2 research outputs found
Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning
Spikes are the currency in central nervous systems for information
transmission and processing. They are also believed to play an essential role
in low-power consumption of the biological systems, whose efficiency attracts
increasing attentions to the field of neuromorphic computing. However,
efficient processing and learning of discrete spikes still remains as a
challenging problem. In this paper, we make our contributions towards this
direction. A simplified spiking neuron model is firstly introduced with effects
of both synaptic input and firing output on membrane potential being modeled
with an impulse function. An event-driven scheme is then presented to further
improve the processing efficiency. Based on the neuron model, we propose two
new multi-spike learning rules which demonstrate better performance over other
baselines on various tasks including association, classification, feature
detection. In addition to efficiency, our learning rules demonstrate a high
robustness against strong noise of different types. They can also be
generalized to different spike coding schemes for the classification task, and
notably single neuron is capable of solving multi-category classifications with
our learning rules. In the feature detection task, we re-examine the ability of
unsupervised STDP with its limitations being presented, and find a new
phenomenon of losing selectivity. In contrast, our proposed learning rules can
reliably solve the task over a wide range of conditions without specific
constraints being applied. Moreover, our rules can not only detect features but
also discriminate them. The improved performance of our methods would
contribute to neuromorphic computing as a preferable choice.Comment: 13 page
Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning
The capability for environmental sound recognition (ESR) can determine the
fitness of individuals in a way to avoid dangers or pursue opportunities when
critical sound events occur. It still remains mysterious about the fundamental
principles of biological systems that result in such a remarkable ability.
Additionally, the practical importance of ESR has attracted an increasing
amount of research attention, but the chaotic and non-stationary difficulties
continue to make it a challenging task. In this study, we propose a spike-based
framework from a more brain-like perspective for the ESR task. Our framework is
a unifying system with a consistent integration of three major functional parts
which are sparse encoding, efficient learning and robust readout. We first
introduce a simple sparse encoding where key-points are used for feature
representation, and demonstrate its generalization to both spike and non-spike
based systems. Then, we evaluate the learning properties of different learning
rules in details with our contributions being added for improvements. Our
results highlight the advantages of the multi-spike learning, providing a
selection reference for various spike-based developments. Finally, we combine
the multi-spike readout with the other parts to form a system for ESR.
Experimental results show that our framework performs the best as compared to
other baseline approaches. In addition, we show that our spike-based framework
has several advantageous characteristics including early decision making, small
dataset acquiring and ongoing dynamic processing. Our framework is the first
attempt to apply the multi-spike characteristic of nervous neurons to ESR. The
outstanding performance of our approach would potentially contribute to draw
more research efforts to push the boundaries of spike-based paradigm to a new
horizon.Comment: 13 pages,12 figure