In recent years, artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence. Neuron in ANNs is designed by the Knowledge about the biological neurons in the brain 70 years ago.Neuron in ANNs is expressed as f(wx+b) or f(WX). The design of this architecture does not consider the information processing capabilities of dendrites. However, recently, studies shows that dendrites participate in the pre-calculation of input data in the brain. Concretely, biological dendrites play a role in the pre-processing for the interaction information of input data. Therefore, it may be time to perfect the neuron of ANNs. According to our previous studies (Gang transform), this paper adds the dendrite processing section to neurons of ANNs. The dendrite processing section can be expressed as Wi,i-1Ai-1 ○ A0|1|2|...|i-1 . The generalized new neuron can be expressed as f(W(Wi,i-1Ai-1 ○ A0|1|2|...|i-1)) .The simplified new neuron be expressed as f(∑(WA ○ X)) . After perfecting the neuron, there are so many networks to try. This paper shows some basic architecture for reference in the future.
Interesting things: (1) The computational complexity of dendrite modules (Wi,i-1Ai-1 ○ Ai-1) after being connected in series is far lower than Horner's method. Will this speed up the calculation of basic functions in computers? (2) The range of sight of animals has a gradient, but the convolution layer does not have this characteristic. This paper proposes receptive fields with gradient. (3) The networks using Gang neurons can delete the full connectional layer of traditional networks. In other words, the parameters of the full connectional layers are assigned to a single neuron, which reduces parameters of a network for the same mapping capacity.
ORCID of Submitting Authorhttps://orcid.org/0000-0002-7379-1988
Submitting Author's InstitutionXi'an Jiaotong University
Submitting Author's CountryChina