Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linear Constraints Learning for Spiking Neurons   We introduce a new supervised learning algorithm based to train spiking
neural networks for classification. The algorithm overcomes a limitation of
existing multi-spike learning methods: it solves the problem of interference
between interacting output spikes during a learning trial. This problem of
learning interference causes learning performance in existing approaches to
decrease as the number of output spikes increases, and represents an important
limitation in existing multi-spike learning approaches. We address learning
interference by introducing a novel mechanism to balance the magnitudes of
weight adjustments during learning, which in theory allows every spike to
simultaneously converge to their desired timings. Our results indicate that our
method achieves significantly higher memory capacity and faster convergence
compared to existing approaches for multi-spike classification. In the
ubiquitous Iris and MNIST datasets, our algorithm achieves competitive
predictive performance with state-of-the-art approaches.

36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an