Search

In this paper, we study how a biologically-plausible cerebellum architecture can store and retrieve different robotic-arm internal models (in synaptic connections between granular layer and Purkinje cells) at the granule layer (dynamic modifications of a base robot-arm-plant model), and how the model microstructure and input signal representations can efficiently infer models in a robot control scenario during object manipulation. More specifically, we have evaluated the contribution of the granular layer to the ability of the cerebellum to generate corrective actions. To achieve this we have embedded a spiking cerebellar model into an analog control loop whose output commands a simulated robot arm. The performance results obtained by using a cerebellum which includes granular layer are compared to those using a cerebellum without this layer. The results show that this layer effectively contributes to the generation of accurate cerebellar corrections. This work represents a well defined case of study in the field of neurobotics, in which biologically plausible neural systems and robots are used to study the functionality of biological systems.

The driver's loss of attention is an important problem in which are spent considerable research efforts in different areas such as psychology, automobile technology, computer vision and driving assistance. We use here a simple algorithm based on rigid-body and motion detection. This scheme efficiently segments moving objects using the visual field of the driver's rear-view mirror. The overtaking scene in the rear-view mirror is distorted due to perspective, making it difficult to detect the overtaking car. Thus we propose two alternative methods to deal with this problem and compare the results in different overtaking sequences.

The efficient simulation of spiking neural networks (SNN) remains as an open challenge. Current SNN computing engines are still far away of being able to efficiently simulate systems of millions of neurons. This contribution describes a computing scheme that takes full advantage of the massive parallel processing resources available at FPGA devices. The computing engine adopts an event-driven simulation scheme and an efficient next-event-to-go searching method to achieve high performance. We have designed a pipelined datapath in order to compute several events in parallel avoiding idle computing resources. The system is able to compute approximately 2.5 million spikes per second. The whole computing machine is composed only by an FPGA device and five external memory SRAM chips. Therefore the presented approach is of high interest for simulation experiments that require embedded simulation engines (for instance in robotic experiments with autonomous agents).

A spiking neural network modeling the cerebellum is presented. The model, consisting of more than 2000 conductance-based neurons and more than 50 000 synapses, runs in real-time on a dual-processor computer. The model is implemented on an event-driven spiking neural network simulator with table-based conductance and voltage computations. The cerebellar model interacts every millisecond with a time-driven simulation of a simple environment in which adaptation experiments are setup. Learning is achieved in real-time using spike time dependent plasticity rules, which drive synaptic weight changes depending on the neurons activity and the timing in the spiking representation of an error signal. The cerebellar model is tested on learning to continuously predict a target position moving along periodical trajectories. This setup reproduces experiments with primates learning the smooth pursuit of visual targets on a screen. The model learns effectively and concurrently different target trajectories. This is true even though the spiking rate of the error representation is very low, reproducing physiological conditions. Hence, we present a complete physiologically relevant spiking cerebellar model that runs and learns in real-time in realistic conditions reproducing psychophysical experiments. This work was funded in part by the EC SpikeFORCE project (IST-2001-35271, www.spikeforce.org).

Overtaking is one of the most dangerous operations in driving. The rear-view mirror is sometimes not consulted by the driver or is momentarily useless because of the blind spot. This paper describes a simple FPGA system based on motion detectors of the fly and rigid-body detection that is able to efficiently segment overtaking cars using a sparse map of features from the visual field of the rear-view mirror. FPGA implementation allows real-time image processing on an embedded system.

In this work we present several hardware implementations of a standard multi-layer perceptron and a modified version called extended multilayer perceptron. The implementations have been developed and tested onto a FPGA prototyping board. The designs have been defined using a high level hardware description language, which enables the study of different implementation versions with diverse parallelism levels. The test bed application addressed is speech recognition. The contribution presented in this paper can be seen as a low cost portable system, which can be easily modified. We include a short study of the implementation costs (silicon area), speed and required computational resources.

This work presents a flexible reconfigurable approach to a bioinspired spiking neuron. The main objective of this contribution is to evaluate the silicon cost of the implementation of lime-dependent conductances in spiking neurons. The design presented here has been defined using a high level Hardware Description Language (HDL). This facilitates the extraction of simulation results, and the easy change of the circuit. The paper discusses how different aspects of lime-dependent conductances can be particularized in the circuit, and Iheir hardware requirements.