Nonextensive realizations in interacting ion channels: Implications for mechano-electrical transducer mechanisms.

We propose a theoretical model to investigate the thermodynamics of single and coupled two-state ion channels, associated with mechanoelectrical transduction (MET) and hair cell biophysics. The modeling was based on the Tsallis nonextensive statistical mechanics. The choice for a nonextensive framework in modeling ion channels is encouraged on the fact that we take into account the presence of interactions or long-range correlations in the dynamics of single and coupled ion channels. However, the basic assumptions that support Boltzmann-Gibbs statistics, traditionally used to model ion channel dynamics, state that the system is formed by independent or weakly interacting elements. Despite being well studied in many biological systems, the literature has not yet addressed the study of both entropy and mutual information related to isolated or physically interacting pairs of MET channels. Inspired by hair cell biophysics, we show how the presence of nonextensivity, or subadditivity and superadditivity modulates the nonextensive entropy and mutual information as functions of stereocilia displacements. We also observe that the magnitude of the interaction between the two channels, given by a nonextensive parameter, influences the amplitude of the nonextensive joint entropy and mutual information as functions of the hair cell displacements. Finally, we show how nonextensivity regulates the current versus displacement curve for a single and a pair of interacting two-state channels. The present findings shed light on the thermodynamic process involved in the molecular mechanisms of the auditory system.

Correction to: Statistical crossover and nonextensive behavior of neuronal short-term depression.

The authors apologize for the following errors published in the article. However, these errors do not modify the main assumptions in our work nor affects the discussion (interpretation) of the results.

Statistical crossover and nonextensive behavior of neuronal short-term depression.

The theoretical basis of neuronal coding, associated with short-term degradation in synaptic transmission, is a matter of debate in the literature. In fact, electrophysiological signals are commonly characterized as inversely proportional to stimulus intensity. Among theoretical descriptions of this phenomenon, models based on 1/f-dependency are employed to investigate the biophysical properties of short-term synaptic depression. In this work, we formulate a model based on a paradigmatic q-differential equation to obtain a generalized formalism useful for investigation of nonextensivity in this specific type of synaptic plasticity. Our analysis reveals nonextensivity in data from electrophysiological recordings and also a statistical crossover in neurotransmission. In particular, statistical transitions provide additional support to the hypothesis of heterogeneous release probability of neurotransmitters. On the other hand, the simple vesicle model agrees with data only at low-frequency stimulations. Thus, the present work presents a method to demonstrate that short-term depression is not only governed by random mechanisms but also by nonextensive behavior. Our findings also conciliate morphological and electrophysiological investigations into a coherent biophysical scenario.

Maximum-likelihood q-estimator uncovers the role of potassium at neuromuscular junctions.

Recently, we demonstrated the existence of nonextensive behavior in neuromuscular transmission (da Silva et al. in Phys Rev E 84:041925, 2011). In this letter, we first obtain a maximum-likelihood q-estimator to calculate the scale factor ([Formula: see text]) and the q-index of q-Gaussian distributions. Next, we use the indexes to analyze spontaneous miniature end plate potentials in electrophysiological recordings from neuromuscular junctions. These calculations were performed assuming both normal and high extracellular potassium concentrations [Formula: see text]. This protocol was used to test the validity of Tsallis statistics under electrophysiological conditions closely resembling physiological stimuli. The analysis shows that q-indexes are distinct depending on the extracellular potassium concentration. Our letter provides a general way to obtain the best estimate of parameters from a q-Gaussian distribution function. It also expands the validity of Tsallis statistics in realistic physiological stimulus conditions. In addition, we discuss the physical and physiological implications of these findings.

Firing patterns and synchronization in nonsynaptic epileptiform activity: the effect of gap junctions modulated by potassium accumulation.

Several lines of evidence point to the modification of firing patterns and of synchronization due to gap junctions (GJs) as having a role in the establishment of epileptiform activity (EA). However, previous studies consider GJs as ohmic resistors, ignoring the effects of intense variations in ionic concentration known to occur during seizures. In addition to GJs, extracellular potassium is regarded as a further important factor involved in seizure initiation and sustainment. To analyze how these two mechanisms act together to shape firing and synchronization, we use a detailed computational model for in vitro high-K(+) and low-Ca(2+) nonsynaptic EA. The model permits us to explore the modulation of electrotonic interactions under ionic concentration changes caused by electrodiffusion in the extracellular space, altered by tortuosity. In addition, we investigate the special case of null GJ current. Increased electrotonic interaction alters bursts and action potential frequencies, favoring synchronization. The particularities of pattern changes depend on the tortuosity and array size. Extracellular potassium accumulation alone modifies firing and synchronization when the GJ coupling is null.