TY - JOUR
T1 - Engineering calcium signaling of astrocytes for neural–molecular computing logic gates
AU - Barros, Michael Taynnan
AU - Doan, Phuong
AU - Kandhavelu, Meenakshisundaram
AU - Jennings, Brendan
AU - Balasubramaniam, Sasitharan
N1 - Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - This paper proposes the use of astrocytes to realize Boolean logic gates, through manipulation of the threshold of Ca 2 + ion flows between the cells based on the input signals. Through wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17 genes as well as chemical compounds, we show that both AND and OR gates can be implemented by controlling Ca 2 + signals that flow through the population. A reinforced learning platform is also presented in the paper to optimize the Ca 2 + activated level and time slot of input signals Tb into the gate. This design platform caters for any size and connectivity of the cell population, by taking into consideration the delay and noise produced from the signalling between the cells. To validate the effectiveness of the reinforced learning platform, a Ca 2 + signalling simulator was used to simulate the signalling between the astrocyte cells. The results from the simulation show that an optimum value for both the Ca 2 + activated level and time slot of input signals Tb is required to achieve up to 90% accuracy for both the AND and OR gates. Our method can be used as the basis for future Neural–Molecular Computing chips, constructed from engineered astrocyte cells, which can form the basis for a new generation of brain implants.
AB - This paper proposes the use of astrocytes to realize Boolean logic gates, through manipulation of the threshold of Ca 2 + ion flows between the cells based on the input signals. Through wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17 genes as well as chemical compounds, we show that both AND and OR gates can be implemented by controlling Ca 2 + signals that flow through the population. A reinforced learning platform is also presented in the paper to optimize the Ca 2 + activated level and time slot of input signals Tb into the gate. This design platform caters for any size and connectivity of the cell population, by taking into consideration the delay and noise produced from the signalling between the cells. To validate the effectiveness of the reinforced learning platform, a Ca 2 + signalling simulator was used to simulate the signalling between the astrocyte cells. The results from the simulation show that an optimum value for both the Ca 2 + activated level and time slot of input signals Tb is required to achieve up to 90% accuracy for both the AND and OR gates. Our method can be used as the basis for future Neural–Molecular Computing chips, constructed from engineered astrocyte cells, which can form the basis for a new generation of brain implants.
UR - http://www.scopus.com/inward/record.url?scp=85099217795&partnerID=8YFLogxK
U2 - 10.1038/s41598-020-79891-x
DO - 10.1038/s41598-020-79891-x
M3 - Article
C2 - 33436729
AN - SCOPUS:85099217795
SN - 2045-2322
VL - 11
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 595
ER -