Optimizing the depth and the direction of prospective planning using information values

Evaluating the future consequences of actions is achievable by simulating a mental search tree into the future. Expanding deep trees, however, is computationally taxing. Therefore, machines and humans use a plan-until-habit scheme that simulates the environment up to a limited depth and then exploits habitual values as proxies for consequences that may arise in the future. Two outstanding questions in this scheme are “in which directions the search tree should be expanded?”, and “when should the expansion stop?”. Here we propose a principled solution to these questions based on a speed/accuracy tradeoff: deeper expansion in the appropriate directions leads to more accurate planning, but at the cost of slower decision-making. Our simulation results show how this algorithm expands the search tree effectively and efficiently in a grid-world environment. We further show that our algorithm can explain several behavioral patterns in animals and humans, namely the effect of time-pressure on the depth of planning, the effect of reward magnitudes on the direction of planning, and the gradual shift from goal-directed to habitual behavior over the course of training. The algorithm also provides several predictions testable in animal/human experiments

Evaluation of cotton (Gossypium spp.) Germplasm for heat tolerance under normal and late planting time

The objective of this study was to determine cotton (Gossypium ssp.) germplasm for heat tolerance under normal and late planting time. For this aiming 200 cotton genotypes and five check varieties (Gloria, SG 125, Flash, Ozbek 105 and Candia) were evaluated under two different temperature regimes and experiments were conducted according to the augmented design with four blocks. Field studies were carried out at the GAP International Agricultural Research and Training Center’s experimental area in Diyarbakır, Turkey, in 2016 cotton growing season. In the study heat susceptibility index was used for discriminate to the genotypes for heat tolerance. Genotypes were classified into four groups based on the heat susceptibility index. The results of this study indicated that five cotton genotypes (TAM 139-17 ELS, CIM-240, Haridost, MNH-990 and AzGR-11835) were in highly heat tolerant, 28 genotypes were found heat tolerant, 56 genotypes were in the moderately heat tolerant and other 120 genotypes were observed susceptible for heat tolerance. Based on the heat susceptibility index, five cotton genotypes can be used as parent for heat tolerance improvement in the cotton breeding program where high temperature is a limiting factor for seed cotton yield

Algorithms for obtaining parsimonious higher order neurons

Due to copyright restrictions, the access to the full text of this article is only available via subscription.Most neurons in the central nervous system exhibit all-or-none firing behavior. This makes Boolean Functions (BFs) tractable candidates for representing computations performed by neurons, especially at finer time scales, even though BFs may fail to capture some of the richness of neuronal computations such as temporal dynamics. One biologically plausible way to realize BFs is to compute a weighted sum of products of inputs and pass it through a heaviside step function. This representation is called a Higher Order Neuron (HON). A HON can trivially represent any n-variable BF with 2n product terms. There have been several algorithms proposed for obtaining representations with fewer product terms. In this work, we propose improvements over previous algorithms for obtaining parsimonious HON representations and present numerical comparisons. In particular, we improve the algorithm proposed by Sezener and Oztop [1] and cut down its time complexity drastically, and develop a novel hybrid algorithm by combining metaheuristic search and the deterministic algorithm of Oztop