Distinguished Speaker Series

Seeing by moving: the indissoluble bond between perception and action Establishing a representation of space is a major goal of sensory systems.  Spatial information, however, is not always explicit in the incoming sensory signals. In most modalities it needs to be actively extracted from cues embedded in the temporal flow of receptor activation. Vision, on

I will present a model of continuous-time opinion dynamics for an arbitrary number of agents that communicate over a network and form real-valued opinions about an arbitrary number of options.  The model generalizes linear and nonlinear models in the literature. Drawing from biology, physics, and social psychology, we introduce an attention parameter to modulate social

ABSTRACT: Self-preservation is embodied in our brain’s circuitry: we seek food when hungry, warmth when cold, and mates when lusty. In the evolution of the mammalian brain, circuitry for regulating one’s own survival and well-being was modified. For sociality, the important result was that the ambit of me extends to include others -- me-and-mine. Offspring, mates, and kin came

Speaker: Peter Dayan, Max Planck Institute for Biological Cybernetics, https://www.mpg.de/12309357/biologische-kybernetik-dayan Hosted by Henning Sprekeler; moderated by Robert Tjarko Lange Peril, Prudence and Planning as Risk, Avoidance and Worry Risk occupies a central role in both the theory and practice of decision-making. Although it is deeply implicated in many conditions involving dysfunctional behavior and thought, modern

It is a commonly held assumption that the fovea is where the interesting action occurs. To scrutinize this assumption, we conducted a series of experiments that addressed the following question: How important are the different regions of the visual field for gaze guidance in everyday visual-cognitive tasks? Following on from classic findings for sentence reading, I will present key results from various

Recent years have seen a considerable surge of research on interest-based engagement, examining how and why people are engaged in activities without relying on extrinsic rewards. However, the field of inquiry has been somewhat segregated into three different research traditions which have been developed relatively independently --- research on curiosity, interest, and trait curiosity/interest. The

Abstract: In current debates over deep-neural-network-based AI, deep learning researchers have adopted the mantle of philosophical empiricism and associationism, and its critics have taken up the side of philosophical rationalism and nativism.  These rationalist critics, however, often interpret associationism and empiricism in a way which is too caricatured to fit the views of any significant

Postponed to a later date (to be assessed) More info coming soon!  

Abstract: Humans spend a lifetime learning, storing and refining a repertoire of motor memories. However, it is unknown what principle underlies the way our continuous stream of sensorimotor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a principled theory of motor learning based on the

Abstract: Learning is a central concept in many scientific disciplines. Communication about research on learning is, however, hampered by the fact that different researchers define learning in different ways. In this talk, we introduce the extended functional definition of learning that can be used across scientific disciplines. We provide examples of how the definition can

Abstract: We can view cortex from two fundamentally different perspectives: a powerful device for performing optimal inference, or an assembly of biological components not built for achieving statistical optimality. The former approach is attractive thanks to its elegance and potentially wide applicability, however the basic facts of human pattern vision do not support it. Instead,

Abstract: Unsupervised learning is experiencing a renaissance. Driven by an abundance of unlabelled data and the advent of deep generative models, machines are now able to synthesise complex images, videos and sounds. In robotics, one of the most promising features of these models - the ability to learn structured latent spaces - is gradually gaining