Inter-spikes-intervals exponential and gamma distributions study of neuron firing rate for SVITE motor control model on FPGA

This paper presents a statistical study on a neuro-inspired spike-based implementation of the Vector-Integration-To-End-Point motor controller (SVITE) and compares its deterministic neuron-model stream of spikes with a proposed modification that converts the model, and thus the controller, in a Poisson like spike stream distribution. A set of hardware pseudo-random numbers generators, based on a Linear Feedback Shift Register (LFSR), have been introduced in the neuron-model so that they reach a closer biological neuron behavior. To validate the new neuron-model behavior a comparison between the Inter-Spikes-Interval empirical data and the Exponential and Gamma distributions has been carried out using the Kolmogorov–Smirnoff test. An in-hardware validation of the controller has been performed in a Spartan6 FPGA to drive directly with spikes DC motors from robotics to study the behavior and viability of the modified controller with random components. The results show that the original deterministic spikes distribution of the controller blocks can be swapped with Poisson distributions using 30-bit LFSRs. The comparative between the usable controlling signals such as the trajectory and the speed profile using a deterministic and the new controller show a standard deviation of 11.53 spikes/s and 3.86 spikes/s respectively. These rates do not affect our system because, within Pulse Frequency Modulation, in order to drive the motors, time length can be fixed to spread the spikes. Tuning this value, the slow rates could be filtered by the motor. Therefore, this SVITE neuro-inspired controller can be integrated within complex neuromorphic architectures with Poisson-like neurons

NeuroPod: a real-time neuromorphic spiking CPG applied to robotics

Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being designed to take a step further, by performing more complex tasks that have been carried out by smart animals or humans up to date. To this end, inspiration needs to be taken from biological examples. For instance, insects are able to optimally solve complex environment navigation problems, and many researchers have started to mimic how these insects behave. Recent interest in neuromorphic engineering has motivated us to present a real-time, neuromorphic, spike-based Central Pattern Generator of application in neurorobotics, using an arthropod-like robot. A Spiking Neural Network was designed and implemented on SpiNNaker. The network models a complex, online-change capable Central Pattern Generator which generates three gaits for a hexapod robot locomotion. Recon gurable hardware was used to manage both the motors of the robot and the real-time communication interface with the Spiking Neural Networks. Real-time measurements con rm the simulation results, and locomotion tests show that NeuroPod can perform the gaits without any balance loss or added delay.Ministerio de Economía y Competitividad TEC2016-77785-

Performance Study of Software AER-Based Convolutions on a Parallel Supercomputer

This paper is based on the simulation of a convolution model for bioinspired neuromorphic systems using the Address-Event-Representation (AER) philosophy and implemented in the supercomputer CRS of the University of Cadiz (UCA). In this work we improve the runtime of the simulation, by dividing an image into smaller parts before AER convolution and running each operation in a node of the cluster. This research involves a test cases design in which the optimal parameters are set to run the AER convolution in parallel processors. These cases consist on running the convolution taking an image divided in different number of parts, applying to each part a Sobel filter for edge detection, and based on the AER-TOOL simulator. Execution times are compared for all cases and the optimal configuration of the system is discussed. In general, CRS obtain better performances when the image is divided than for the whole image.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

Beneficial effects of dietary supplementation with green tea catechins and cocoa flavanols on aging-related regressive changes in the mouse neuromuscular system

This work was supported by Abbott and a grant from the Spanish Ministerio de Ciencia, Innovacion y Universidades cofinanced by Fondo Europeo de Desarrollo Regional (RTI2018-099278-B-I00 to JC and JE) .Besides skeletal muscle wasting, sarcopenia entails morphological and molecular changes in distinct components of the neuromuscular system, including spinal cord motoneurons (MNs) and neuromuscular junctions (NMJs); moreover, noticeable microgliosis has also been observed around aged MNs. Here we examined the impact of two flavonoid-enriched diets containing either green tea extract (GTE) catechins or cocoa flavanols on age-associated regressive changes in the neuromuscular system of C57BL/6J mice. Compared to control mice, GTE- and cocoa-supplementation significantly improved the survival rate of mice, reduced the proportion of fibers with lipofuscin aggregates and central nuclei, and increased the density of satellite cells in skeletal muscles. Additionally, both supplements significantly augmented the number of innervated NMJs and their degree of maturity compared to controls. GTE, but not cocoa, prominently increased the density of VAChT and VGluT2 afferent synapses on MNs, which were lost in control aged spinal cords; conversely, cocoa, but not GTE, significantly augmented the proportion of VGluT1 afferent synapses on aged MNs. Moreover, GTE, but not cocoa, reduced aging-associated microgliosis and increased the proportion of neuroprotective microglial phenotypes. Our data indicate that certain plant flavonoids may be beneficial in the nutritional management of age-related deterioration of the neuromuscular system.Abbott LaboratoriesSpanish Ministerio de Ciencia, Innovacion y UniversidadesEuropean Commission RTI2018-099278-B-I0

Event-based control system on FPGA applied to the pencil balancer robotic platform

An event-based motor controller design is presented. The system is designed to solve the classic inverted pendulum problem by using a robotic platform and a totally neuro-inspired event-based mechanism. Specifically, DVS retinas provide feedback and an FPGA implements control. The robotic platform used is the so called ’pencil balancer’. The retinas provide visual information to the FPGA that processes it and obtains the center of mass of the pencil. Once this center of mass is averaged over time, it is used joint with the cart position provided by a flat potentiometer bar to compute the angle of the pencil from the vertical. The angle is delivered to an eventbased Proportional-Derivative (PD) controller that drives the DC motor using Pulse Frequency Modulation (PFM) to accomplish the control objective. The results show an accurate, real-time and efficient controller design

Visual Spike-based Convolution Processing with a Cellular Automata Architecture

this paper presents a first approach for implementations which fuse the Address-Event-Representation (AER) processing with the Cellular Automata using FPGA and AER-tools. This new strategy applies spike-based convolution filters inspired by Cellular Automata for AER vision processing. Spike-based systems are neuro-inspired circuits implementations traditionally used for sensory systems or sensor signal processing. AER is a neuromorphic communication protocol for transferring asynchronous events between VLSI spike-based chips. These neuro-inspired implementations allow developing complex, multilayer, multichip neuromorphic systems and have been used to design sensor chips, such as retinas and cochlea, processing chips, e.g. filters, and learning chips. Furthermore, Cellular Automata is a bio-inspired processing model for problem solving. This approach divides the processing synchronous cells which change their states at the same time in order to get the solution.Ministerio de Educación y Ciencia TEC2006-11730-C03-02Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Junta de Andalucía P06-TIC-0141

SVITE: A Spike-Based VITE Neuro-Inspired Robot Controller

This paper presents an implementation of a neuro-inspired algorithm called VITE (Vector Integration To End Point) in FPGA in the spikes domain. VITE aims to generate a non-planned trajectory for reaching tasks in robots. The algorithm has been adapted to work completely in the spike domain under Simulink simulations. The FPGA implementation consists in 4 VITE in parallel for controlling a 4-degree-of-freedom stereo-vision robot. This work represents the main layer of a complex spike-based architecture for robot neuro-inspired reaching tasks in FPGAs. It has been implemented in two Xilinx FPGA families: Virtex-5 and Spartan-6. Resources consumption comparative between both devices is presented. Results obtained for Spartan device could allow controlling complex robotic structures with up to 96 degrees of freedom per FPGA, providing, in parallel, high speed connectivity with other neuromorphic systems sending movement references. An exponential and gamma distribution test over the inter spike interval has been performed to proof the approach to the neural code proposed.Ministerio de Economía y Competitividad TEC2012-37868-C04-0

Live Demonstration: neuromorphic robotics, from audio to locomotion through spiking CPG on SpiNNaker.

This live demonstration presents an audio-guided neuromorphic robot: from a Neuromorphic Auditory Sensor (NAS) to locomotion using Spiking Central Pattern Generators (sCPGs). Several gaits are generated by sCPGs implemented on a SpiNNaker board. The output of these sCPGs is sent in a real-time manner to an Field Programmable Gate Array (FPGA) board using an AER-to-SpiNN interface. The control of the hexapod robot joints is performed by the FPGA board. The robot behavior can be changed in real-time by means of the NAS. The audio information is sent to the SpiNNaker board which classifies it using a Spiking Neural Network (SNN). Thus, the input sound will activate a specific gait pattern which will eventually modify the behavior of the robot.Ministerio de Economía y Competitividad TEC2016-77785-

ED-BioRob: A Neuromorphic Robotic Arm With FPGA-Based Infrastructure for Bio-Inspired Spiking Motor Controllers

Compared to classic robotics, biological nervous systems respond to stimuli in a fast and efficient way regarding the body motor actions. Decision making, once the sensory information arrives to the brain, is in the order of ms, while the whole process from sensing to movement requires tens of ms. Classic robotic systems usually require complex computational abilities. Key differences between biological systems and robotic machines lie in the way information is coded and transmitted. A neuron is the "basic" element that constitutes biological nervous systems. Neurons communicate in an event-driven way through small currents or ionic pulses (spikes). When neurons are arranged in networks, they allow not only for the processing of sensory information, but also for the actuation over the muscles in the same spiking manner. This paper presents the application of a classic motor control model (proportional-integral-derivative) developed with the biological spike processing principle, including the motor actuation with time enlarged spikes instead of the classic pulse-width-modulation. This closed-loop control model, called spike-based PID controller (sPID), was improved and adapted for a dual FPGA-based system to control the four joints of a bioinspired light robot (BioRob X5), called event-driven BioRob (ED-BioRob). The use of spiking signals allowed the system to achieve a current consumption bellow 1A for the entire 4 DoF working at the same time. Furthermore, the robot joints commands can be received from a population of silicon-neurons running on the Dynap-SE platform. Thus, our proposal aims to bridge the gap between a general purpose processing analog neuromorphic hardware and the spiking actuation of a robotic platform

AER-based robotic closed-loop control system

Address-Event-Representation (AER) is an asynchronous protocol for transferring the information of spiking neuro-inspired systems. Actually AER systems are able to see, to ear, to process information, and to learn. Regarding to the actuation step, the AER has been used for implementing Central Pattern Generator algorithms, but not for controlling the actuators in a closed-loop spike-based way. In this paper we analyze an AER based model for a real-time neuro-inspired closed-loop control system. We demonstrate it into a differential control system for a two-wheel vehicle using feedback AER information. PFM modulation has been used to power the DC motors of the vehicle and translation into AER of encoder information is also presented for the close-loop. A codesign platform (called AER-Robot), based into a Xilinx Spartan 3 FPGA and an 8051 USB microcontroller, with power stages for four DC motors has been used for the demonstrator.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0