TY - JOUR
T1 - Biochemical neuron
T2 - hardware implementation of functional devices by mimicking the natural intelligence such as metabolic control systems
AU - Okamoto, Masahiro
AU - Sekiguchi, Tatsuya
AU - Tanaka, Kouji
AU - Maki, Yukihiro
AU - Yoshida, Satoshi
N1 - Funding Information:
This study was performed through the Scientific Funds of the Frontier Research in Telecommunications and of the Basic Research 21 for Breakthroughs in Info-Communications of the Ministry of Posts and Telecommunications, Japan and through the Grant-in-Aid for the Scientific Research on Priority Areas, “Ultimate Integration of Intelligence on Silicon Electronic Systems” (No. 09224213) of the Ministry of Education, Science, Sports and Culture, Japan.
PY - 1999/9
Y1 - 1999/9
N2 - Mimicking the switching property of cyclic enzyme systems in metabolic pathways, we have proposed a different type of molecular switching device of which mechanism can be represented by threshold-logic function capable of storing short-term memory or 'Hebbian rule' or 'post-synaptic neuron' with synaptic history. The great advantage of this device is that it can process the time-variant input signals. We have named this system a 'biochemical neuron' and have developed the board-leveled analog circuit. In the present study, building the integrated artificial neural network system being composed of biochemical neurons, we have examined whether this network can recognize the pattern similarity in time-variant external analog signals or not. Our neural network showed highly recognition of total time-variant patterns of external analog signals even if signals involve an uniformed random noise.
AB - Mimicking the switching property of cyclic enzyme systems in metabolic pathways, we have proposed a different type of molecular switching device of which mechanism can be represented by threshold-logic function capable of storing short-term memory or 'Hebbian rule' or 'post-synaptic neuron' with synaptic history. The great advantage of this device is that it can process the time-variant input signals. We have named this system a 'biochemical neuron' and have developed the board-leveled analog circuit. In the present study, building the integrated artificial neural network system being composed of biochemical neurons, we have examined whether this network can recognize the pattern similarity in time-variant external analog signals or not. Our neural network showed highly recognition of total time-variant patterns of external analog signals even if signals involve an uniformed random noise.
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U2 - 10.1016/S0045-7906(99)00007-5
DO - 10.1016/S0045-7906(99)00007-5
M3 - Article
AN - SCOPUS:0033189133
SN - 0045-7906
VL - 25
SP - 421
EP - 438
JO - Computers and Electrical Engineering
JF - Computers and Electrical Engineering
IS - 5
ER -