A neural network for uncertainty anticipation and information seeking

In a world flooded with ‘click bait’, ‘alternative facts’, and ‘fake news’ one’s ability to seek out, discern, and value information is of utmost importance. Although contemporary phenomena, these cultural ills take advantage of an evolutionarily-preserved drive for humans and nonhuman animals to monitor for and pursue opportunities to gain information. Indeed, in a natural environment where rewards are scarce and can be risky, animals often seek sensory cues as a source of information about future outcomes. Interestingly, humans and nonhuman animals will seek sensory information that provides advance information that predicts an outcome even when this information does not influence the event outcome or may even come at a cost to the eventual reward. This willingness to ‘pay’ for information, despite being unable to impact task outcome, indicates that the information itself has intrinsic value to subjects. But how and where in the brain are opportunities to learn new information about uncertain events signaled? How does the brain guide behavior towards pursuing this information? Elucidating these mechanisms would expand our understanding of how information seeking interacts with primary reward seeking in naturalistic environments and could further inform theories of attention, learning, and economic decision-making. Here, I demonstrate that connected regions of the anterior cingulate cortex (ACC), striatum, and pallidum contain neurons whose activity is selectively modulated by the presence and levels of outcome uncertainty. I describe the response of these neurons, many of which anticipate the resolution of uncertainty about an outcome— including when it is resolved through the animal seeking advance information. Finally, I demonstrate that the neural activity within areas of basal ganglia in this ‘uncertainty circuit’ causally contributes to information-seeking behaviors observed in nonhuman primates. This work demonstrates that connected regions of the brain previously associated with responses to primary rewards and motivation also contain a mechanism for anticipating uncertainty resolution and directing behaviors towards pursuing information that reduces uncertainty about upcoming events

Machine Learning in Drug Discovery and Development Part 1: A Primer

Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.Laboratorio de Investigación y Desarrollo de Bioactivo