3 research outputs found
Synaptic Learning with Augmented Spikes
Traditional neuron models use analog values for information representation
and computation, while all-or-nothing spikes are employed in the spiking ones.
With a more brain-like processing paradigm, spiking neurons are more promising
for improvements on efficiency and computational capability. They extend the
computation of traditional neurons with an additional dimension of time carried
by all-or-nothing spikes. Could one benefit from both the accuracy of analog
values and the time-processing capability of spikes? In this paper, we
introduce a concept of augmented spikes to carry complementary information with
spike coefficients in addition to spike latencies. New augmented spiking neuron
model and synaptic learning rules are proposed to process and learn patterns of
augmented spikes. We provide systematic insight into the properties and
characteristics of our methods, including classification of augmented spike
patterns, learning capacity, construction of causality, feature detection,
robustness and applicability to practical tasks such as acoustic and visual
pattern recognition. The remarkable results highlight the effectiveness and
potential merits of our methods. Importantly, our augmented approaches are
versatile and can be easily generalized to other spike-based systems,
contributing to a potential development for them including neuromorphic
computing.Comment: 13 page
Neural Population Coding for Effective Temporal Classification
Neural encoding plays an important role in faithfully describing the
temporally rich patterns, whose instances include human speech and
environmental sounds. For tasks that involve classifying such spatio-temporal
patterns with the Spiking Neural Networks (SNNs), how these patterns are
encoded directly influence the difficulty of the task. In this paper, we
compare several existing temporal and population coding schemes and evaluate
them on both speech (TIDIGITS) and sound (RWCP) datasets. We show that, with
population neural codings, the encoded patterns are linearly separable using
the Support Vector Machine (SVM). We note that the population neural codings
effectively project the temporal information onto the spatial domain, thus
improving linear separability in the spatial dimension, achieving an accuracy
of 95\% and 100\% for TIDIGITS and RWCP datasets classified using the SVM,
respectively. This observation suggests that an effective neural coding scheme
greatly simplifies the classification problem such that a simple linear
classifier would suffice. The above datasets are then classified using the
Tempotron, an SNN-based classifier. SNN classification results agree with the
SVM findings that population neural codings help to improve classification
accuracy. Hence, other than the learning algorithm, effective neural encoding
is just as important as an SNN designed to recognize spatio-temporal patterns.
It is an often neglected but powerful abstraction that deserves further study
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