PhD) — Boston College, 2014.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Physics

NEUROMORPHIC VLSI REALIZATION OF THE HIPPOCAMPAL FORMATION AND THE LATERAL SUPERIOR OLIVE

In this work, the focus is on realizing the function of the hippocampal formation (HF) and the lateral superior olive (LSO) in electronic circuits. The first major contribution of this dissertation is to realize the function of the HF in silicon. This was based on the GRIDSmap model and the Bayesian integration. For this, two novel circuits were designed and integrated with others. The first circuit was that of a Bayesian integration synapse which can perform Bayesian integration at the single neuron level. The second circuit was that of a velocity integrator which is so compact that it can enable integration of the entire system on a single chip compared to its predecessors which would have needed 27 chips! However, since the computational neuroscience models of the hippocampal place cells do not explain all the characteristics observed empirically, a novel model for the place cells, based on the sensori-motor integration of inputs is proposed. This is the second major contribution of this thesis. The third major contribution is to demonstrate a VLSI system which can perform azimuthal localization based on population response of the LSO. This system was based on the Reed and Blum's model of the LSO. For this, a novel circuit of a second order synapse and that of a conductance neuron was designed and integrated with other circuits. This synapse circuit can produce an output current whose peak is delayed and is proportional to the number of inputs it receives. The HF is thought to aid in spatial navigation and the LSO is thought to be involved in azimuthal localization of sounds both of which are useful for autonomous robotic spatial navigation. Hence, silicon realization of these two will be useful in robotics which is an area of interest for the neuromorphic engineers

Acoustic modelling of bat pinnae utilising the TLM method

This thesis describes the numerical modelling of bioacoustic structures, the focus being the outer ear or pinnae of the Rufous Horseshoe bat (Rhinolophus rouxii). There have been several novel developments derived from this work including: • A method of calculating directionality based on the sphere with a distribution of measuring points such that each lies in an equal area segment. • Performance estimation of the pinna by considering the directionality of an equivalent radiating aperture. • A simple synthetic geometry that appears to give similar performance to a bat pinna. The outcome of applying the methods have yielded results that agree with measurements, indeed, this work is the first time TLM has been applied to a structure of this kind. It paves the way towards a greater understanding of bioacoustics and ultimately towards generating synthetic structures that can perform as well as those found in the natural world

ORIENTING IN 3D SPACE: BEHAVIORAL AND NEUROPHYSIOLOGICAL STUDIES IN BIG BROWN BATS

In their natural environment, animals engage in a wide range of behavioral tasks that require them to orient to stimuli in three-dimensional space, such as navigating around obstacles, reaching for objects and escaping from predators. Echolocating bats, for example, have evolved a high-resolution 3D acoustic orienting system that allows them to localize and track small moving targets in azimuth, elevation and range. The bat’s active control over the features of its echolocation signals contributes directly to the information represented in its sonar receiver, and its adaptive adjustments in sonar signal design provide a window into the acoustic features that are important for different behavioral tasks. When bats inspect sonar objects and require accurate 3D localization of targets, they produce sonar sound groups (SSGs), which are clusters of sonar calls produced at short intervals and flanked by long interval calls. SSGs are hypothesized to enhance the bat’s range resolution, but this hypothesis has not been directly tested. We first, in Chapter 2, provide a comprehensive comparison of SSG production of bats flying in the field and in the lab under different environmental conditions. Further, in Chapter 3, we devise an experiment to specifically compare SSG production under conditions when target motion is predictable and unpredictable, with the latter mimicking natural conditions where bats chase erratically moving prey. Data from both of these studies are consistent with the hypothesis that SSGs improve the bat’s spatio-temporal resolution of target range, and provide a behavioral foundation for the analysis and interpretation of neural recording data in chapters 4 and 6. The complex orienting behaviors exhibited by animals can be understood as a feedback loop between sensing and action. A primary brain structure involved in sensorimotor integration is the midbrain superior colliculus (SC). The SC is a widely studied brain region and has been implicated in species-specific orienting behaviors. However, most studies of the SC have investigated its functional organization using synthetic 2D (azimuth and elevation) stimuli in restrained animals, leaving gaps in our knowledge of how 3D space (azimuth, elevation and distance) is represented in the CNS. In contrast, the representation of stimulus distance in the auditory systems of bats has been widely studied. Almost all of these studies have been conducted in passively listening bats, thus severing the loop between sensing and action and leaving gaps in our knowledge regarding how target distance is represented in the auditory system of actively echolocating bats. In chapters 4, 5 and 6, we attempt to fill gaps in our knowledge by recording from the SC of free flying echolocating bats engaged in a naturalistic navigation task where bats produce SSGs. In chapter 4, we provide a framework to compute time-of-arrival and direction of the instantaneous echo stimuli received at the bats ears. In chapters 5 and 6, we provide an algorithm to classify neural activity in the SC as sensory, sensorimotor and premotor and then compute spatial receptive fields of SC neurons. Our results show that neurons in the SC of the free-flying echolocating bat respond selectively to stimulus azimuth, elevation and range. Importantly, we find that SC neuron response profiles are modulated by the bat’s behavioral state, indicated by the production of SSG. Broadly, we use both behavior and electrophysiology to understand the action-perception loop that supports spatial orientation by echolocation. We believe that the results and methodological advances presented here will open doors to further studies of sensorimotor integration in freely behaving animals

Bio-inspired learning and hardware acceleration with emerging memories

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called action potentials. Motivated by these facts, the Spiking Neural Networks (SNNs), which are a bio-plausible version of neural networks have been proposed as an alternative computing paradigm where the timing of spikes generated by artificial neurons is central to its learning and inference capabilities. This dissertation demonstrates the computational power of the SNNs using conventional CMOS and emerging nanoscale hardware platforms. The first half of this dissertation presents an SNN architecture which is trained using a supervised spike-based learning algorithm for the handwritten digit classification problem. This network achieves an accuracy of 98.17% on the MNIST test data-set, with about 4X fewer parameters compared to the state-of-the-art neural networks achieving over 99% accuracy. In addition, a scheme for parallelizing and speeding up the SNN simulation on a GPU platform is presented. The second half of this dissertation presents an optimal hardware design for accelerating SNN inference and training with SRAM (Static Random Access Memory) and nanoscale non-volatile memory (NVM) crossbar arrays. Three prominent NVM devices are studied for realizing hardware accelerators for SNNs: Phase Change Memory (PCM), Spin Transfer Torque RAM (STT-RAM) and Resistive RAM (RRAM). The analysis shows that a spike-based inference engine with crossbar arrays of STT-RAM bit-cells is 2X and 5X more efficient compared to PCM and RRAM memories, respectively. Furthermore, the STT-RAM design has nearly 6X higher throughput per unit Watt per unit area than that of an equivalent SRAM-based (Static Random Access Memory) design. A hardware accelerator with on-chip learning on an STT-RAM memory array is also designed, requiring $16$ bits of floating-point synaptic weight precision to reach the baseline SNN algorithmic performance on the MNIST dataset. The complete design with STT-RAM crossbar array achieves nearly 20X higher throughput per unit Watt per unit mm^2 than an equivalent design with SRAM memory. In summary, this work demonstrates the potential of spike-based neuromorphic computing algorithms and its efficient realization in hardware based on conventional CMOS as well as emerging technologies. The schemes presented here can be further extended to design spike-based systems that can be ubiquitously deployed for energy and memory constrained edge computing applications