Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energy consumption. However, this usually comes at the cost of reduced control over the dynamics of the emulated networks. In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate. We first convert a deep neural network trained in software to a spiking network on the BrainScaleS wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10 000 compared to the biological time domain. This mapping is followed by the in-the-loop training, where in each training step, the network activity is first recorded in hardware and then used to compute the parameter updates in software via backpropagation. An essential finding is that the parameter updates do not have to be precise, but only need to approximately follow the correct gradient, which simplifies the computation of updates. Using this approach, after only several tens of iterations, the spiking network shows an accuracy close to the ideal software-emulated prototype. The presented techniques show that deep spiking networks emulated on analog neuromorphic devices can attain good computational performance despite the inherent variations of the analog substrate.Comment: 8 pages, 10 figures, submitted to IJCNN 201

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Accelerated physical emulation of Bayesian inference in spiking neural networks

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.Comment: This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi: 10.3389/fnins.2019.0120

Event-driven visual attention for the humanoid robot iCub.

Fast reaction to sudden and potentially interesting stimuli is a crucial feature for safe and reliable interaction with the environment. Here we present a biologically inspired attention system developed for the humanoid robot iCub. It is based on input from unconventional event-driven vision sensors and an efficient computational method. The resulting system shows low-latency and fast determination of the location of the focus of attention. The performance is benchmarked against an instance of the state of the art in robotics artificial attention system used in robotics. Results show that the proposed system is two orders of magnitude faster that the benchmark in selecting a new stimulus to attend

Longitudinal Studies Of Caenorhabditis Elegans Aging And Behavior Using A Microfabricated Multi-Well Device

The roundworm C. elegans is a powerful model organism for dissecting the genetics of behavior and aging. The central genetic pathways regulating lifespan, such as insulin signaling, were first identified in worms. C. elegans is also the only animal for which a full map of all neural synpatic connections, or connectome, exists. However, current manual and automated methods are unable to efficiently monitor and quantify behavioral phenotypes which unfold over long time scales. Therefore, it has been difficult to study phenotypes such as long-term behavior states and behavioral changes with age in worms. To address these limitations, here I describe a novel device, called the WorMotel, to longitudinally monitor behavior in up to 240 single C. elegans on time scales encompassing the worm\u27s maximum lifespan of two months. The WorMotel is fabricated from polydimethylsiloxane from a 3-D printed negative mold. Each device consists of 240 individual wells, each of which houses a single worm atop agar and bacterial food. I use custom software to quantify movement between frames to longitudinally monitor behavior for each animal. I first describe the application of the WorMotel to the automation of lifespan measurements in C. elegans, the characterization of intra-strain and inter-strain variability in behavioral decline, the relationship between behavior and lifespan, and the scaling of behavioral decline with increasing stress. I then describe the application of the WorMotel to quantify locomotive behavioral states and their modulation by the presence or absence of food as well as biogenic amine neurotransmitters. Using the WorMotel in combination with genetics and pharmacology, I outline a neural circuit by which the biogenic amines serotonin and octopamine regulate locomotion state to signal animals to adopt behavior appropriate to a fed and fasting state, respectively. I include protocols for construction of custom imaging rigs and requirements for long-term imaging as an appendix. The WorMotel is a powerful tool that can facilitate discovery and understanding of the mechanisms underlying long-term phenotypes such as behavioral states and aging

A Self-Calibrating, Camera-Based Eye Tracker for the Recording of Rodent Eye Movements

Much of neurophysiology and vision science relies on careful measurement of a human or animal subject's gaze direction. Video-based eye trackers have emerged as an especially popular option for gaze tracking, because they are easy to use and are completely non-invasive. However, video eye trackers typically require a calibration procedure in which the subject must look at a series of points at known gaze angles. While it is possible to rely on innate orienting behaviors for calibration in some non-human species, other species, such as rodents, do not reliably saccade to visual targets, making this form of calibration impossible. To overcome this problem, we developed a fully automated infrared video eye-tracking system that is able to quickly and accurately calibrate itself without requiring co-operation from the subject. This technique relies on the optical geometry of the cornea and uses computer-controlled motorized stages to rapidly estimate the geometry of the eye relative to the camera. The accuracy and precision of our system was carefully measured using an artificial eye, and its capability to monitor the gaze of rodents was verified by tracking spontaneous saccades and evoked oculomotor reflexes in head-fixed rats (in both cases, we obtained measurements that are consistent with those found in the literature). Overall, given its fully automated nature and its intrinsic robustness against operator errors, we believe that our eye-tracking system enhances the utility of existing approaches to gaze-tracking in rodents and represents a valid tool for rodent vision studies

Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo

We present the development and in vivo application of a feedback-based tracking microscope to follow individual mitochondria in sensory neurons of zebrafish larvae with nanometer precision and millisecond temporal resolution. By combining various technical improvements, we tracked individual mitochondria with unprecedented spatiotemporal resolution over distances of >100 mu m. Using these nanoscopic trajectory data, we discriminated five motional states: a fast and a slow directional motion state in both the anterograde and retrograde directions and a stationary state. The transition pattern revealed that, after a pause, mitochondria predominantly persist in the original direction of travel, while transient changes of direction often exhibited longer pauses. Moreover, mitochondria in the vicinity of a second, stationary mitochondria displayed an increased probability to pause. The capability of following and optically manipulating a single organelle with high spatiotemporal resolution in a living organism offers a new approach to elucidating their function in its complete physiological context

Random-access scanning microscopy for 3D imaging in awake behaving animals

Understanding how neural circuits process information requires rapid measurements of activity from identified neurons distributed in 3D space. Here we describe an acousto-optic lens two-photon microscope that performs high-speed focusing and line scanning within a volume spanning hundreds of micrometers. We demonstrate its random-access functionality by selectively imaging cerebellar interneurons sparsely distributed in 3D space and by simultaneously recording from the soma, proximal and distal dendrites of neocortical pyramidal cells in awake behaving mice

Rapid diffusion in the brain extracellular space - biophysical constraints and physiological implications

Physiological experiments backed by biophysical models have shown that, in central glutamatergic synapses, changes in extracellular diffusivity or glutamate transporter functions exert significant influences on the excitatory transmission. Failures of transporter functions have also been related to neurological disorders. The underlying biophysical mechanisms remain poorly understood. Here, we first combine two‐photon excitation imaging with electrophysiology to estimate the diffusivity of small soluble molecules, such as glutamate in the hippocampal neuropil (area CA1). Next, we adopt time‐resolved fluorescence anisotropy imaging microscopy to establish the previously unknown instantaneous diffusivity of small molecules in the extracellular space. The result indicates that nanometer‐scale diffusivity in the brain extracellular space is 25‐30% slower than that in free medium. Accounting for this retardation may have fundamental consequences for accurate interpretation of diffusion‐limited reactions in the brain. To obtain insight into the mechanisms contributing to the excitatory signal formation, we incorporate these results in a newly developed Monte‐Carlo model of the typical environment of small excitatory synapses including unevenly distributed receptors and transporters. In addition, we build a macroscopic three‐dimensional compartmental model of the hippocampal neuropil based on available experimental data to examine the effect of transporter distribution on the extracellular landscape of glutamate. Monte‐Carlo simulations show to what extent altering diffusivity inside or outside the synaptic cleft affect synaptic responses. Modelling also predicts that extrasynaptic transporters have little effect on fast synaptic transmission through AMPARs and NMDARs. However, they influence the responses of high‐affinity extrasynaptic receptors, such as NMDA or metabotropic receptors. Conversely, intra‐cleft glutamate transporters should significantly attenuate activation of synaptic transmission. On a larger neuropil scale, failure of >95% transporters is required for any significant elevation of glutamate (above 1‐2 μM) to occur. Our data shed light on fundamental biophysical constraints important for a better understanding of excitatory signal formation in central neural circuits