A spike-based head-movement and echolocation model of the bat superior colliculus

Echolocating bats use sonar to sense their environment and hunt for food in darkness. To understand this unusual sensory system from a computational perspective with aspirations towards developing high performance electronic implementations, we study the bat brain. The midbrain superior colliculus (SC) has been shown (in many species) to support multisensory integration and orientation behaviors, namely eye saccades and head turns. Previous computational models of the SC have emphasized the behavior typical to monkeys, barn owls, and cats. Using unique neurobiological data for the bat and incorporating knowledge from other species, a computational spiking model has been developed to produce both head-movement and sonar vocalization. The model accomplishes this with simple neuron equations and synapses, which is promising for implementation on a VLSI chip. This model can serve as a foundation for further developments, using new data from bat experiments, and be easily connected to spiking motor and vocalization systems

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

Hardware design of LIF with Latency neuron model with memristive STDP synapses

In this paper, the hardware implementation of a neuromorphic system is presented. This system is composed of a Leaky Integrate-and-Fire with Latency (LIFL) neuron and a Spike-Timing Dependent Plasticity (STDP) synapse. LIFL neuron model allows to encode more information than the common Integrate-and-Fire models, typically considered for neuromorphic implementations. In our system LIFL neuron is implemented using CMOS circuits while memristor is used for the implementation of the STDP synapse. A description of the entire circuit is provided. Finally, the capabilities of the proposed architecture have been evaluated by simulating a motif composed of three neurons and two synapses. The simulation results confirm the validity of the proposed system and its suitability for the design of more complex spiking neural network

Burst synchronization in two pulse-coupled resonate-and-fire neuron circuits

The present paper addresses burst synchronization in out of phase observed in two pulse-coupled resonate-and-fire neuron (RFN) circuits. The RFN circuit is a silicon spiking neuron that has second-order membrane dynamics and exhibits fast subthreshold oscillation of membrane potential. Due to such dynamics, the behavior of the RFN circuit is sensitive to the timing of stimuli. We investigated the effects of the sensitivity and the mutual interaction on the dynamic behavior of two pulse-coupled RFN circuits, and will demonstrate out of phase burst synchronization and bifurcation phenomena through circuit simulations.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

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

Burst synchronization in two pulse-coupled resonate-and-fire neuron circuits

The present paper addresses burst synchronization in out of phase observed in two pulse-coupled resonate-and-fire neuron (RFN) circuits. The RFN circuit is a silicon spiking neuron that has second-order membrane dynamics and exhibits fast subthreshold oscillation of membrane potential. Due to such dynamics, the behavior of the RFN circuit is sensitive to the timing of stimuli. We investigated the effects of the sensitivity and the mutual interaction on the dynamic behavior of two pulse-coupled RFN circuits, and will demonstrate out of phase burst synchronization and bifurcation phenomena through circuit simulations.Applications in Artificial Intelligence - ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Toward a Neuromorphic Microphone

Neuromorphic systems are used in variety of circumstances: as parts of sensory systems, for modeling parts of neural systems and for analog signal processing. In the sensory processing domain, neuromorphic systems can be considered in three parts: pre-transduction processing, transduction itself, and post-transduction processing. Neuromorphic systems include transducers for light, odors, and touch but so far neuromorphic applications in the sound domain have used standard microphones for transduction. We discuss why this is the case and describe what research has been done on neuromorphic approaches to transduction. We make a case for a change of direction toward systems where sound transduction itself has a neuromorphic component

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

A spatial contrast retina with on-chip calibration for neuromorphic spike-based AER vision systems

We present a 32 32 pixels contrast retina microchip that provides its output as an address event representation (AER) stream. Spatial contrast is computed as the ratio between pixel photocurrent and a local average between neighboring pixels obtained with a diffuser network. This current-based computation produces an important amount of mismatch between neighboring pixels, because the currents can be as low as a few pico-amperes. Consequently, a compact calibration circuitry has been included to trimm each pixel. Measurements show a reduction in mismatch standard deviation from 57% to 6.6% (indoor light). The paper describes the design of the pixel with its spatial contrast computation and calibration sections. About one third of pixel area is used for a 5-bit calibration circuit. Area of pixel is 58 m 56 m, while its current consumption is about 20 nA at 1-kHz event rate. Extensive experimental results are provided for a prototype fabricated in a standard 0.35- m CMOS process.This work was supported by Spanish Research Grants TIC2003-08164-C03-01 (SAMANTA), TEC2006-11730-C03-01 (SAMANTA-II), and EU grant IST-2001-34124 (CAVIAR). JCS was supported by the I3P program of the Spanish Research Council. RSG was supported by a national grant from the Spanish Ministry of Education and Science.Peer reviewe