Title :
Ensembles of membrane proteins as statistical mixed-signal computers
Author :
Eliashberg, Victor
Author_Institution :
Dept. of Electr. Eng., Stanford Univ., CA, USA
fDate :
July 31 2005-Aug. 4 2005
Abstract :
The paper presents a formalism that connects functional properties of neurons with the properties of membrane proteins treated as abstract probabilistic machines. The machines are referred to as probabilistic molecular machines (PMM). It is shown that ensembles of PMMs (EPMM) provide robust statistical implementation of mixed-signal computers combining the dynamical capabilities of analog computers with the sequencing capabilities of state machines. The classical Hodgkin and Huxley model is reformulated in terms of two EPMMs and is used as a detailed example illustrating the structure and the representational possibilities of the PMM/EPMM formalism.
Keywords :
analogue computers; biocomputing; differential equations; finite state machines; proteins; abstract probabilistic machines; analog computers; membrane proteins; probabilistic molecular machines; state machines; statistical mixed-signal computers; Analog computers; Biomembranes; Brain modeling; Differential equations; Information processing; Kinetic theory; Mathematical model; Neural networks; Protein engineering; Robustness;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556237
