Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Biologically Inspired Oscillating Activation Functions Can Bridge the
  Performance Gap between Biological and Artificial Neurons   Nonlinear activation functions endow neural networks with the ability to
learn complex high-dimensional functions. The choice of activation function is
a crucial hyperparameter that determines the performance of deep neural
networks. It significantly affects the gradient flow, speed of training and
ultimately the representation power of the neural network. Saturating
activation functions like sigmoids suffer from the vanishing gradient problem
and cannot be used in deep neural networks. Universal approximation theorems
guarantee that multilayer networks of sigmoids and ReLU can learn arbitrarily
complex continuous functions to any accuracy. Despite the ability of multilayer
neural networks to learn arbitrarily complex activation functions, each neuron
in a conventional neural network (networks using sigmoids and ReLU like
activations) has a single hyperplane as its decision boundary and hence makes a
linear classification. Thus single neurons with sigmoidal, ReLU, Swish, and
Mish activation functions cannot learn the XOR function. Recent research has
discovered biological neurons in layers two and three of the human cortex
having oscillating activation functions and capable of individually learning
the XOR function. The presence of oscillating activation functions in
biological neural neurons might partially explain the performance gap between
biological and artificial neural networks. This paper proposes 4 new
oscillating activation functions which enable individual neurons to learn the
XOR function without manual feature engineering. The paper explores the
possibility of using oscillating activation functions to solve classification
problems with fewer neurons and reduce training time.

37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int