Neuromorphic vision represents a significant advancement in biomimetic systems, particularly with the development of event cameras. Unlike traditional frame-based cameras, event cameras operate asynchronously, detecting changes in brightness at each pixel and providing high temporal resolution and low latency suitable for robotics and autonomous systems in dynamic environments. However, event cameras face a critical challenge that limits their effectiveness in some applications: static scene blindness, where no events are generated in static environments, and which biology solves using a combination of eye tremors and neural persistence. This paper explores a solution to static scene blindness by developing a persistent representation of event camera outputs through a specialized architecture of Spiking Neural Networks (SNNs), referred to as Event-Driven Spiking Cortical Models (ESCMs). By incorporating recurrent neural connections that maintain and update spatial information over time, the proposed architecture retains visual information even after motion has stopped, allowing for continuous and meaningful scene representation. The model balances spatial detail and temporal precision, reduces noise accumulation with respect to state-of-the-art methods, and handles sparse event data, confirming its ability to overcome static scene blindness. These findings contribute to the advancement of neuromorphic vision solutions for real-time visual processing, particularly in applications requiring consistent scene understanding despite varying motion dynamics.

Physical reservoir computing systems are promising for low power classification problems. Here we show that Percolating Networks of Nanoparticles can be successfully used experimentally as reservoirs for both speaker recognition and Braille classification tasks. We demonstrate low word error rates in both tasks and discuss the effects of various parameters such as the numbers of electrodes and required timescales for the input data (which are on the order of microseconds).

Tracking the position and orientation of objects in space (i.e., in 6-DoF) in real time is a fundamental problem in robotics for environment interaction. It becomes more challenging when objects move at high-speed due to frame rate limitations in conventional cameras and motion blur. Event cameras are characterized by high temporal resolution, low latency and high dynamic range, that can potentially overcome the impacts of motion blur. Traditional RGB cameras provide rich visual information that is more suitable for the challenging task of single-shot object pose estimation. In this work, we propose using event-based optical flow combined with an RGB based global object pose estimator for 6-DoF pose tracking of objects at high-speed, exploiting the core advantages of both types of vision sensors. Specifically, we propose an event-based optical flow algorithm for object motion measurement to implement an object 6-DoF velocity tracker. By integrating the tracked object 6-DoF velocity with low frequency estimated pose from the global pose estimator, the method can track pose when objects move at high-speed. The proposed algorithm is tested and validated on both synthetic and real world data, demonstrating its effectiveness, especially in high-speed motion scenarios.

Event cameras offer low-latency and data compression for visual applications, through event-driven operation, that can be exploited for edge processing in tiny autonomous agents. Robust, accurate and low latency extraction of highly informative features such as corners is key for most visual processing. While several corner detection algorithms have been proposed, state-of-the-art performance is achieved by "luvHarris". However, this algorithm requires a high number of memory accesses per event, making it less-than ideal for low-latency, low-energy implementation in tiny edge processors. In this paper, we propose a new event-driven corner detection implementation tailored for edge computing devices, which requires much lower memory access than lu-vHarris while also improving accuracy. Our method trades computation for memory access, which is more expensive for large memories. For a DAVIS346 camera, our method requires ≈ 3.8X less memory, ≈ 36.6X less memory accesses with only ≈ 2.3X more computes.

High-frequency, low-latency, 6-DoF object tracking is useful for grasping objects in motion, taking robots beyond pick-and-place tasks. We propose using an event-camera for tracking the objects to leverage the low-latency and continuous (i.e. not fixed-rate) data capture for high-frequency tracking. We propose the EDOPT algorithm, which maintains real-time operation with a variable event-rate (which occurs due to variation in camera velocity and scene texture) and avoids frame-jumps and motion-blur which are problematic in traditional computer vision solutions. EDOPT uses a strong object prior, leading to a novel solution possible only with the event-camera. To our knowledge, this is the first method for 6-DoF object pose estimation with only the event-camera. The proposed method achieves comparable results to a state-of-the-art DNN technique that fuses frames, depth, and events. We demonstrate smooth, online object pose tracking with a live camera feed at > 300 Hz.

Robust object pose tracking plays an important role in robot manipulation, but it is still an open issue for quickly moving targets as motion blur and low frequency detection can reduce pose estimation accuracy even for state-of-the-art RGB-D-based methods. An event-camera is a low-latency vision sensor that can act complementary to RGB-D. Specifically, its sub-millisecond temporal resolution can be exploited to correct for pose estimation inaccuracies due to low frequency RGB-D based detection. To do so, we propose a dual Kalman filter: the first filter estimates an object's velocity from the spatiotemporal patterns of “events”, the second filter fuses the tracked object velocity with a low-frequency object pose estimated from a deep neural network using RGB-D data. The full system outputs high frequency, accurate object poses also for fast moving objects. The proposed method works towards low-power robotics by replacing high-cost GPU-based optical flow used in prior work with event-cameras that inherently extract the required signal without costly processing. The proposed algorithm achieves comparable or better performance when compared to two state-of-the-art 6-DoF object pose estimation algorithms and one hybrid event/RGB-D algorithm on benchmarks with simulated and real data. We discuss the benefits and tradeoffs for using the event-camera and contribute algorithm, code, and datasets to the community. The code and datasets are available at https://github.com/event-driven-robotics/Hybrid-object-tracking-with-events-and-frames.

Tactile object shapes are considered as important properties in robotic manipulation. Many researches have focused recently on using tactile sensing systems to enable tactile information processing in robotics. Spiking Neural Networks (SNNs) are emerging as promising methods alternative to deep learning due to their ability to process information in an event-driven manner. In this paper, we propose a SNN architecture and hardware implementation for tactile object shapes recognition. The network is fed by an array of 160 piezoresistive tactile sensors where the object shapes are applied. Results demonstrate that the proposed system is able to discriminate the tactile object shapes with 100% accuracy on unseen data having time steps up to 0.1 ms. Moreover, the network has been implemented on a Raspberry Pi platform achieving real time classification.

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of μs), very high dynamic range (140 dB versus 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.

There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use, for example when a camera is randomly moved in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel 'threshold ordinal event-surface' that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load per event is minimised and computational heavy convolutions are performed only 'as-fast-as-possible', i.e., only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than 2.6× the speed of current state-of-the-art; a necessity when using a high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras.

Robots and users of hand prosthesis could easily manipulate objects if endowed with the sense of touch. Towards this goal, information about touched objects and surfaces has to be inferred from raw data coming from the sensors. An important cue for objects discrimination is the orientation of edges, that is used both in artificial vision and touch as pre-processing stage. We present a spiking neural network, inspired on the encoding of edges in human first order tactile afferents. The network uses three layers of Leaky Integrate and Fire neurons to distinguish different edge orientations of a bar pressed on the artificial skin of the iCub robot. The architecture is successfully able to discriminate eight different orientations (from 0oto 180o), by implementing a structured model of overlapping receptive fields. We demonstrate that the network can learn the appropriate connectivity through unsupervised spike based learning, and that the number and spatial distribution of sensitive areas within the receptive fields are important in edge orientation discrimination.

We propose a method to address audio-visual target speaker enhancement in multi-talker environments using eventdriven cameras. State of the art audio-visual speech separation methods shows that crucial information is the movement of the facial landmarks related to speech production. However, all approaches proposed so far work offline, using frame-based video input, making it difficult to process an audio-visual signal with low latency, for online applications. In order to overcome this limitation, we propose the use of event-driven cameras and exploit compression, high temporal resolution and low latency, for low cost and low latency motion feature extraction, going towards online embedded audio-visual speech processing. We use the event-driven optical flow estimation of the facial landmarks as input to a stacked Bidirectional LSTM trained to predict an Ideal Amplitude Mask that is then used to filter the noisy audio, to obtain the audio signal of the target speaker. The presented approach performs almost on pair with the frame- based approach, with very low latency and computational cost.

Fast, non-linear trajectories have been shown to be more accurately visually measured, and hence predicted, when sampled spatially (that is when the target position changes) rather than temporally, i.e. at a fixed-rate as in traditional frame-based cameras. Event-cameras, with their asynchronous, low latency information stream, allow for spatial sampling with very high temporal resolution, improving the quality of the data and the accuracy of post-processing operations. This paper investigates the use of Long Short-Term Memory (LSTM) networks with event-cameras spatial sampling for trajectory prediction. We show the benefit of using an Encoder-Decoder architecture over parameterised models for regression on event-based human-to-robot handover trajectories. In particular, we exploit the temporal information associated to the events stream to predict not only the incoming spatial trajectory points, but also when these will occur in time. After having studied the proper LSTM input/output sequence length, the network performance are compared to other regression models. Then, prediction behavior and computational time are analysed for the proposed method. We carry out the experiment using an iCub robot equipped with event-cameras, addressing the problem from the robot perspective.

Autonomous robots can rely on attention mechanisms to explore complex scenes and select salient stimuli relevant for behaviour. Stimulus selection should be fast to efficiently allocate available (and limited) computational resources to process in detail a subset of the otherwise overwhelmingly large sensory input. The amount of processing required is a product of the amount of data sampled by a robot's sensors; while a standard RGB camera produces a fixed amount of data for every pixel of the sensor, an event-camera produces data only for where there is a contrast change in the field of view, and does so with a lower latency. In this paper, we describe the implementation of a state-of-the-art bottom-up attention model, based on structuring the visual scene in terms of proto-objects. As an event-camera encodes different visual information compared to frame-based cameras, the original algorithm must be adapted and modified. We find that the event-camera's inherent detection of edges removes the need for some early stages of processing in the model. We describe the modifications, compare the event-driven algorithm to the original, and validate the potential for use on the iCub humanoid robot.

This demonstration presents the first implementation of “event-driven” serial asynchronous communication on optical fiber. “event-driven” communication is used by neuromorphic sensors, that sample the sensory signal when the signal itself changes of a given amount. This type of sensing adapts to the dynamics of the input itself, achieving at the same time extremely high temporal resolution (when needed), low latency and signal compression. To apply this technology in robotics, optical communication will greatly improve the resilience to electric disturbances.

We propose a novel method for computationally efficient audio-visual voice activity detection (VAD) where visual temporal information is provided by an energy efficient event-camera (EC). Unlike conventional cameras, ECs perform on-chip low-power pixel-level change detection, adapting the sampling frequency to the dynamics of the activity in the visual scene and removing redundancy, hence enabling energy and computational efficiency. In our VAD pipeline, first, lip activity is located and detected jointly by a probabilistic estimation after spatio-temporal filtering. Then, over the lips, a feather-weight speech-related lip motion detection is performed with minimum false negative rate to activate a highly accurate but expensive acoustic deep neural networks-based VAD. Our experiments show that ECs are accurate at detecting and locating lip activity; and EC-driven VAD can result in considerable savings in computations as well as can substantially reduce false positive rates in low acoustic signal-to-noise ratio conditions.

Event cameras offer many advantages for dynamic robotics due to their low latency response to motion, high dynamic range, and inherent compression of the visual signal. Many algorithms easily achieve real-time performance when testing on off-line datasets, however with an increase in camera resolution and applications on fast-moving robots, latency-free operation is not guaranteed. The event-rate is not constant, but is proportional to the amount of movement in the scene, or the velocity of the camera itself. Recently, algorithms have instead reported a maximum event-rate that can be achieved in real-time. In this paper we present the event-driven framework used on the iCub robot, which closes the loop between algorithm processing rate and the actual event-rate of the camera in order to smoothly control and limit the latency, while allowing the algorithm to degrade gracefully when large bursts of events occur. We show two algorithms that process events differently from each other and demonstrate the trade-off between latency and algorithm performance that the framework provides.

Event cameras are an emerging technology in computer vision, offering extremely low latency and bandwidth, as well as a high temporal resolution and dynamic range. Inherent data compression is achieved as pixel data is only produced by contrast changes at the edges of moving objects. However, current trends in state-of-the-art visual algorithms rely on deep-learning with networks designed to process colour and intensity information contained in dense arrays, but are notoriously computationally heavy. While the combination of these visual technologies could lead to fast, efficient, and accurate detection and recognition algorithms, it is uncertain whether the compressed event-camera data actually contain the required information for these techniques to discriminate between objects and a cluttered background. This paper presents a pilot study in which off-the-shelf deep-learning is applied to visual events for object detection on the iCub robotic platform, and analyses the impact of temporal integration of the event data. We also present a novel pipeline that bootstraps event-based dataset annotation from mature frame-based algorithms, in order to more quickly generate the required datasets.

The demonstration presents a robotic event-based tactile infrastructure for a humanoid robot. It leverages on currently deployed sample-based capacitive sensors to generate tactile events, enabling the investigation and development of event-driven tactile applications, and minimizing communication bandwidth and latency. The modular FPGA-based system samples data from tactile sensors and generates address-events, transmitted through an asynchronous serial address-event representation protocol. To enable performance comparisons of the event-driven approach with respect to standard sample-based solutions, the acquisition modules can directly forward the input samples through the same event-based communication channel. We will show in real time a comparison between the tactile events and the original sampled data generated when the skin patch is touched.

We propose a method to compress the enormous amount of data originating from tactile sensors is presented that explicitly exploits the inherent sparseness over space and time, sending tactile “events” only when a contact is detected. The resulting modular architecture is based on FPGA modules that acquire data samples from off-the-shelf tactile sensors based on capacitive transducers and generate and transmit an event-driven readout. This architecture has been specifically implemented for integration on robots with a large number of tactile sensors, to reduce communication bandwidth, power and processing requirements. An asynchronous serial address-event representation protocol further optimises effective data transmission rate (efficiency of 94.1%) and latency (340 ns) with respect to more common transmission protocols (e.g., Ethernet, CAN). We propose two complementary algorithms for the translation of raw-data into events, optimising data rate and bandwidth, or exploiting the asynchronous nature of the event-driven encoding and the temporal information within the sensory signal. Data reduction capability can reach up to 20 % of the correspondent clock-based encoding, with limited information loss due to the compression.

Event cameras are a new technology that can enable low-latency, fast visual sensing in dynamic environments towards faster robotic vision as they respond only to changes in the scene and have a very high temporal resolution (<; 1μs). Moving targets produce dense spatio-temporal streams of events that do not suffer from information loss “between frames”, which can occur when traditional cameras are used to track fast-moving targets. Event-based tracking algorithms need to be able to follow the target position within the spatio-temporal data, while rejecting clutter events that occur as a robot moves in a typical office setting. We introduce a particle filter with the aim to be robust to temporal variation that occurs as the camera and the target move with different relative velocities, which can lead to a loss in visual information and missed detections. The proposed system provides a more persistent tracking compared to prior state-of-the-art, especially when the robot is actively following a target with its gaze. Experiments are performed on the iCub humanoid robot performing ball tracking and gaze following.

The fast temporal-dynamics and intrinsic motion segmentation of event-based cameras are beneficial for robotic tasks that require low-latency visual tracking and control, for example a robot catching a ball. When the event-driven iCub humanoid robot grasps an object its head and torso move, inducing camera motion, and tracked objects become no longer trivially segmented amongst the mass of background clutter. Current event-based tracking algorithms have mostly considered stationary cameras that have clean event-streams with minimal clutter. This paper introduces novel methods to extend the Hough-based circle detection algorithm using optical flow information that is readily extracted from the spatio-temporal event space. Results indicate the proposed directed-Hough algorithm is more robust to other moving objects and the background event-clutter. Finally, we demonstrate successful on-line robot control and gaze following on the iCub robot.

The detection of consistent feature points in an image is fundamental for various kinds of computer vision techniques, such as stereo matching, object recognition, target tracking and optical flow computation. This paper presents an event-based approach to the detection of corner points, which benefits from the high temporal resolution, compressed visual information and low latency provided by an asynchronous neuromorphic event-based camera. The proposed method adapts the commonly used Harris corner detector to the event-based data, in which frames are replaced by a stream of asynchronous events produced in response to local light changes at μs temporal resolution. Responding only to changes in its field of view, an event-based camera naturally enhances edges in the scene, simplifying the detection of corner features. We characterised and tested the method on both a controlled pattern and a real scenario, using the dynamic vision sensor (DVS) on the neuromorphic iCub robot. The method detects corners with a typical error distribution within 2 pixels. The error is constant for different motion velocities and directions, indicating a consistent detection across the scene and over time. We achieve a detection rate proportional to speed, higher than frame-based technique for a significant amount of motion in the scene, while also reducing the computational cost.

We present an event-driven tactile sensing element that encodes both the absolute value of the input force and its variation over time. It is based on the POSFET device and Leaky-Integrate and Fire neurons, connected by a transconductance amplifier; the proposed circuit exploits the advantages of the POSFET device, such as high integration scale, fast response, wide bandwidth and force sensitivity, as well as the advantages of event-driven encoding, such as low latency, low power dissipation, and high temporal resolution, coupled with redundancy reduction.

Event-driven vision sensors have the potential to support a new generation of efficient and robust robots. This requires the development of a new computational framework that exploits not only the spatial information, like in the traditional frame-based approach, but also the temporal content of the sensory data. We propose a method for unsupervised learning of filters for the processing of the visual signal from event-driven sensors. This method exploits the temporal coincidence of events generated by each object in a spatial location of the visual field. The approach is based on a modification of Spike Timing Dependent Plasticity that takes into account the specific implementation on the robot and the characteristics of the used sensor. It gives rise to oriented spatial filters that are very similar to the receptive fields observed in the primary visual cortex and traditionally used in bio-inspired hierarchical structures for object recognition, as well as to novel curved spatial structures. Using mutual information measure we provide a quantitative evidence that such curved spatial filters provide more information than equivalent oriented Gabor filters and can be an important aspect for object recognition in robotic applications.

On-chip AER serialization is a required step for the successful integration of event-driven neuromorphic devices on complex robotic platforms. We propose an architecture and its implementation on AMS 180nm technology, synthesised with the Quasi-Delay Insensitive design principles and with minimum timing assumptions. The serialization of 19 bits completes in 70ns with an average power consumption of 2.34mW.

This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray levels into spike timings is currently at the basis of almost every spike-based modeling of biological visual systems. The use of images naturally leads to generating incorrect artificial and redundant spike timings and, more important, also contradicts biological findings indicating that visual processing is massively parallel, asynchronous with high temporal resolution. A new concept for acquiring visual information through pixel-individual asynchronous level-crossing sampling has been proposed in a recent generation of asynchronous neuromorphic visual sensors. Unlike conventional cameras, these sensors acquire data not at fixed points in time for the entire array but at fixed amplitude changes of their input, resulting optimally sparse in space and time-pixel individually and precisely timed only if new, (previously unknown) information is available (event based). This letter uses the high temporal resolution spiking output of neuromorphic event-based visual sensors to show that lowering time precision degrades performance on several recognition tasks specifically when reaching the conventional range of machine vision acquisition frequencies (30-60 Hz). The use of information theory to characterize separability between classes for each temporal resolution shows that high temporal acquisition provides up to 70% more information that conventional spikes generated from frame-based acquisition as used in standard artificial vision, thus drastically increasing the separability between classes of objects. Experiments on real data show that the amount of information loss is correlated with temporal precision. Our information-theoretic study highlights the potentials of neuromorphic asynchronous visual sensors for both practical applications and theoretical investigations. Moreover, it suggests that representing visual information as a precise sequence of spike times as reported in the retina offers considerable advantages for neuro-inspired visual computations.

Object tracking is an important step in many artificial vision tasks. The current state-of-the-art implementations remain too computationally demanding for the problem to be solved in real time with high dynamics. This paper presents a novel real-time method for visual part-based tracking of complex objects from the output of an asynchronous event-based camera. This paper extends the pictorial structures model introduced by Fischler and Elschlager 40 years ago and introduces a new formulation of the problem, allowing the dynamic processing of visual input in real time at high temporal resolution using a conventional PC. It relies on the concept of representing an object as a set of basic elements linked by springs. These basic elements consist of simple trackers capable of successfully tracking a target with an ellipse-like shape at several kilohertz on a conventional computer. For each incoming event, the method updates the elastic connections established between the trackers and guarantees a desired geometric structure corresponding to the tracked object in real time. This introduces a high temporal elasticity to adapt to projective deformations of the tracked object in the focal plane. The elastic energy of this virtual mechanical system provides a quality criterion for tracking and can be used to determine whether the measured deformations are caused by the perspective projection of the perceived object or by occlusions. Experiments on real-world data show the robustness of the method in the context of dynamic face tracking.

In this work, we report a novel circuit architecture to implement event-driven tactile sensing using the POSFET tactile device. The proposed circuit matches advantages of the POSFET device (integration of sensing and electronics on the same die, high electromechanical transduction bandwidth, etc.) with the ones of the event-driven approach. In the proposed circuit, the POSFET device is interfaced with a spiking neuron, of the type integrate and fire: the input mechanical stimulus is translated into digital pulses. The proposed approach paves the way for the implementation of neuromorphic integrated tactile sensing systems based on POSFET devices.

Homeostatic plasticity is a property of biological neural circuits that stabilizes their neuronal firing rates in face of input changes or environmental variations. Synaptic scaling is a particular homeostatic mechanism that acts at the level of the single neuron over long time scales, by changing the gain of all its afferent synapses to maintain the neuron's mean firing within proper operating bounds. In this paper we present ultra low leakage analog circuits that allow the integration of compact integrated filters in multi-neuron chips, able to achieve time constants of the order of hundreds of seconds, and describe automatic gain control circuits that when interfaced to neuromorphic neuron and synapse circuits implement faithful models of biologically realistic synaptic scaling mechanisms. We present simulation results of the low leakage circuits and describe the control circuits that have been designed for a neuromorphic multi-neuron chip, fabricated using a standard 180nm CMOS process.

Several analog and digital brain-inspired electronic systems have been recently proposed as dedicated solutions for fast simulations of spiking neural networks. While these architectures are useful for exploring the computational properties of large-scale models of the nervous system, the challenge of building low-power compact physical artifacts that can behave intelligently in the real world and exhibit cognitive abilities still remains open. In this paper, we propose a set of neuromorphic engineering solutions to address this challenge. In particular, we review neuromorphic circuits for emulating neural and synaptic dynamics in real time and discuss the role of biophysically realistic temporal dynamics in hardware neural processing architectures; we review the challenges of realizing spike-based plasticity mechanisms in real physical systems and present examples of analog electronic circuits that implement them;we describe the computational properties of recurrent neural networks and show how neuromorphic winner-take-all circuits can implement working-memory and decision-making mechanisms. We validate the neuromorphic approach proposed with experimental results obtained from our own circuits and systems, and argue how the circuits and networks presented in this work represent a useful set of components for efficiently and elegantly implementing neuromorphic cognition.

This paper introduces a new methodology to compute dense visual flow using the precise timings of spikes from an asynchronous event-based retina. Biological retinas, and their artificial counterparts, are totally asynchronous and data-driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework to estimate visual flow from the local properties of events' spatiotemporal space. We will show that precise visual flow orientation and amplitude can be estimated using a local differential approach on the surface defined by coactive events. Experimental results are presented; they show the method adequacy with high data sparseness and temporal resolution of event-based acquisition that allows the computation of motion flow with microsecond accuracy and at very low computational cost.

We present a compact vision sensor comprising a one-dimensional array of adaptive photo-receptors, spatio temporal feature extraction circuits, feature normalization circuits, and an attentional readout circuit that selects the most salient region in the feature map. The sensor comprises also digital input and output circuits for directly interfacing it to digital processing units, making it an ideal device for mobile robotic applications. We describe the sensor architecture and present experimental results measured from the fabricated chip. As we identified unexpected results from one of the computational stages, we compare the measured responses to circuit simulations and propose improvements for new revisions of the chip.

Synapses are crucial elements for computation and information transfer in both real and artificial neural systems. Recent experimental findings and theoretical models of pulse-based neural networks suggest that synaptic dynamics can play a crucial role for learning neural codes and encoding spatiotemporal spike patterns. Within the context of hardware implementations of pulse-based neural networks, several analog VLSI circuits modeling synaptic functionality have been proposed. We present an overview of previously proposed circuits and describe a novel analog VLSI synaptic circuit suitable for integration in large VLSI spike-based neural systems. The circuit proposed is based on a computational model that fits the real postsynaptic currents with exponentials. We present experimental data showing how the circuit exhibits realistic dynamics and show how it can be connected to additional modules for implementing a wide range of synaptic properties.

Selective attention is the strategy used by biological sensory systems to solve the problem of limited parallel processing capacity: salient subregions of the input stimuli are serially processed, while nonsalient regions are suppressed. We present an mixed mode analog/digital Very Large Scale Integration implementation of a building block for a multichip neuromorphic hardware model of selective attention. We describe the chip's architecture and its behavior, when its is part of a multichip system with a spiking retina as input, and show how it can be used to implement in real-time flexible models of bottom-up attention.

A cortical model for motion-in-depth selectivity of complex cells in the visual cortex is proposed. The model is based on a time ex(cid:173) tension of the phase-based techniques for disparity estimation. We consider the computation of the total temporal derivative of the time-varying disparity through the combination of the responses of disparity energy units. To take into account the physiological plau(cid:173) sibility, the model is based on the combinations of binocular cells characterized by different ocular dominance indices. The resulting cortical units of the model show a sharp selectivity for motion-in(cid:173) depth that has been compared with that reported in the literature for real cortical cells.