65 research outputs found
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Spatial Pyramid Matching (SPM) and its variants have achieved a lot of
success in image classification. The main difference among them is their
encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of
Vector Quantization (VQ) into the framework of SPM. Although the methods
achieve a higher recognition rate than the traditional SPM, they consume more
time to encode the local descriptors extracted from the image. In this paper,
we propose using Low Rank Representation (LRR) to encode the descriptors under
the framework of SPM. Different from SC, LRR considers the group effect among
data points instead of sparsity. Benefiting from this property, the proposed
method (i.e., LrrSPM) can offer a better performance. To further improve the
generalizability and robustness, we reformulate the rank-minimization problem
as a truncated projection problem. Extensive experimental studies show that
LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving
competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201
A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning
With the help of Deep Neural Networks, Deep Reinforcement Learning (DRL) has
achieved great success on many complex tasks during the past few years. Spiking
Neural Networks (SNNs) have been used for the implementation of Deep Neural
Networks with superb energy efficiency on dedicated neuromorphic hardware, and
recent years have witnessed increasing attention on combining SNNs with
Reinforcement Learning, whereas most approaches still work with huge energy
consumption and high latency. This work proposes the Adaptive Coding Spiking
Framework (ACSF) for SNN-based DRL and achieves low latency and great energy
efficiency at the same time. Inspired by classical conditioning in biology, we
simulate receptors, central interneurons, and effectors with spike encoders,
SNNs, and spike decoders, respectively. We use our proposed ACSF to estimate
the value function in reinforcement learning and conduct extensive experiments
to verify the effectiveness of our proposed framework
Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks
Networks of spiking neurons underpin the extraordinary information-processing
capabilities of the brain and have emerged as pillar models in neuromorphic
intelligence. Despite extensive research on spiking neural networks (SNNs),
most are established on deterministic models. Integrating noise into SNNs leads
to biophysically more realistic neural dynamics and may benefit model
performance. This work presents the noisy spiking neural network (NSNN) and the
noise-driven learning rule (NDL) by introducing a spiking neuron model
incorporating noisy neuronal dynamics. Our approach shows how noise may act as
a resource for computation and learning and theoretically provides a framework
for general SNNs. Moreover, NDL provides an insightful biological rationale for
surrogate gradients. By incorporating various SNN architectures and algorithms,
we show that our approach exhibits competitive performance and improved
robustness against challenging perturbations than deterministic SNNs.
Additionally, we demonstrate the utility of the NSNN model for neural coding
studies. Overall, NSNN offers a powerful, flexible, and easy-to-use tool for
machine learning practitioners and computational neuroscience researchers.Comment: Fixed the bug in the BBL file generated with bibliography management
progra
Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
Developing computational models of neural response is crucial for
understanding sensory processing and neural computations. Current
state-of-the-art neural network methods use temporal filters to handle temporal
dependencies, resulting in an unrealistic and inflexible processing flow.
Meanwhile, these methods target trial-averaged firing rates and fail to capture
important features in spike trains. This work presents the temporal
conditioning spiking latent variable models (TeCoS-LVM) to simulate the neural
response to natural visual stimuli. We use spiking neurons to produce spike
outputs that directly match the recorded trains. This approach helps to avoid
losing information embedded in the original spike trains. We exclude the
temporal dimension from the model parameter space and introduce a temporal
conditioning operation to allow the model to adaptively explore and exploit
temporal dependencies in stimuli sequences in a natural paradigm. We show that
TeCoS-LVM models can produce more realistic spike activities and accurately fit
spike statistics than powerful alternatives. Additionally, learned TeCoS-LVM
models can generalize well to longer time scales. Overall, while remaining
computationally tractable, our model effectively captures key features of
neural coding systems. It thus provides a useful tool for building accurate
predictive computational accounts for various sensory perception circuits.Comment: spiking neural networks, neural coding, visual coding, latent
variable models, variational information bottleneck, noisy spiking neural
network
Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection
While advances in artificial intelligence and neuroscience have enabled the
emergence of neural networks capable of learning a wide variety of tasks, our
understanding of the temporal dynamics of these networks remains limited. Here,
we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF)
neural networks in tasks of continual familiarity detection. Drawing
inspiration from the field of network neuroscience, we examine the network's
dynamic reconfiguration, focusing on how network modules evolve throughout
learning. Through a comprehensive assessment involving metrics like network
accuracy, modular flexibility, and distribution entropy across diverse learning
modes, our approach reveals various previously unknown patterns of network
reconfiguration. In particular, we find that the emergence of network
modularity is a salient predictor of performance, and that modularization
strengthens with increasing flexibility throughout learning. These insights not
only elucidate the nuanced interplay of network modularity, accuracy, and
learning dynamics but also bridge our understanding of learning in artificial
and biological realms
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