Neural Circuit Inference from Function to Structure

Advances in technology are opening new windows on the structural connectivity and functional dynamics of brain circuits. Quantitative frameworks are needed that integrate these data from anatomy and physiology. Here, we present a modeling approach that creates such a link. The goal is to infer the structure of a neural circuit from sparse neural recordings, using partial knowledge of its anatomy as a regularizing constraint. We recorded visual responses from the output neurons of the retina, the ganglion cells. We then generated a systematic sequence of circuit models that represents retinal neurons and connections and fitted them to the experimental data. The optimal models faithfully recapitulated the ganglion cell outputs. More importantly, they made predictions about dynamics and connectivity among unobserved neurons internal to the circuit, and these were subsequently confirmed by experiment. This circuit inference framework promises to facilitate the integration and understanding of big data in neuroscience

Population genetic analyses of the Powerplex Fusion kit in a cosmopolitan sample of Chubut province (Patagonia Argentina)

Allele frequencies and forensic parameters for 22 autosomal STR loci and DYS391 locus included in the PowerPlex® Fusion System kit were estimated in a sample of 770 unrelated individuals from Chubut province, southern Patagonia. No significant deviations from Hardy-Weinberg equilibrium were observed after Bonferroni?s correction. The combined power of discrimination and the combined probability of exclusion were >0.999999 and 0.999984, respectively. Comparisons with other worldwide populations were performed. The MDS obtained show a close biological relation between Chubut and Chile. The estimated interethnic admixture supports a high Native American contribution (46%) in the population sample of Chubut. These results enlarge the Argentine databases of autosomal STR and would provide a valuable contribution for identification tests and population genetic studies.Fil: Parolin, María Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Real, Luciano Esteban. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Martinazzo Giménez, Liza Beatriz. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; ArgentinaFil: Basso, Nestor Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto de Diversidad y Evolución Austral; Argentin

Multi-objective evolution for Generalizable Policy Gradient Algorithms

Performance, generalizability, and stability are three Reinforcement Learning (RL) challenges relevant to many practical applications in which they present themselves in combination. Still, state-of-the-art RL algorithms fall short when addressing multiple RL objectives simultaneously and current human-driven design practices might not be well-suited for multi-objective RL. In this paper we present MetaPG, an evolutionary method that discovers new RL algorithms represented as graphs, following a multi-objective search criteria in which different RL objectives are encoded in separate fitness scores. Our findings show that, when using a graph-based implementation of Soft Actor-Critic (SAC) to initialize the population, our method is able to find new algorithms that improve upon SAC's performance and generalizability by 3% and 17%, respectively, and reduce instability up to 65%. In addition, we analyze the graph structure of the best algorithms in the population and offer an interpretation of specific elements that help trading performance for generalizability and vice versa. We validate our findings in three different continuous control tasks: RWRL Cartpole, RWRL Walker, and Gym Pendulum.Comment: 23 pages, 12 figures, 10 table

Evolving Reinforcement Learning Algorithms

We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our method can both learn from scratch and bootstrap off known existing algorithms, like DQN, enabling interpretable modifications which improve performance. Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm. Bootstrapped from DQN, we highlight two learned algorithms which obtain good generalization performance over other classical control tasks, gridworld type tasks, and Atari games. The analysis of the learned algorithm behavior shows resemblance to recently proposed RL algorithms that address overestimation in value-based methods.Comment: ICLR 2021 Oral. See project website at https://sites.google.com/view/evolvingr

El papel del valor de la marca propia en la composición del surtido del minorista: su influencia sobre la lealtad al establecimiento

El objetivo del presente trabajo es conocer la influencia del valor de la MdD, en diferentes tipos de surtido, sobre la lealtad de los consumidores hacia el establecimiento, tanto para una categoría de producto como para la cesta de la compra. Para ello, consideramos surtidos con diferente tamaño y estructura (solo MdD y mixtos) y desarrollamos un experimento online con una muestra de 1.400 consumidores en España. A partir de un análisis ANOVA, los resultados solo muestran diferencias significativas en surtidos mixtos donde la MF de alto valor tiene poco peso, no encontrándose diferencias significativas en surtidos con solo MdD, ni en surtidos mixtos con mayor proporción de MF de alto valor. Estos resultados sugieren diferentes recomendaciones para los minoristas, dependiendo del tipo de surtido por el que optenThe objective of this work is to know the influence of the PL equity, in different structures of the assortment, on the consumer’s loyalty towards the store, for both a product category and the shopping basket. Thus, we consider assortments with different sizes and composition (PL-only and mixed) and develop an online experiment with a sample of 1,400 consumers in Spain. Through an ANOVA analysis, the results only show significant differences in mixed assortments where NBs high equity represent a low ratio, not finding significant differences in PL-only assortments, nor in mixed assortments with a higher ratio of NBs high equity. These results suggest different suggestions for retailers, depending on the type of assortmen