A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes

Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016

Modeling of Cellular Automata and Agent-Based Complex Systems

The term \u27complex systems\u27 may sound terrifying whenever you come across it as it depicts an overall collective structure which indeed can live up to its name; but when you comprehend the system at its fundamental level by stripping to its simpler multiple- interacting individual parts, the insights it provides may be used to describe and understand different problems ranging from atomic particles to the economics of societies and evolution. The simple laws can be used to simulate the behaviors of disparate complex systems. In this thesis, a brief study is done emulating few such complex systems through programming techniques like cellular automata and neural networks. The patterns of complex behavior obtained are also classified respectively along with the help of Conway\u27s game of life; the working of an autonomous and self organizing organism is simulated in a program written to show the complex patterns formed by a virtual ant. Then an important aspect of competition and cooperation among these agents is shown through game theory and dilemmas which throws light on the essence of survival of complex systems. A formal study is also done on the uses of artificial neural networks as associative memories and pattern recognizers

A Computational Investigation of Neural Dynamics and Network Structure

With the overall goal of illuminating the relationship between neural dynamics and neural network structure, this thesis presents a) a computer model of a network infrastructure capable of global broadcast and competition, and b) a study of various convergence properties of spike-timing dependent plasticity (STDP) in a recurrent neural network. The first part of the thesis explores the parameter space of a possible Global Neuronal Workspace (GNW) realised in a novel computational network model using stochastic connectivity. The structure of this model is analysed in light of the characteristic dynamics of a GNW: broadcast, reverberation, and competition. It is found even with careful consideration of the balance between excitation and inhibition, the structural choices do not allow agreement with the GNW dynamics, and the implications of this are addressed. An additional level of competition – access competition – is added, discussed, and found to be more conducive to winner-takes-all competition. The second part of the thesis investigates the formation of synaptic structure due to neural and synaptic dynamics. From previous theoretical and modelling work, it is predicted that homogeneous stimulation in a recurrent neural network with STDP will create a self-stabilising equilibrium amongst synaptic weights, while heterogeneous stimulation will induce structured synaptic changes. A new factor in modulating the synaptic weight equilibrium is suggested from the experimental evidence presented: anti-correlation due to inhibitory neurons. It is observed that the synaptic equilibrium creates competition amongst synapses, and those specifically stimulated during heterogeneous stimulation win out. Further investigation is carried out in order to assess the effect that more complex STDP rules would have on synaptic dynamics, varying parameters of a trace STDP model. There is little qualitative effect on synaptic dynamics under low frequency (< 25Hz) conditions, justifying the use of simple STDP until further experimental or theoretical evidence suggests otherwise

A neurobiological and computational analysis of target discrimination in visual clutter by the insect visual system.

Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons.Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 200

Causally Investigating Cortical Dynamics and Signal Processing by Targeting Natural System Attractors With Precisely Timed (Electrical) Stimulation

Electrical stimulation is a promising tool for interacting with neuronal dynamics to identify neural mechanisms that underlie cognitive function. Since effects of a single short stimulation pulse typically vary greatly and depend on the current network state, many experimental paradigms have rather resorted to continuous or periodic stimulation in order to establish and maintain a desired effect. However, such an approach explicitly leads to forced and “unnatural” brain activity. Further, continuous stimulation can make it hard to parse the recorded activity and separate neural signal from stimulation artifacts. In this study we propose an alternate strategy: by monitoring a system in realtime, we use the existing preferred states or attractors of the network and apply short and precise pulses in order to switch between those states. When pushed into one of its attractors, one can use the natural tendency of the system to remain in such a state to prolong the effect of a stimulation pulse, opening a larger window of opportunity to observe the consequences on cognitive processing. To elaborate on this idea, we consider flexible information routing in the visual cortex as a prototypical example. When processing a stimulus, neural populations in the visual cortex have been found to engage in synchronized gamma activity. In this context, selective signal routing is achieved by changing the relative phase between oscillatory activity in sending and receiving populations (communication through coherence, CTC). In order to explore how perturbations interact with CTC, we investigate a network of interneuronal gamma (ING) oscillators composed of integrate-and-fire neurons exhibiting similar synchronization and signal routing phenomena. We develop a closed-loop stimulation paradigm based on the phase-response characteristics of the network and demonstrate its ability to establish desired synchronization states. By measuring information content throughout the model, we evaluate the effect of signal contamination caused by the stimulation in relation to the magnitude of the injected pulses and intrinsic noise in the system. Finally, we demonstrate that, up to a critical noise level, precisely timed perturbations can be used to artificially induce the effect of attention by selectively routing visual signals to higher cortical areas

Neuromorphic Engineering Editors' Pick 2021

This collection showcases well-received spontaneous articles from the past couple of years, which have been specially handpicked by our Chief Editors, Profs. André van Schaik and Bernabé Linares-Barranco. The work presented here highlights the broad diversity of research performed across the section and aims to put a spotlight on the main areas of interest. All research presented here displays strong advances in theory, experiment, and methodology with applications to compelling problems. This collection aims to further support Frontiers’ strong community by recognizing highly deserving authors

Neural architecture for echo suppression during sound source localization based on spiking neural cell models

Zusammenfassung Diese Arbeit untersucht die biologischen Ursachen des psycho-akustischen Präzedenz Effektes, der Menschen in die Lage versetzt, akustische Echos während der Lokalisation von Schallquellen zu unterdrücken. Sie enthält ein Modell zur Echo-Unterdrückung während der Schallquellenlokalisation, welches in technischen Systemen zur Mensch-Maschine Interaktion eingesetzt werden kann. Die Grundlagen dieses Modells wurden aus eigenen elektrophysiologischen Experimenten an der Mongolischen Wüstenrennmaus gewonnen. Die dabei erstmalig an der Wüstenrennmaus erzielten Ergebnisse, zeigen ein besonderes Verhalten spezifischer Zellen im Dorsalen Kern des Lateral Lemniscus, einer dedizierten Region des auditorischen Hirnstammes. Die dort sichtbare Langzeithemmung scheint die Grundlage für die Echounterdrückung in höheren auditorischen Zentren zu sein. Das entwickelte Model war in der Lage dieses Verhalten nachzubilden, und legt die Vermutung nahe, dass eine starke und zeitlich präzise Hyperpolarisation der zugrundeliegende physiologische Mechanismus dieses Verhaltens ist. Die entwickelte Neuronale Modellarchitektur modelliert das Innenohr und fünf wesentliche Kerne des auditorischen Hirnstammes in ihrer Verbindungsstruktur und internen Dynamik. Sie stellt einen neuen Typus neuronaler Modellierung dar, der als Spike-Interaktionsmodell (SIM) bezeichnet wird. SIM nutzen die präzise räumlich-zeitliche Interaktion einzelner Aktionspotentiale (Spikes) für die Kodierung und Verarbeitung neuronaler Informationen. Die Basis dafür bilden Integrate-and-Fire Neuronenmodelle sowie Hebb'sche Synapsen, welche um speziell entwickelte dynamische Kernfunktionen erweitert wurden. Das Modell ist in der Lage, Zeitdifferenzen von 10 mykrosekunden zu detektieren und basiert auf den Prinzipien der zeitlichen und räumlichen Koinzidenz sowie der präzisen lokalen Inhibition. Es besteht ausschließlich aus Elementen einer eigens entwickelten Neuronalen Basisbibliothek (NBL) die speziell für die Modellierung verschiedenster Spike- Interaktionsmodelle entworfen wurde. Diese Bibliothek erweitert die kommerziell verfügbare dynamische Simulationsumgebung von MATLAB/SIMULINK um verschiedene Modelle von Neuronen und Synapsen, welche die intrinsischen dynamischen Eigenschaften von Nervenzellen nachbilden. Die Nutzung dieser Bibliothek versetzt sowohl den Ingenieur als auch den Biologen in die Lage, eigene, biologisch plausible, Modelle der neuronalen Informationsverarbeitung ohne detaillierte Programmierkenntnisse zu entwickeln. Die grafische Oberfläche ermöglicht strukturelle sowie parametrische Modifikationen und ist in der Lage, den Zeitverlauf mikroskopischer Zellpotentiale aber auch makroskopischer Spikemuster während und nach der Simulation darzustellen. Zwei grundlegende Elemente der Neuronalen Basisbibliothek wurden zur Implementierung als spezielle analog-digitale Schaltungen vorbereitet. Erste Silizium Implementierungen durch das Team des DFG Graduiertenkollegs GRK 164 konnten die Möglichkeit einer vollparallelen on line Verarbeitung von Schallsignalen nachweisen. Durch Zuhilfenahme des im GRK entwickelten automatisierten Layout Generators wird es möglich, spezielle Prozessoren zur Anwendung biologischer Verarbeitungsprinzipien in technischen Systemen zu entwickeln. Diese Prozessoren unterscheiden sich grundlegend von den klassischen von Neumann Prozessoren indem sie räumlich und zeitlich verteilte Spikemuster, anstatt sequentieller binärer Werte zur Informationsrepräsentation nutzen. Sie erweitern das digitale Kodierungsprinzip durch die Dimensionen des Raumes (2 dimensionale Nachbarschaft) der Zeit (Frequenz, Phase und Amplitude) sowie der zeitlichen Dynamik analoger Potentialverläufe. Diese Dissertation besteht aus sieben Kapiteln, welche den verschiedenen Bereichen der Computational Neuroscience gewidmet sind. Kapitel 1 beschreibt die Motivation dieser Arbeit welche aus der Absicht rühren, biologische Prinzipien der Schallverarbeitung zu erforschen und für technische Systeme während der Interaktion mit dem Menschen nutzbar zu machen. Zusätzlich werden fünf Gründe für die Nutzung von Spike-Interaktionsmodellen angeführt sowie deren neuartiger Charakter beschrieben. Kapitel 2 führt die biologischen Prinzipien der Schallquellenlokalisation und den psychoakustischen Präzedenz Effekt ein. Aktuelle Hypothesen zur Entstehung dieses Effektes werden anhand ausgewählter experimenteller Ergebnisse verschiedener Forschungsgruppen diskutiert. Kapitel 3 beschreibt die entwickelte Neuronale Basisbibliothek und führt die einzelnen neuronalen Simulationselemente ein. Es erklärt die zugrundeliegenden mathematischen Funktionen der dynamischen Komponenten und beschreibt deren generelle Einsetzbarkeit zur dynamischen Simulation spikebasierter Neuronaler Netzwerke. Kapitel 4 enthält ein speziell entworfenes Modell des auditorischen Hirnstammes beginnend mit den Filterkaskaden zur Simulation des Innenohres, sich fortsetzend über mehr als 200 Zellen und 400 Synapsen in 5 auditorischen Kernen bis zum Richtungssensor im Bereich des auditorischen Mittelhirns. Es stellt die verwendeten Strukturen und Parameter vor und enthält grundlegende Hinweise zur Nutzung der Simulationsumgebung. Kapitel 5 besteht aus drei Abschnitten, wobei der erste Abschnitt die Experimentalbedingungen und Ergebnisse der eigens durchgeführten Tierversuche beschreibt. Der zweite Abschnitt stellt die Ergebnisse von 104 Modellversuchen zur Simulationen psycho-akustischer Effekte dar, welche u.a. die Fähigkeit des Modells zur Nachbildung des Präzedenz Effektes testen. Schließlich beschreibt der letzte Abschnitt die Ergebnisse der 54 unter realen Umweltbedingungen durchgeführten Experimente. Dabei kamen Signale zur Anwendung, welche in normalen sowie besonders stark verhallten Räumen aufgezeichnet wurden. Kapitel 6 vergleicht diese Ergebnisse mit anderen biologisch motivierten und technischen Verfahren zur Echounterdrückung und Schallquellenlokalisation und führt den aktuellen Status der Hardwareimplementierung ein. Kapitel 7 enthält schließlich eine kurze Zusammenfassung und einen Ausblick auf weitere Forschungsobjekte und geplante Aktivitäten. Diese Arbeit möchte zur Entwicklung der Computational Neuroscience beitragen, indem sie versucht, in einem speziellen Anwendungsfeld die Lücke zwischen biologischen Erkenntnissen, rechentechnischen Modellen und Hardware Engineering zu schließen. Sie empfiehlt ein neues räumlich-zeitliches Paradigma der dynamischen Informationsverarbeitung zur Erschließung biologischer Prinzipien der Informationsverarbeitung für technische Anwendungen.This thesis investigates the biological background of the psycho-acoustical precedence effect, enabling humans to suppress echoes during the localization of sound sources. It provides a technically feasible and biologically plausible model for sound source localization under echoic conditions, ready to be used by technical systems during man-machine interactions. The model is based upon own electro-physiological experiments in the mongolian gerbil. The first time in gerbils obtained results reveal a special behavior of specific cells of the dorsal nucleus of the lateral lemniscus (DNLL) - a distinct region in the auditory brainstem. The explored persistent inhibition effect of these cells seems to account for the base of echo suppression at higher auditory centers. The developed model proved capable to duplicate this behavior and suggests, that a strong and timely precise hyperpolarization is the basic mechanism behind this cell behavior. The developed neural architecture models the inner ear as well as five major nuclei of the auditory brainstem in their connectivity and intrinsic dynamics. It represents a new type of neural modeling described as Spike Interaction Models (SIM). SIM use the precise spatio-temporal interaction of single spike events for coding and processing of neural information. Their basic elements are Integrate-and-Fire Neurons and Hebbian synapses, which have been extended by specially designed dynamic transfer functions. The model is capable to detect time differences as small as 10 mircrosecondes and employs the principles of coincidence detection and precise local inhibition for auditory processing. It consists exclusively of elements of a specifically designed Neural Base Library (NBL), which has been developed for multi purpose modeling of Spike Interaction Models. This library extends the commercially available dynamic simulation environment of MATLAB/SIMULINK by different models of neurons and synapses simulating the intrinsic dynamic properties of neural cells. The usage of this library enables engineers as well as biologists to design their own, biologically plausible models of neural information processing without the need for detailed programming skills. Its graphical interface provides access to structural as well as parametric changes and is capable to display the time course of microscopic cell parameters as well as macroscopic firing pattern during simulations and thereafter. Two basic elements of the Neural Base Library have been prepared for implementation by specialized mixed analog-digital circuitry. First silicon implementations were realized by the team of the DFG Graduiertenkolleg GRK 164 and proved the possibility of fully parallel on line processing of sounds. By using the automated layout processor under development in the Graduiertenkolleg, it will be possible to design specific processors in order to apply theprinciples of distributed biological information processing to technical systems. These processors differ from classical von Neumann processors by the use of spatio temporal spike pattern instead of sequential binary values. They will extend the digital coding principle by the dimensions of space (spatial neighborhood), time (frequency, phase and amplitude) as well as the dynamics of analog potentials and introduce a new type of information processing. This thesis consists of seven chapters, dedicated to the different areas of computational neuroscience. Chapter 1: provides the motivation of this study arising from the attempt to investigate the biological principles of sound processing and make them available to technical systems interacting with humans under real world conditions. Furthermore, five reasons to use spike interaction models are given and their novel characteristics are discussed. Chapter 2: introduces the biological principles of sound source localization and the precedence effect. Current hypothesis on echo suppression and the underlying principles of the precedence effect are discussed by reference to a small selection of physiological and psycho-acoustical experiments. Chapter 3: describes the developed neural base library and introduces each of the designed neural simulation elements. It also explains the developed mathematical functions of the dynamic compartments and describes their general usage for dynamic simulation of spiking neural networks. Chapter 4: introduces the developed specific model of the auditory brainstem, starting from the filtering cascade in the inner ear via more than 200 cells and 400 synapses in five auditory regions up to the directional sensor at the level of the auditory midbrain. It displays the employed parameter sets and contains basic hints for the set up and configuration of the simulation environment. Chapter 5: consists of three sections, whereas the first one describes the set up and results of the own electro-physiological experiments. The second describes the results of 104 model simulations, performed to test the models ability to duplicate psycho-acoustical effects like the precedence effect. Finally, the last section of this chapter contains the results of 54 real world experiments using natural sound signals, recorded under normal as well as highly reverberating conditions. Chapter 6: compares the achieved results to other biologically motivated and technical models for echo suppression and sound source localization and introduces the current status of silicon implementation. Chapter 7: finally provides a short summary and an outlook toward future research subjects and areas of investigation. This thesis aims to contribute to the field of computational neuroscience by bridging the gap between biological investigation, computational modeling and silicon engineering in a specific field of application. It suggests a new spatio-temporal paradigm of information processing in order to access the capabilities of biological systems for technical applications

Modeling the Bat Spatial Navigation System: A Neuromorphic VLSI Approach

Autonomously navigating robots have long been a tough challenge facing engineers. The recent push to develop micro-aerial vehicles for practical military, civilian, and industrial use has added a significant power and time constraint to the challenge. In contrast, animals, from insects to humans, have been navigating successfully for millennia using a wide range of variants of the ultra-low-power computational system known as the brain. For this reason, we look to biological systems to inspire a solution suitable for autonomously navigating micro-aerial vehicles. In this dissertation, the focus is on studying the neurobiological structures involved in mammalian spatial navigation. The mammalian brain areas widely believed to contribute directly to navigation tasks are the Head Direction Cells, Grid Cells and Place Cells found in the post-subiculum, the medial entorhinal cortex, and the hippocampus, respectively. In addition to studying the neurobiological structures involved in navigation, we investigate various neural models that seek to explain the operation of these structures and adapt them to neuromorphic VLSI circuits and systems. We choose the neuromorphic approach for our systems because we are interested in understanding the interaction between the real-time, physical implementation of the algorithms and the real-world problem (robot and environment). By utilizing both analog and asynchronous digital circuits to mimic similar computations in neural systems, we envision very low power VLSI implementations suitable for providing practical solutions for spatial navigation in micro-aerial vehicles

Neural Models of Subcortical Auditory Processing

An important feature of the auditory system is its ability to distinguish many simultaneous sound sources. The primary goal of this work was to understand how a robust, preattentive analysis of the auditory scene is accomplished by the subcortical auditory system. Reasonably accurate modelling of the morphology and organisation of the relevant auditory nuclei, was seen as being of great importance. The formulation of plausible models and their subsequent simulation was found to be invaluable in elucidating biological processes and in highlighting areas of uncertainty. In the thesis, a review of important aspects of mammalian auditory processing is presented and used as a basis for the subsequent modelling work. For each aspect of auditory processing modelled, psychophysical results are described and existing models reviewed, before the models used here are described and simulated. Auditory processes which are modelled include the peripheral system, and the production of tonotopic maps of the spectral content of complex acoustic stimuli, and of modulation frequency or periodicity. A model of the formation of sequential associations between successive sounds is described, and the model is shown to be capable of emulating a wide range of psychophysical behaviour. The grouping of related spectral components and the development of pitch perception is also investigated. Finally a critical assessment of the work and ideas for future developments are presented. The principal contributions of this work are the further development of a model for pitch perception and the development of a novel architecture for the sequential association of those groups. In the process of developing these ideas, further insights into subcortical auditory processing were gained, and explanations for a number of puzzling psychophysical characteristics suggested.Royal Naval Engineering College, Manadon, Plymout

Exploring the potential of brain-inspired computing

The gap between brains and computers regarding both their cognitive capability and power efficiency is remarkably huge. Brains process information massively in parallel and its constituents are intrinsically self-organizing, while in digital computers the execution of instructions is deterministic and rather serial. The recent progress in the development of dedicated hardware systems implementing physical models of neurons and synapses enables to efficiently emulate spiking neural networks. In this work, we verify the design and explore the potential for brain-inspired computing of such an analog neuromorphic system, called Spikey. We demonstrate the versatility of this highly configurable substrate by the implementation of a rich repertoire of network models, including models for signal propagation and enhancement, general purpose classifiers, cortical models and decorrelating feedback systems. Network emulations on Spikey are highly accelerated and consume less than 1 nJ per synaptic transmission. The Spikey system, hence, outperforms modern desktop computers in terms of fast and efficient network simulations closing the gap to brains. During this thesis the stability, performance and user-friendliness of the Spikey system was improved integrating it into the neuroscientific tool chain and making it available for the community. The implementation of networks suitable to solve everyday tasks, like object or speech recognition, qualifies this technology to be an alternative to conventional computers. Considering the compactness, computational capability and power efficiency, neuromorphic systems may qualify as a valuable complement to classical computation