[2H3-J-2] Machine learning: selective preprocess

[2H3-J-2-01] Surround Inhibition Mechanism by Deep Learning

〇Mamoru Sugamoto¹, Naoaki Fujimoto², Muneharu Onoue³, Tsukasa Yumibayashi⁴, Akio Sugamoto⁵ (1. apprhythm inc., 2. Tama Art U., 3. Geelive, Inc, 4. Otsuma Women’s U., 5. Ochanomizu U. and OUJ)

Keywords:Surround Inhibition Mechanism

In the sensation of tones, visions and other stimuli, the “surround inhibition mechanism” (or “lateral inhibition mechanism”) is crucial. The mechanism enhances the signals of the strongest tone, color and other stimuli, by reducing and inhibiting the surrounding signals, since the latter signals are less important. This surround inhibition mechanism is well studied in the physiology of sensor systems. The neural network with two hidden layers in addition to input and output layers is constructed; having 60 neurons (units) in each of the four layers. The label (correct answer) is prepared from an input signal by applying seven times operations of the “Hartline mechanism”, that is, by sending inhibitory signals from the neighboring neurons and amplifying all the signals afterwards. The implication obtained by the deep learning of this neural network is compared with the standard physiological understanding of the surround inhibition mechanism.