Neural mechanisms

…can advance our understanding of the neural mechanisms underlying human cognition and behavior. Funded proposals typically relate precise and rich quantifications of physiological responses and behavior in ways that advance theory (Intellectual Merit). Funded proposal also typically strengthen the field through, for example, outreach, mentoring the next generation of diverse cognitive neuroscientists, and/or increasing awareness and utilization of the research the field produces (Broader Impacts). Intellectual Merit In

Biological mechanisms often have to be reconstructed from indirect measurements such as microscopy images, gene activity and cellular time series. This workshop examines inverse problems in living systems, where noise, complexity and diversity challenge existing mathematical approaches. Mathematicians, physicists, computer scientists and biologists will explore methods including deep learning and models informed by physical constraints to infer hidden cellular, genetic and neural mechanisms from experimental data.

Why do individuals, both humans and animals, exhibit personal biases in two-alternative decision-making tasks, even when no clear reason exists to favor one alternative over another? In this talk, I will explore two competing hypotheses to explain these idiosyncratic biases. The first suggests that such tendencies arise from unique personal experiences, where past associations between actions and feedback influence future choices. The second hypothesis proposes that the bias reflects irreducible microscopic heterogeneities in the dynamics of decision-making networks. I will present experiment

Cognitive flexibility, the capacity to adjust thoughts and actions in response to sudden changes in environment, situation, or task demands, is essential for a healthy daily life and for understanding maladaptive behaviors. Reversal learning tasks in animal research are commonly employed to evaluate this adaptability. In particular, serial reversal learning paradigms are useful to assess the ability to switch task-solving strategies. However, conventional serial reversal learning tasks in mice, in general, are time-consuming and labor intensive, limiting its broader application. To address thi