Biologically plausible learning in neural networks: a lesson from bacterial chemotaxis

Learning processes in the brain are usually associated with plastic changes made to optimize the strength of connections between neurons. Although many details related to biophysical mechanisms of synaptic plasticity have been discovered, it is unclear how the concurrent performance of adaptive modifications in a huge number of spatial locations is organized to minimize a given objective function. Since direct experimental observation of even a relatively small subset of such changes is not feasible, computational modeling is an indispensable investigation tool for solving this problem. However, the conventional method of error back-propagation (EBP) employed for optimizing synaptic weights in artificial neural networks is not biologically plausible. This study based on computational experiments demonstrated that such optimization can be performed rather efficiently using the same general method that bacteria employ for moving closer to an attractant or away from a repellent. With regard to neural network optimization, this method consists of regulating the probability of an abrupt change in the direction of synaptic weight modification according to the temporal gradient of the objective function. Neural networks utilizing this method (regulation of modification probability, RMP) can be viewed as analogous to swimming in the multidimensional space of their parameters in the flow of biochemical agents carrying information about the optimality criterion. The efficiency of RMP is comparable to that of EBP, while RMP has several important advantages. Since the biological plausibility of RMP is beyond a reasonable doubt, the RMP concept provides a constructive framework for the experimental analysis of learning in natural neural networks.     

Adaptive force produced by stress-induced regulation of random variation intensity

The Darwinian theory of life evolution is capable of explaining the majority of related phenomena. At the same time, the mechanisms of optimizing traits beneficial to a population as a whole but not directly to an individual remain largely unclear. There are also significant problems with explaining the phenomenon of punctuated equilibrium. From another perspective, multiple mechanisms for the regulation of the rate of genetic mutations according to the environmental stress have been discovered, but their precise functional role is not well understood yet. Here a novel mathematical paradigm called a Kinetic-Force Principle (KFP), which can serve as a general basis for biologically plausible optimization methods, is introduced and its rigorous derivation is provided. Based on this principle, it is shown that, if the rate of random changes in a biological system is proportional, even only roughly, to the amount of environmental stress, a virtual force is created, acting in the direction of stress relief. It is demonstrated that KFP can provide important insights into solving the above problems. Evidence is presented in support of a hypothesis that the nature employs KFP for accelerating adaptation in biological systems. A detailed comparison between KFP and the principle of variation and natural selection is presented and their complementarity is revealed. It is concluded that KFP is not a competing alternative, but a powerful addition to the principle of variation and natural selection. It is also shown KFP can be used in multiple ways for adaptation of individual biological organisms.     

Fast Three-Dimensional Single-Particle Tracking in Natural Brain Tissue

Observation of molecular dynamics is often biased by the optical very heterogeneous environment of cells and complex tissue. Here, we have designed an algorithm that facilitates molecular dynamic analyses within brain slices. We adjust fast astigmatism-based three-dimensional single-particle tracking techniques to depth-dependent optical aberrations induced by the refractive index mismatch so that they are applicable to complex samples. In contrast to existing techniques, our online calibration method determines the aberration directly from the acquired two-dimensional image stream by exploiting the inherent particle movement and the redundancy introduced by the astigmatism. The method improves the positioning by reducing the systematic errors introduced by the aberrations, and allows correct derivation of the cellular morphology and molecular diffusion parameters in three dimensions independently of the imaging depth. No additional experimental effort for the user is required. Our method will be useful for many imaging configurations, which allow imaging in deep cellular structures.     

Impact of weak water deficit on growth,photosynthetic primary processes and storage processes in pine and spruce seedlings

Photosynthesis Research - We investigated the influence of 40 days of drought on growth, storage processes and primary photosynthetic processes in 3-month-old Scots pine and Norway spruce seedlings...     

Coupled molecular dynamics and continuum electrostatic method to compute the ionization pKa’s of proteins as a function of pH. Test on a large set of proteins

A computational method, to predict the pKa values of the ionizable residues Asp, Glu, His, Tyr, and Lys of proteins, is presented here. Calculation of the electrostatic free-energy of the proteins is based on an efficient version of a continuum dielectric electrostatic model. The conformational flexibility of the protein is taken into account by carrying out molecular dynamics simulations of 10 ns in implicit water. The accuracy of the proposed method of calculation of pKa values is estimated from a test set of experimental pKa data for 297 ionizable residues from 34 proteins. The pKa-prediction test shows that, on average, 57, 86, and 95% of all predictions have an error lower than 0.5, 1.0, and 1.5 pKa units, respectively. This work contributes to our general understanding of the importance of protein flexibility for an accurate computation of pKa, providing critical insight about the significance of the multiple neutral states of acid and histidine residues for pKa-prediction, and may spur significant progress in our effort to develop a fast and accurate electrostatic-based method for pKa-predictions of proteins as a function of pH.     

The ER protein Ema19 facilitates the degradation of nonimported mitochondrial precursor proteins

For the biogenesis of mitochondria, hundreds of proteins need to be targeted from the cytosol into the various compartments of this organelle. The intramitochondrial targeting routes these proteins take to reach their respective location in the organelle are well understood. However, the early targeting processes, from cytosolic ribosomes to the membrane of the organelle, are still largely unknown. In this study, we present evidence that an integral membrane protein of the endoplasmic reticulum (ER), Ema19, plays a role in this process. Mutants lacking Ema19 show an increased stability of mitochondrial precursor proteins, indicating that Ema19 promotes the proteolytic degradation of nonproductive precursors. The deletion of Ema19 improves the growth of respiration-deficient cells, suggesting that Ema19-mediated degradation can compete with productive protein import into mitochondria. Ema19 is the yeast representative of a conserved protein family. The human Ema19 homologue is known as sigma 2 receptor or TMEM97. Though its molecular function is not known, previous studies suggested a role of the sigma 2 receptor as a quality control factor in the ER, compatible with our observations about Ema19. More globally, our data provide an additional demonstration of the important role of the ER in mitochondrial protein targeting.