201 research outputs found

    An AER Spike-Processing Filter Simulator and Automatic VHDL Generator Based on Cellular Automata

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    Spike-based systems are neuro-inspired circuits implementations traditionally used for sensory systems or sensor signal processing. Address-Event- Representation (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 (CA) 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. This paper presents a software simulator able to gather several spike-based elements into the same workspace in order to test a CA architecture based on AER before a hardware implementation. Furthermore this simulator produces VHDL for testing the AER-CA into the FPGA of the USBAER AER-tool.Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

    Neuromorphic analogue VLSI

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    Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do

    From Vision Sensor to Actuators, Spike Based Robot Control through Address-Event-Representation

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    One field of the neuroscience is the neuroinformatic whose aim is to develop auto-reconfigurable systems that mimic the human body and brain. In this paper we present a neuro-inspired spike based mobile robot. From commercial cheap vision sensors converted into spike information, through spike filtering for object recognition, to spike based motor control models. A two wheel mobile robot powered by DC motors can be autonomously controlled to follow a line drown in the floor. This spike system has been developed around the well-known Address-Event-Representation mechanism to communicate the different neuro-inspired layers of the system. RTC lab has developed all the components presented in this work, from the vision sensor, to the robot platform and the FPGA based platforms for AER processing.Ministerio de Ciencia e Innovación TEC2006-11730-C03-02Junta de Andalucía P06-TIC-0141

    Neuro-inspired system for real-time vision sensor tilt correction

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    Neuromorphic engineering tries to mimic biological information processing. Address-Event-Representation (AER) is an asynchronous protocol for transferring the information of spiking neuro-inspired systems. Currently AER systems are able sense visual and auditory stimulus, to process information, to learn, to control robots, etc. In this paper we present an AER based layer able to correct in real time the tilt of an AER vision sensor, using a high speed algorithmic mapping layer. A codesign platform (the AER-Robot platform), with a Xilinx Spartan 3 FPGA and an 8051 USB microcontroller, has been used to implement the system. Testing it with the help of the USBAERmini2 board and the jAER software.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-02Ministerio de Ciencia e Innovación TEC2009-10639-C04-0

    AER and dynamic systems co-simulation over Simulink with Xilinx System Generator

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    Address-Event Representation (AER) is a neuromorphic communication protocol for transferring information of spiking neurons implemented into VLSI chips. These neuro-inspired implementations have been used to design sensor chips (retina, cochleas), processing chips (convolutions, filters) and learning chips, what makes possible the development of complex, multilayer, multichip neuromorphic systems. In biology one of the last steps of the processing is to move a muscle, to apply the results of these complex neuromorphic processing to the real world. One interesting question is to be able to transform, or translate, the AER information into robot movements, like for example, moving a DC motor. This paper presents several ways to translate AER spikes into DC motor power, and to control a DC motor speed, based on Pulse Frequency Modulation. These methods have been simulated into Simulink with Xilinx System Generator, and tested into the AER-Robot platform.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0

    AER-based robotic closed-loop control system

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    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

    Embedding Multi-Task Address-Event- Representation Computation

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    Address-Event-Representation, AER, is a communication protocol that is intended to transfer neuronal spikes between bioinspired chips. There are several AER tools to help to develop and test AER based systems, which may consist of a hierarchical structure with several chips that transmit spikes among them in real-time, while performing some processing. Although these tools reach very high bandwidth at the AER communication level, they require the use of a personal computer to allow the higher level processing of the event information. We propose the use of an embedded platform based on a multi-task operating system to allow both, the AER communication and processing without the requirement of either a laptop or a computer. In this paper, we present and study the performance of an embedded multi-task AER tool, connecting and programming it for processing Address-Event information from a spiking generator.Ministerio de Ciencia e Innovación TEC2006-11730-C03-0

    Neuromorphic Implementation of Orientation Hypercolumns

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    Neurons in the mammalian primary visual cortex are selective along multiple stimulus dimensions, including retinal position, spatial frequency, and orientation. Neurons tuned to different stimulus features but the same retinal position are grouped into retinotopic arrays of hypercolumns. This paper describes a neuromorphic implementation of orientation hypercolumns, which consists of a single silicon retina feeding multiple chips, each of which contains an array of neurons tuned to the same orientation and spatial frequency, but different retinal locations. All chips operate in continuous time, and communicate with each other using spikes transmitted by the address-event representation protocol. This system is modular in the sense that orientation coverage can be increased simply by adding more chips, and expandable in the sense that its output can be used to construct neurons tuned to other stimulus dimensions. We present measured results from the system, demonstrating neuronal selectivity along position, spatial frequency and orientation. We also demonstrate that the system supports recurrent feedback between neurons within one hypercolumn, even though they reside on different chips. The measured results from the system are in excellent concordance with theoretical predictions

    Visual Spike-based Convolution Processing with a Cellular Automata Architecture

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    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
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