1
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Ng HW, Guan C. Deep Unsupervised Representation Learning for Feature-Informed EEG Domain Extraction. IEEE Trans Neural Syst Rehabil Eng 2023; 31:4882-4894. [PMID: 38048235 DOI: 10.1109/tnsre.2023.3339179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
In electroencephalography (EEG) classification paradigms, data from a target subject is often difficult to obtain, leading to difficulties in training a robust deep learning network. Transfer learning and their variations are effective tools in improving such models suffering from lack of data. However, many of the proposed variations and deep models often rely on a single assumed distribution to represent the latent features which may not scale well due to inter- and intra-subject variations in signals. This leads to significant instability in individual subject decoding performances. The presence of non-trivial domain differences between different sets of training or transfer learning data causes poorer model generalization towards the target subject. However, the detection of these domain differences is often difficult to perform due to the ill-defined nature of the EEG domain features. This study proposes a novel inference model, the Joint Embedding Variational Autoencoder, that offers conditionally tighter approximation of the estimated spatiotemporal feature distribution through the use of jointly optimised variational autoencoders to achieve optimizable data dependent inputs as an additional variable for improved overall model optimisation and scaling without sacrificing model tightness. To learn the variational bound, we show that maximising the marginal log-likelihood of only the second embedding section is required to achieve conditionally tighter lower bounds. Furthermore, we show that this model provides state-of-the-art EEG data reconstruction and deep feature extraction. The extracted domains of the EEG signals across each subject displays the rationale as to why there exists disparity between subjects' adaptation efficacy.
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2
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Factorization of Broad Expansion for Broad Learning System. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.02.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
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3
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Xu Y, Perera S, Bethi Y, Afshar S, van Schaik A. Event-driven spectrotemporal feature extraction and classification using a silicon cochlea model. Front Neurosci 2023; 17:1125210. [PMID: 37144092 PMCID: PMC10151790 DOI: 10.3389/fnins.2023.1125210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/27/2023] [Indexed: 05/06/2023] Open
Abstract
This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR-FAC) cochlea models and leaky integrate-and-fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks.
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4
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Ralph N, Joubert D, Jolley A, Afshar S, Tothill N, van Schaik A, Cohen G. Real-Time Event-Based Unsupervised Feature Consolidation and Tracking for Space Situational Awareness. Front Neurosci 2022; 16:821157. [PMID: 35600627 PMCID: PMC9120364 DOI: 10.3389/fnins.2022.821157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 04/04/2022] [Indexed: 11/19/2022] Open
Abstract
Earth orbit is a limited natural resource that hosts a vast range of vital space-based systems that support the international community's national, commercial and defence interests. This resource is rapidly becoming depleted with over-crowding in high demand orbital slots and a growing presence of space debris. We propose the Fast Iterative Extraction of Salient targets for Tracking Asynchronously (FIESTA) algorithm as a robust, real-time and reactive approach to optical Space Situational Awareness (SSA) using Event-Based Cameras (EBCs) to detect, localize, and track Resident Space Objects (RSOs) accurately and timely. We address the challenges of the asynchronous nature and high temporal resolution output of the EBC accurately, unsupervised and with few tune-able parameters using concepts established in the neuromorphic and conventional tracking literature. We show this algorithm is capable of highly accurate in-frame RSO velocity estimation and average sub-pixel localization in a simulated test environment to distinguish the capabilities of the EBC and optical setup from the proposed tracking system. This work is a fundamental step toward accurate end-to-end real-time optical event-based SSA, and developing the foundation for robust closed-form tracking evaluated using standardized tracking metrics.
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Affiliation(s)
- Nicholas Ralph
- International Centre for Neuromorphic Engineering, MARCS Institute for Brain Behaviour and Development, Western Sydney University, Werrington, NSW, Australia
- *Correspondence: Nicholas Ralph
| | - Damien Joubert
- International Centre for Neuromorphic Engineering, MARCS Institute for Brain Behaviour and Development, Western Sydney University, Werrington, NSW, Australia
| | - Andrew Jolley
- International Centre for Neuromorphic Engineering, MARCS Institute for Brain Behaviour and Development, Western Sydney University, Werrington, NSW, Australia
- Air and Space Power Development Centre, Royal Australian Air Force, Canberra, ACT, Australia
| | - Saeed Afshar
- International Centre for Neuromorphic Engineering, MARCS Institute for Brain Behaviour and Development, Western Sydney University, Werrington, NSW, Australia
| | - Nicholas Tothill
- International Centre for Neuromorphic Engineering, MARCS Institute for Brain Behaviour and Development, Western Sydney University, Werrington, NSW, Australia
| | - André van Schaik
- International Centre for Neuromorphic Engineering, MARCS Institute for Brain Behaviour and Development, Western Sydney University, Werrington, NSW, Australia
| | - Gregory Cohen
- International Centre for Neuromorphic Engineering, MARCS Institute for Brain Behaviour and Development, Western Sydney University, Werrington, NSW, Australia
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5
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Tayarani-Najaran MH, Schmuker M. Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review. Front Neural Circuits 2021; 15:610446. [PMID: 34135736 PMCID: PMC8203204 DOI: 10.3389/fncir.2021.610446] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
The nervous systems converts the physical quantities sensed by its primary receptors into trains of events that are then processed in the brain. The unmatched efficiency in information processing has long inspired engineers to seek brain-like approaches to sensing and signal processing. The key principle pursued in neuromorphic sensing is to shed the traditional approach of periodic sampling in favor of an event-driven scheme that mimicks sampling as it occurs in the nervous system, where events are preferably emitted upon the change of the sensed stimulus. In this paper we highlight the advantages and challenges of event-based sensing and signal processing in the visual, auditory and olfactory domains. We also provide a survey of the literature covering neuromorphic sensing and signal processing in all three modalities. Our aim is to facilitate research in event-based sensing and signal processing by providing a comprehensive overview of the research performed previously as well as highlighting conceptual advantages, current progress and future challenges in the field.
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Affiliation(s)
| | - Michael Schmuker
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom
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6
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Gauvain G, Akolkar H, Chaffiol A, Arcizet F, Khoei MA, Desrosiers M, Jaillard C, Caplette R, Marre O, Bertin S, Fovet CM, Demilly J, Forster V, Brazhnikova E, Hantraye P, Pouget P, Douar A, Pruneau D, Chavas J, Sahel JA, Dalkara D, Duebel J, Benosman R, Picaud S. Optogenetic therapy: high spatiotemporal resolution and pattern discrimination compatible with vision restoration in non-human primates. Commun Biol 2021; 4:125. [PMID: 33504896 PMCID: PMC7840970 DOI: 10.1038/s42003-020-01594-w] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 12/09/2020] [Indexed: 01/06/2023] Open
Abstract
Vision restoration is an ideal medical application for optogenetics, because the eye provides direct optical access to the retina for stimulation. Optogenetic therapy could be used for diseases involving photoreceptor degeneration, such as retinitis pigmentosa or age-related macular degeneration. We describe here the selection, in non-human primates, of a specific optogenetic construct currently tested in a clinical trial. We used the microbial opsin ChrimsonR, and showed that the AAV2.7m8 vector had a higher transfection efficiency than AAV2 in retinal ganglion cells (RGCs) and that ChrimsonR fused to tdTomato (ChR-tdT) was expressed more efficiently than ChrimsonR. Light at 600 nm activated RGCs transfected with AAV2.7m8 ChR-tdT, from an irradiance of 1015 photons.cm−2.s−1. Vector doses of 5 × 1010 and 5 × 1011 vg/eye transfected up to 7000 RGCs/mm2 in the perifovea, with no significant immune reaction. We recorded RGC responses from a stimulus duration of 1 ms upwards. When using the recorded activity to decode stimulus information, we obtained an estimated visual acuity of 20/249, above the level of legal blindness (20/400). These results lay the groundwork for the ongoing clinical trial with the AAV2.7m8 - ChR-tdT vector for vision restoration in patients with retinitis pigmentosa. Gauvain et al demonstrate that optogenetic therapy using the AAV2.7m8- ChR-tdT construct can partially restore vision in non-human primates to levels above those considered legally-blind. This study enables the identification of the most suitable construct for ongoing clinical trials attempting vision restoration in patients with retinitis pigmentosa.
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Affiliation(s)
- Gregory Gauvain
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France.
| | - Himanshu Akolkar
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France.,Department of Ophthalmology, University Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Antoine Chaffiol
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Fabrice Arcizet
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Mina A Khoei
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Mélissa Desrosiers
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Céline Jaillard
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Romain Caplette
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Olivier Marre
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Stéphane Bertin
- CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, F-75012, Paris, France
| | - Claire-Maelle Fovet
- Département des Sciences du Vivant (DSV), MIRcen, Institut d'imagerie Biomédicale (I2BM), Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), 92260, Fontenay-aux-Roses, France
| | - Joanna Demilly
- Département des Sciences du Vivant (DSV), MIRcen, Institut d'imagerie Biomédicale (I2BM), Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), 92260, Fontenay-aux-Roses, France
| | - Valérie Forster
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Elena Brazhnikova
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Philippe Hantraye
- Département des Sciences du Vivant (DSV), MIRcen, Institut d'imagerie Biomédicale (I2BM), Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), 92260, Fontenay-aux-Roses, France
| | - Pierre Pouget
- ICM, UMRS 1127 UPMC - U 1127 INSERM - UMR 7225 CNRS, Paris, France
| | - Anne Douar
- Gensight Biologics, 74 rue du faubourg Saint Antoine, F-75012, Paris, France
| | - Didier Pruneau
- Gensight Biologics, 74 rue du faubourg Saint Antoine, F-75012, Paris, France
| | - Joël Chavas
- Gensight Biologics, 74 rue du faubourg Saint Antoine, F-75012, Paris, France
| | - José-Alain Sahel
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France.,Department of Ophthalmology, University Pittsburgh Medical Center, Pittsburgh, PA, USA.,CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, 28 rue de Charenton, F-75012, Paris, France
| | - Deniz Dalkara
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Jens Duebel
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France
| | - Ryad Benosman
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France.,Department of Ophthalmology, University Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Serge Picaud
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, 17 rue Moreau, F-75012, Paris, France.
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7
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Ramesh B, Yang H, Orchard G, Le Thi NA, Zhang S, Xiang C. DART: Distribution Aware Retinal Transform for Event-Based Cameras. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2020; 42:2767-2780. [PMID: 31144625 DOI: 10.1109/tpami.2019.2919301] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras. The DART descriptor is applied to four different problems, namely object classification, tracking, detection and feature matching: (1) The DART features are directly employed as local descriptors in a bag-of-words classification framework and testing is carried out on four standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS, NCaltech-101); (2) Extending the classification system, tracking is demonstrated using two key novelties: (i) Statistical bootstrapping is leveraged with online learning for overcoming the low-sample problem during the one-shot learning of the tracker, (ii) Cyclical shifts are induced in the log-polar domain of the DART descriptor to achieve robustness to object scale and rotation variations; (3) To solve the long-term object tracking problem, an object detector is designed using the principle of cluster majority voting. The detection scheme is then combined with the tracker to result in a high intersection-over-union score with augmented ground truth annotations on the publicly available event camera dataset; (4) Finally, the event context encoded by DART greatly simplifies the feature correspondence problem, especially for spatio-temporal slices far apart in time, which has not been explicitly tackled in the event-based vision domain.
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8
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Xiao R, Tang H, Ma Y, Yan R, Orchard G. An Event-Driven Categorization Model for AER Image Sensors Using Multispike Encoding and Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:3649-3657. [PMID: 31714243 DOI: 10.1109/tnnls.2019.2945630] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this article, we present a systematic computational model to explore brain-based computation for object recognition. The model extracts temporal features embedded in address-event representation (AER) data and discriminates different objects by using spiking neural networks (SNNs). We use multispike encoding to extract temporal features contained in the AER data. These temporal patterns are then learned through the tempotron learning rule. The presented model is consistently implemented in a temporal learning framework, where the precise timing of spikes is considered in the feature-encoding and learning process. A noise-reduction method is also proposed by calculating the correlation of an event with the surrounding spatial neighborhood based on the recently proposed time-surface technique. The model evaluated on wide spectrum data sets (MNIST, N-MNIST, MNIST-DVS, AER Posture, and Poker Card) demonstrates its superior recognition performance, especially for the events with noise.
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9
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Paredes-Valles F, Scheper KYW, de Croon GCHE. Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2020; 42:2051-2064. [PMID: 30843817 DOI: 10.1109/tpami.2019.2903179] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The combination of spiking neural networks and event-based vision sensors holds the potential of highly efficient and high-bandwidth optical flow estimation. This paper presents the first hierarchical spiking architecture in which motion (direction and speed) selectivity emerges in an unsupervised fashion from the raw stimuli generated with an event-based camera. A novel adaptive neuron model and stable spike-timing-dependent plasticity formulation are at the core of this neural network governing its spike-based processing and learning, respectively. After convergence, the neural architecture exhibits the main properties of biological visual motion systems, namely feature extraction and local and global motion perception. Convolutional layers with input synapses characterized by single and multiple transmission delays are employed for feature and local motion perception, respectively; while global motion selectivity emerges in a final fully-connected layer. The proposed solution is validated using synthetic and real event sequences. Along with this paper, we provide the cuSNN library, a framework that enables GPU-accelerated simulations of large-scale spiking neural networks. Source code and samples are available at https://github.com/tudelft/cuSNN.
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10
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Maro JM, Ieng SH, Benosman R. Event-Based Gesture Recognition With Dynamic Background Suppression Using Smartphone Computational Capabilities. Front Neurosci 2020; 14:275. [PMID: 32327968 PMCID: PMC7160298 DOI: 10.3389/fnins.2020.00275] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 03/10/2020] [Indexed: 11/13/2022] Open
Abstract
In this paper, we introduce a framework for dynamic gesture recognition with background suppression operating on the output of a moving event-based camera. The system is developed to operate in real-time using only the computational capabilities of a mobile phone. It introduces a new development around the concept of time-surfaces. It also presents a novel event-based methodology to dynamically remove backgrounds that uses the high temporal resolution properties of event-based cameras. To our knowledge, this is the first Android event-based framework for vision-based recognition of dynamic gestures running on a smartphone without off-board processing. We assess the performances by considering several scenarios in both indoors and outdoors, for static and dynamic conditions, in uncontrolled lighting conditions. We also introduce a new event-based dataset for gesture recognition with static and dynamic backgrounds (made publicly available). The set of gestures has been selected following a clinical trial to allow human-machine interaction for the visually impaired and older adults. We finally report comparisons with prior work that addressed event-based gesture recognition reporting comparable results, without the use of advanced classification techniques nor power greedy hardware.
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Affiliation(s)
| | - Sio-Hoi Ieng
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
- CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, Paris, France
| | - Ryad Benosman
- Sorbonne Université, INSERM, CNRS, Institut de la Vision, Paris, France
- CHNO des Quinze-Vingts, INSERM-DGOS CIC 1423, Paris, France
- Departments of Ophthalmology/ECE/BioE, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Computer Science, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States
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11
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Afshar S, Ralph N, Xu Y, Tapson J, van Schaik A, Cohen G. Event-Based Feature Extraction Using Adaptive Selection Thresholds. SENSORS 2020; 20:s20061600. [PMID: 32183052 PMCID: PMC7146588 DOI: 10.3390/s20061600] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 03/07/2020] [Accepted: 03/08/2020] [Indexed: 11/25/2022]
Abstract
Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers. The feature extraction method is tested on both the N-MNIST (Neuromorphic-MNIST) benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage.
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12
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Afshar S, Hamilton TJ, Tapson J, van Schaik A, Cohen G. Investigation of Event-Based Surfaces for High-Speed Detection, Unsupervised Feature Extraction, and Object Recognition. Front Neurosci 2019; 12:1047. [PMID: 30705618 PMCID: PMC6344467 DOI: 10.3389/fnins.2018.01047] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 12/24/2018] [Indexed: 12/31/2022] Open
Abstract
In this work, we investigate event-based feature extraction through a rigorous framework of testing. We test a hardware efficient variant of Spike Timing Dependent Plasticity (STDP) on a range of spatio-temporal kernels with different surface decaying methods, decay functions, receptive field sizes, feature numbers, and back end classifiers. This detailed investigation can provide helpful insights and rules of thumb for performance vs. complexity trade-offs in more generalized networks, especially in the context of hardware implementation, where design choices can incur significant resource costs. The investigation is performed using a new dataset consisting of model airplanes being dropped free-hand close to the sensor. The target objects exhibit a wide range of relative orientations and velocities. This range of target velocities, analyzed in multiple configurations, allows a rigorous comparison of time-based decaying surfaces (time surfaces) vs. event index-based decaying surface (index surfaces), which are used to perform unsupervised feature extraction, followed by target detection and recognition. We examine each processing stage by comparison to the use of raw events, as well as a range of alternative layer structures, and the use of random features. By comparing results from a linear classifier and an ELM classifier, we evaluate how each element of the system affects accuracy. To generate time and index surfaces, the most commonly used kernels, namely event binning kernels, linearly, and exponentially decaying kernels, are investigated. Index surfaces were found to outperform time surfaces in recognition when invariance to target velocity was made a requirement. In the investigation of network structure, larger networks of neurons with large receptive field sizes were found to perform best. We find that a small number of event-based feature extractors can project the complex spatio-temporal event patterns of the dataset to an almost linearly separable representation in feature space, with best performing linear classifier achieving 98.75% recognition accuracy, using only 25 feature extracting neurons.
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Affiliation(s)
- Saeed Afshar
- Biomedical Engineering and Neuroscience Program, The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Sydney, NSW, Australia
| | - Tara Julia Hamilton
- Biomedical Engineering and Neuroscience Program, The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Sydney, NSW, Australia
| | - Jonathan Tapson
- Biomedical Engineering and Neuroscience Program, The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Sydney, NSW, Australia
| | - André van Schaik
- Biomedical Engineering and Neuroscience Program, The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Sydney, NSW, Australia
| | - Gregory Cohen
- Biomedical Engineering and Neuroscience Program, The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Sydney, NSW, Australia
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13
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Hopkins M, Pineda-García G, Bogdan PA, Furber SB. Spiking neural networks for computer vision. Interface Focus 2018; 8:20180007. [PMID: 29951187 PMCID: PMC6015816 DOI: 10.1098/rsfs.2018.0007] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2018] [Indexed: 12/13/2022] Open
Abstract
State-of-the-art computer vision systems use frame-based cameras that sample the visual scene as a series of high-resolution images. These are then processed using convolutional neural networks using neurons with continuous outputs. Biological vision systems use a quite different approach, where the eyes (cameras) sample the visual scene continuously, often with a non-uniform resolution, and generate neural spike events in response to changes in the scene. The resulting spatio-temporal patterns of events are then processed through networks of spiking neurons. Such event-based processing offers advantages in terms of focusing constrained resources on the most salient features of the perceived scene, and those advantages should also accrue to engineered vision systems based upon similar principles. Event-based vision sensors, and event-based processing exemplified by the SpiNNaker (Spiking Neural Network Architecture) machine, can be used to model the biological vision pathway at various levels of detail. Here we use this approach to explore structural synaptic plasticity as a possible mechanism whereby biological vision systems may learn the statistics of their inputs without supervision, pointing the way to engineered vision systems with similar online learning capabilities.
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Affiliation(s)
| | | | | | - Steve B. Furber
- School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
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14
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Neftci EO. Data and Power Efficient Intelligence with Neuromorphic Learning Machines. iScience 2018; 5:52-68. [PMID: 30240646 PMCID: PMC6123858 DOI: 10.1016/j.isci.2018.06.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 06/04/2018] [Accepted: 06/26/2018] [Indexed: 11/22/2022] Open
Abstract
The success of deep networks and recent industry involvement in brain-inspired computing is igniting a widespread interest in neuromorphic hardware that emulates the biological processes of the brain on an electronic substrate. This review explores interdisciplinary approaches anchored in machine learning theory that enable the applicability of neuromorphic technologies to real-world, human-centric tasks. We find that (1) recent work in binary deep networks and approximate gradient descent learning are strikingly compatible with a neuromorphic substrate; (2) where real-time adaptability and autonomy are necessary, neuromorphic technologies can achieve significant advantages over main-stream ones; and (3) challenges in memory technologies, compounded by a tradition of bottom-up approaches in the field, block the road to major breakthroughs. We suggest that a neuromorphic learning framework, tuned specifically for the spatial and temporal constraints of the neuromorphic substrate, will help guiding hardware algorithm co-design and deploying neuromorphic hardware for proactive learning of real-world data.
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Affiliation(s)
- Emre O Neftci
- Department of Cognitive Sciences, UC Irvine, Irvine, CA 92697-5100, USA; Department of Computer Science, UC Irvine, Irvine, CA 92697-5100, USA.
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Anumula J, Neil D, Delbruck T, Liu SC. Feature Representations for Neuromorphic Audio Spike Streams. Front Neurosci 2018; 12:23. [PMID: 29479300 PMCID: PMC5811520 DOI: 10.3389/fnins.2018.00023] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 01/11/2018] [Indexed: 11/24/2022] Open
Abstract
Event-driven neuromorphic spiking sensors such as the silicon retina and the silicon cochlea encode the external sensory stimuli as asynchronous streams of spikes across different channels or pixels. Combining state-of-art deep neural networks with the asynchronous outputs of these sensors has produced encouraging results on some datasets but remains challenging. While the lack of effective spiking networks to process the spike streams is one reason, the other reason is that the pre-processing methods required to convert the spike streams to frame-based features needed for the deep networks still require further investigation. This work investigates the effectiveness of synchronous and asynchronous frame-based features generated using spike count and constant event binning in combination with the use of a recurrent neural network for solving a classification task using N-TIDIGITS18 dataset. This spike-based dataset consists of recordings from the Dynamic Audio Sensor, a spiking silicon cochlea sensor, in response to the TIDIGITS audio dataset. We also propose a new pre-processing method which applies an exponential kernel on the output cochlea spikes so that the interspike timing information is better preserved. The results from the N-TIDIGITS18 dataset show that the exponential features perform better than the spike count features, with over 91% accuracy on the digit classification task. This accuracy corresponds to an improvement of at least 2.5% over the use of spike count features, establishing a new state of the art for this dataset.
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Affiliation(s)
- Jithendar Anumula
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Daniel Neil
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Tobi Delbruck
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Shih-Chii Liu
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland
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Kaiser J, Stal R, Subramoney A, Roennau A, Dillmann R. Scaling up liquid state machines to predict over address events from dynamic vision sensors. BIOINSPIRATION & BIOMIMETICS 2017; 12:055001. [PMID: 28569669 DOI: 10.1088/1748-3190/aa7663] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Short-term visual prediction is important both in biology and robotics. It allows us to anticipate upcoming states of the environment and therefore plan more efficiently. In theoretical neuroscience, liquid state machines have been proposed as a biologically inspired method to perform asynchronous prediction without a model. However, they have so far only been demonstrated in simulation or small scale pre-processed camera images. In this paper, we use a liquid state machine to predict over the whole [Formula: see text] event stream provided by a real dynamic vision sensor (DVS, or silicon retina). Thanks to the event-based nature of the DVS, the liquid is constantly fed with data when an object is in motion, fully embracing the asynchronicity of spiking neural networks. We propose a smooth continuous representation of the event stream for the short-term visual prediction task. Moreover, compared to previous works (2002 Neural Comput. 2525 282-93 and Burgsteiner H et al 2007 Appl. Intell. 26 99-109), we scale the input dimensionality that the liquid operates on by two order of magnitudes. We also expose the current limits of our method by running experiments in a challenging environment where multiple objects are in motion. This paper is a step towards integrating biologically inspired algorithms derived in theoretical neuroscience to real world robotic setups. We believe that liquid state machines could complement current prediction algorithms used in robotics, especially when dealing with asynchronous sensors.
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Affiliation(s)
- Jacques Kaiser
- FZI Research Center for Information Technology, 76131 Karlsruhe, Germany. Author to whom any correspondence should be addressed
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17
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Milde MB, Blum H, Dietmüller A, Sumislawska D, Conradt J, Indiveri G, Sandamirskaya Y. Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System. Front Neurorobot 2017; 11:28. [PMID: 28747883 PMCID: PMC5507184 DOI: 10.3389/fnbot.2017.00028] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2016] [Accepted: 05/22/2017] [Indexed: 11/13/2022] Open
Abstract
Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware.
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Affiliation(s)
- Moritz B Milde
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Hermann Blum
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Alexander Dietmüller
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Dora Sumislawska
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Jörg Conradt
- Neuroscientific System Theory, Department of Electrical and Computer Engineering, Technical University of MunichMunich, Germany
| | - Giacomo Indiveri
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
| | - Yulia Sandamirskaya
- Institute of Neuroinformatics, University of Zurich and ETH ZurichZurich, Switzerland
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Lagorce X, Orchard G, Galluppi F, Shi BE, Benosman RB. HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017; 39:1346-1359. [PMID: 27411216 DOI: 10.1109/tpami.2016.2574707] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.
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Affiliation(s)
- Xavier Lagorce
- Vision and Natural Computation Group, Institut National de la Santé et de la Recherche Médicale, Sorbonne Universités, Institut de la Vision, Université Paris 06, Paris, Paris, FranceFrance
| | - Garrick Orchard
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore
| | - Francesco Galluppi
- Vision and Natural Computation Group, Institut National de la Santé et de la Recherche Médicale, Sorbonne Universités, Institut de la Vision, Université Paris 06, Paris, Paris, FranceFrance
| | | | - Ryad B Benosman
- Vision and Natural Computation Group, Institut National de la Santé et de la Recherche Médicale, Sorbonne Universités, Institut de la Vision, Université Paris 06, Paris, Paris, FranceFrance
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Broccard FD, Joshi S, Wang J, Cauwenberghs G. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems. J Neural Eng 2017; 14:041002. [PMID: 28573983 DOI: 10.1088/1741-2552/aa67a9] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. APPROACH This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. MAIN RESULTS Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. SIGNIFICANCE Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a computational tool for investigating fundamental questions related to neural dynamics, the sophistication of current neuromorphic systems now allows direct interfaces with large neuronal networks and circuits, resulting in potentially interesting clinical applications for neuroengineering systems, neuroprosthetics and neurorehabilitation.
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Affiliation(s)
- Frédéric D Broccard
- Institute for Neural Computation, UC San Diego, United States of America. Department of Bioengineering, UC San Diego, United States of America
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