Topic: Forecasting

Nassim Nicholas Taleb examines distributions whose behaviour is dominated by extremes and tail events. He classifies these distributions and identifies circumstances in which familiar statistical tools become unreliable, including slow or problematic convergence of sample averages under the law of large numbers. The lecture questions the robustness of commonly used statistical procedures in fat-tailed settings, the reliability of frequency-based forecasting, and the use of past averages as guides to future outcomes. Taleb then develops implications for decision-making and the changes in metho

Neurons in the brain exhibit significant heterogeneity in their dynamics. Whether this diversity plays any computational role and if it is exploited to perform efficient computation is not well understood. We used the reservoir computing paradigm to answer this question. Balanced networks with different levels of heterogeneity in their neuronal time constants were set up and benchmarked against various working-memory-related tasks, including stimulus recall, forecasting, and nonlinear transformation. Our results suggest that neuronal heterogeneity, even without any recurrent communication, si