The pulsatory liposome works as a neuronal network (I)

In this paper we consider a unilamellar liposome (lipid vesicle) filled with aqueous solution of osmotic solute. This liposome is introduced in a hypotonic aqueous medium of large dimensions. Due to the osmosis process the liposome swells to a critical size, when a trans bilayer pore suddenly appears. Some of the internal solution leaks through this pore and the liposome relaxes and returns to its initial size. The swelling starts again and the liposome begins a new cycle and so on. The evolution of the liposome is a dynamic and cyclical process. The swelling of the liposome is described by a differential equation. The appearance of the pore changes the evolution of the liposome. The internal solution comes out through the pore and the liposome starts its deflation (relaxation) and reaches to its initial size. All the processes which contribute to the liposome relaxing and its coming back to the initial size (pore evolution and internal solution delivery) are described by three differential equations. This system of differential equations describes the evolution of the pulsatory liposome during a cycle and can be integrated using numerical methods. After performing a number of cycles, the pulsatory liposome stops. It can be assimilated to an intelligent biophysical engine and a neural network. A neural layer corresponds to a cycle. A neural layer corresponds to a cycle. Each neuronal layer learns to solve the equations that describe the evolution of the liposome during a cycle. So, a pulsed liposome works according to its own program that can be learned by a neural network.