24 research outputs found
Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Address event representation (AER) cameras have recently attracted more
attention due to the advantages of high temporal resolution and low power
consumption, compared with traditional frame-based cameras. Since AER cameras
record the visual input as asynchronous discrete events, they are inherently
suitable to coordinate with the spiking neural network (SNN), which is
biologically plausible and energy-efficient on neuromorphic hardware. However,
using SNN to perform the AER object classification is still challenging, due to
the lack of effective learning algorithms for this new representation. To
tackle this issue, we propose an AER object classification model using a novel
segmented probability-maximization (SPA) learning algorithm. Technically, 1)
the SPA learning algorithm iteratively maximizes the probability of the classes
that samples belong to, in order to improve the reliability of neuron responses
and effectiveness of learning; 2) a peak detection (PD) mechanism is introduced
in SPA to locate informative time points segment by segment, based on which
information within the whole event stream can be fully utilized by the
learning. Extensive experimental results show that, compared to
state-of-the-art methods, not only our model is more effective, but also it
requires less information to reach a certain level of accuracy.Comment: AAAI 2020 (Oral
A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks
Spiking neural networks (SNNs) have demonstrated excellent capabilities in
various intelligent scenarios. Most existing methods for training SNNs are
based on the concept of synaptic plasticity; however, learning in the realistic
brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike
threshold of biological neurons is a critical intrinsic neuronal feature that
exhibits rich dynamics on a millisecond timescale and has been proposed as an
underlying mechanism that facilitates neural information processing. In this
study, we develop a novel synergistic learning approach that simultaneously
trains synaptic weights and spike thresholds in SNNs. SNNs trained with
synapse-threshold synergistic learning (STL-SNNs) achieve significantly higher
accuracies on various static and neuromorphic datasets than SNNs trained with
two single-learning models of the synaptic learning (SL) and the threshold
learning (TL). During training, the synergistic learning approach optimizes
neural thresholds, providing the network with stable signal transmission via
appropriate firing rates. Further analysis indicates that STL-SNNs are robust
to noisy data and exhibit low energy consumption for deep network structures.
Additionally, the performance of STL-SNN can be further improved by introducing
a generalized joint decision framework (JDF). Overall, our findings indicate
that biologically plausible synergies between synaptic and intrinsic
non-synaptic mechanisms may provide a promising approach for developing highly
efficient SNN learning methods.Comment: 13 pages, 9 figures, submitted for publicatio
Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation
Over the past decade, artificial neural networks (ANNs) have made tremendous
advances, in part due to the increased availability of annotated data. However,
ANNs typically require significant power and memory consumptions to reach their
full potential. Spiking neural networks (SNNs) have recently emerged as a
low-power alternative to ANNs due to their sparsity nature. SNN, however, are
not as easy to train as ANNs. In this work, we propose a hybrid SNN training
scheme and apply it to segment human hippocampi from magnetic resonance images.
Our approach takes ANN-SNN conversion as an initialization step and relies on
spike-based backpropagation to fine-tune the network. Compared with the
conversion and direct training solutions, our method has advantages in both
segmentation accuracy and training efficiency. Experiments demonstrate the
effectiveness of our model in achieving the design goals.Comment: Accepted to ISBI 2023 conferenc
Parallel computing for brain simulation
[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced.
Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain.
Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Conseller铆a de Cultura, Educaci贸n e Ordenaci贸n Universitaria; GRC2014/049Galicia. Conseller铆a de Cultura, Educaci贸n e Ordenaci贸n Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028
Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks
Spiking neural networks (SNNs) are bio-inspired neural networks with
asynchronous discrete and sparse characteristics, which have increasingly
manifested their superiority in low energy consumption. Recent research is
devoted to utilizing spatio-temporal information to directly train SNNs by
backpropagation. However, the binary and non-differentiable properties of spike
activities force directly trained SNNs to suffer from serious gradient
vanishing and network degradation, which greatly limits the performance of
directly trained SNNs and prevents them from going deeper. In this paper, we
propose a multi-level firing (MLF) method based on the existing spatio-temporal
back propagation (STBP) method, and spiking dormant-suppressed residual network
(spiking DS-ResNet). MLF enables more efficient gradient propagation and the
incremental expression ability of the neurons. Spiking DS-ResNet can
efficiently perform identity mapping of discrete spikes, as well as provide a
more suitable connection for gradient propagation in deep SNNs. With the
proposed method, our model achieves superior performances on a non-neuromorphic
dataset and two neuromorphic datasets with much fewer trainable parameters and
demonstrates the great ability to combat the gradient vanishing and degradation
problem in deep SNNs.Comment: Accepted by the Thirty-First International Joint Conference on
Artificial Intelligence (IJCAI-22
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Computational models of object motion detectors accelerated using FPGA technology
The detection of moving objects is a trivial task when performed by vertebrate retinas, yet a complex computer vision task. This PhD research programme has made three key contributions, namely: 1) a multi-hierarchical spiking neural network (MHSNN) architecture for detecting horizontal and vertical movements, 2) a Hybrid Sensitive Motion Detector (HSMD) algorithm for detecting object motion and 3) the Neuromorphic Hybrid Sensitive Motion Detector (NeuroHSMD) , a real-time neuromorphic implementation of the HSMD algorithm.
The MHSNN is a customised 4 layers Spiking Neural Network (SNN) architecture designed to reflect the basic connectivity, similar to canonical behaviours found in the majority of vertebrate retinas (including human retinas). The architecture, was trained using images from a custom dataset generated in laboratory settings. Simulation results revealed that each cell model is sensitive to vertical and horizontal movements, with a detection error of 6.75% contrasted against the teaching signals (expected output signals) used to train the MHSNN. The experimental evaluation of the methodology shows that the MH SNN was not scalable because of the overall number of neurons and synapses which lead to the development of the HSMD.
The HSMD algorithm enhanced an existing Dynamic Background subtraction (DBS) algorithm using a customised 3-layer SNN. The customised 3-layer SNN was used to stabilise the foreground information of moving objects in the scene, which improves the object motion detection. The algorithm was compared against existing background subtraction approaches, available on the Open Computer Vision (OpenCV) library, specifically on the 2012 Change Detection (CDnet2012) and the 2014 Change Detection (CDnet2014) benchmark datasets. The accuracy results show that the HSMD was ranked overall first and performed better than all the other benchmarked algorithms on four of the categories, across all eight test metrics. Furthermore, the HSMD is the first to use an SNN to enhance the existing dynamic background subtraction algorithm without a substantial degradation of the frame rate, being capable of processing images 720 脳 480 at 13.82 Frames Per Second (fps) (CDnet2014) and 720 脳 480 at 13.92 fps (CDnet2012) on a High Performance computer (96 cores and 756 GB of RAM). Although the HSMD analysis shows good Percentage of Correct Classifications (PCC) on the CDnet2012 and CDnet2014, it was identified that the 3-layer customised SNN was the bottleneck, in terms of speed, and could be improved using dedicated hardware.
The NeuroHSMD is thus an adaptation of the HSMD algorithm whereby the SNN component has been fully implemented on dedicated hardware [Terasic DE10-pro Field-Programmable Gate Array (FPGA) board]. Open Computer Language (OpenCL) was used to simplify the FPGA design flow and allow the code portability to other devices such as FPGA and Graphical Processing Unit (GPU). The NeuroHSMD was also tested against the CDnet2012 and CDnet2014 datasets with an acceleration of 82% over the HSMD algorithm, being capable of processing 720 脳 480 images at 28.06 fps (CDnet2012) and 28.71 fps (CDnet2014)
EDFLOW: Event Driven Optical Flow Camera with Keypoint Detection and Adaptive Block Matching
Event cameras such as the Dynamic Vision Sensor (DVS) are useful because of their low latency, sparse output, and high dynamic range. In this paper, we propose a DVS+FPGA camera platform and use it to demonstrate the hardware implementation of event-based corner keypoint detection and adaptive block-matching optical flow. To adapt sample rate dynamically, events are accumulated in event slices using the area event count slice exposure method. The area event count is feedback controlled by the average optical flow matching distance. Corners are detected by streaks of accumulated events on event slice rings of radius 3 and 4 pixels. Corner detection takes about 6 clock cycles (16 MHz event rate at the 100MHz clock frequency) At the corners, flow vectors are computed in 100 clock cycles (1 MHz event rate). The multiscale block match size is 25x25 pixels and the flow vectors span up to 30-pixel match distance. The FPGA processes the sum-of-absolute distance block matching at 123 GOp/s, the equivalent of 1230 Op/clock cycle. EDFLOW is several times more accurate on MVSEC drone and driving optical flow benchmarking sequences than the previous best DVS FPGA optical flow implementation, and achieves similar accuracy to the CNN-based EV-Flownet, although it burns about 100 times less power. The EDFLOW design and benchmarking videos are available at https://sites.google.com/view/edflow21/home