behavioural patterns
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Analyzing artificial neural networks to understand the brain
In the first part of this talk I will present work showing that recurrent neural networks can replicate broad behavioral patterns associated with dynamic visual object recognition in humans. An analysis of these networks shows that different types of recurrence use different strategies to solve the object recognition problem. The similarities between artificial neural networks and the brain presents another opportunity, beyond using them just as models of biological processing. In the second part of this talk, I will discuss—and solicit feedback on—a proposed research plan for testing a wide range of analysis tools frequently applied to neural data on artificial neural networks. I will present the motivation for this approach as well as the form the results could take and how this would benefit neuroscience.
Three levels of variability in the collective behavior of locusts
Many aspects of collective behavior depend on interactions between conspecifics. This is especially true for the collective motion of locusts, which swarm in millions while maintaining synchrony among individuals. However, whether locusts share and maintain the same socio-behavioral patterns – between groups, individuals and situations – remains an open question. Studying marching locusts under lab conditions, we found that (1) different groups behave differently; (2) locusts within a group homogenize their behavior; and (3) individuals have different socio-behavioral tendencies and context-dependent states. These variability levels suggest that behavioral differences within and among individuals exist, affect others, and shape the collective behavior of the entire group.
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