Analyzing the code structures of multidimensional signals for a continuous information transmission channel

One of the directions to improve the efficiency of modern telecommunication systems is the transition to the use of multidimensional signals for continuous channels of information transmission. As a result of studies carried out in recent years, it has been established that it is possible to ensure high quality of information transmission in continuous channels by combining demodulation and decoding operations into a single procedure that involves the construction of a code construct for a multidimensional signal. This paper considers issues related to estimating the possibility to improve the efficiency of continuous information transmission channel by changing the signal distance of the code structure. It has been established that the code structures of such types as a hierarchical code construct of signals, a hierarchical code construct of signals with Euclidean metric, a reversible code construct of signals, a reversible code construct of signals with Euclidean metric have the potential, when used, to increase the speed of information transmission along a continuous channel. With a signal distance reduced by 10 percent or larger, it could increase by two times or faster. The estimation of the effect of reducing a signal distance on the efficiency of certain types of code structures was carried out. It has been established that the hierarchical reversible code construct, compared to the hierarchical code construct, provides a win of up to two or more times in the speed of information transmission with a halved signal distance. Implementing the modulation procedure has no fundamental difficulties, on the condition that for each code of the code construct the encoding procedure is known when using binary codes. The results reported here make it possible to build an acceptably complex demodulation procedure according to the specified types of code structures

Computational design of orthogonal membrane receptor-effector switches for rewiring signaling pathways

Membrane receptors regulate numerous intracellular functions. However, the molecular underpinnings remain poorly understood because most receptors initiate multiple signaling pathways through distinct interaction interfaces that are structurally uncharacterized. We present an integrated computational and experimental approach to model and rationally engineer membrane receptor-intracellular protein systems signaling with novel pathway selectivity. We targeted the dopamine D2 receptor (D2), a G-protein–coupled receptor (GPCR), which primarily signals through Gi, but triggers also the Gq and beta-arrestin pathways. Using this approach, we designed orthogonal D2–Gi complexes, which coupled with high specificity and triggered exclusively the Gi-dependent signaling pathway. We also engineered an orthogonal chimeric D2–Gs/i complex that rewired D2 signaling from a Gi-mediated inhibitory into a Gs-dependent activating pathway. Reinterpreting the evolutionary history of GPCRs in light of the designed proteins, we uncovered an unforeseen hierarchical code of GPCR–G-protein coupling selectivity determinants. The results demonstrate that membrane receptor–cytosolic protein systems can be rationally engineered to regulate mammalian cellular functions. The method should prove useful for creating orthogonal molecular switches that redirect signals at the cell surface for cell-engineering applications.

A principle of economy predicts the functional architecture of grid cells

Grid cells in the brain respond when an animal occupies a periodic lattice of ‘grid fields’ during navigation. Grids are organized in modules with different periodicity. We propose that the grid system implements a hierarchical code for space that economizes the number of neurons required to encode location with a given resolution across a range equal to the largest period. This theory predicts that (i) grid fields should lie on a triangular lattice, (ii) grid scales should follow a geometric progression, (iii) the ratio between adjacent grid scales should be √e for idealized neurons, and lie between 1.4 and 1.7 for realistic neurons, (iv) the scale ratio should vary modestly within and between animals. These results explain the measured grid structure in rodents. We also predict optimal organization in one and three dimensions, the number of modules, and, with added assumptions, the ratio between grid periods and field widths.

BP Network Optimization Based on Improved Genetic Algorithm

In view of the virtue and shortage of genetic algorithm and BP network, this paper proposes a new BP network training method based on improved genetic algorithm (IGA-BP). This algorithm uses hierarchical code, adaptive crossover and mutation, pruning similar chromosomes, dynamic supply new chromosomes and other operations, so the network structure and weight are optimized at the same time and the "premature" phenomenon is avoided. The simulation results show that the IGA-BP network architecture is simple, the convergence rate is quick, and has good approximation and generalization ability.