Entropy production of ion thermo-diffusion in cell membranes

An Integrative View on Entropic Measures Applied to the Nervous System

“…Following this line of thought, other authors have studied the upper limit of the information content of a single spike using Landauer's principle, the results indicating that an action potential can process more information than a single bit of the Shannon entropy [3]. Entropy has been applied as well to the molecular world, e.g., a mathematical model for ion diffusion in ion channels studied aspects of ion diffusion contributing to entropy production [4]. Synapses have not escaped either to the application of entropic metrics, for instance addressing the balance between excitatory and inhibitory synapses needed to ensure appropriate function of neural networks, study that demonstrated highest entropy at the boundary between excitation-dominant and inhibition-dominant regimes [5]; as well, correlation entropy has been applied to synaptic input-output dynamics showing that cortical synapses exhibit low-dimensional chaos driven by natural input patterns [6].…”

An alternative View on Entropic Measures Applied to the Nervous System

“…Following this line of thought, other authors have studied the upper limit of the information content of a single spike using Landauer's principle, the results indicating that an action potential can process more information than a single bit of the Shannon entropy [8]. Entropy has been applied as well to the molecular world, e.g., a mathematical model for ion diffusion in ion channels studied aspects of ion diffusion contributing to entropy production [9]. Synapses have not escaped either to the application of entropic metrics, for instance addressing the balance between excitatory and inhibitory synapses needed to ensure appropriate function of neural networks, study that demonstrated highest entropy at the boundary between excitation-dominant and inhibition-dominant regimes [10]; as well, correlation entropy has been applied to synaptic input-output dynamics showing that cortical synapses exhibit low-dimensional chaos driven by natural input patterns [11].…”

Generalized Lorentz reciprocal theorem in complex fluids and in non-isothermal systems

“…The LRT can be derived [5] from the fundamental thermodynamic reciprocal relations formulated by L. Onsager in 1931 [6,7] for linear irreversible processes. Onsager's reciprocal relations (ORR) are valid for the linear response of fluxes to forces in the vicinity of thermodynamic equilibrium [8][9][10][11][12][13][14][15][16][17]. These relations assert that due to the microscopic time-reversal symmetry, the coefficient matrix that couples the forces and fluxes must be symmetric.…”

“…In the present work, we generalize the LRT from simple fluids to complex fluids in which the hydrodynamic flow is coupled with the evolution of internal degrees of freedom [9][10][11][12][13][14]. We derive the generalized Lorentz reciprocal theorem (GLRT) for two typical complex fluids: two-phase binary fluids [22,23] and micropolar fluids [10,11], in which the solute concentration and spin act as internal degrees of freedom, respectively.…”

“…Onsager's reciprocal relations (ORR) has been proved to be valid for the linear response of fluxes to forces in the vicinity of thermodynamic equilibrium in various phenomena with cross coupling [8], e.g. electro-hydrodynamic coupling in a capillary containing an electrolyte solution [9], spin-flow coupling in micropolar fluids [10,11], cross coupling between surfactant composition and film thickness in thin films of surfactant suspensions [12], ion thermo-diffusion in cell membranes [13], molecular orientation-flow coupling in liquid crystals [14], rotation-translation and thermal-mechanical couplings in colloidal suspensions [15][16][17]. The reciprocal relations assert that due to the microscopic time-reversal symmetry, the coefficient matrix that couples the forces and fluxes must be symmetric.…”