Neuroscience Institute
http://events.berkeley.edu/index.php/calendar/sn/HWNI.html
Upcoming EventsLikelihood Based Evaluation for Scanpath Models in Scene Viewing, May 30
http://events.berkeley.edu/index.php/calendar/sn/HWNI.html?event_ID=109178&date=2017-05-30
When observers view natural scenes their eye movements show elaborate statistical patterns beyond the fixation density over an image. An important approach to understand these patterns --- and thus ultimately to understand how humans choose where to look at --- is to build models which generate full scanpaths in natural scenes, i.e. a sequence of fixation locations. There are a multitude of different approaches to build scanpath models and to evaluate them statistically, however. Therefore, unifying and improving the statistical analysis of such models is essential. In my talk I will show that a likelihood can be calculated directly for virtually all scanpath models. Using our recent SceneWalk model as an example, I will illustrate how likelihood enables better model fitting including Bayesian inference to obtain reliable parameter estimates and corresponding credible intervals. Using hierarchical models, inference is even possible for individual observers. Furthermore, the likelihood can be used to compare different models. As an example I will show that the SceneWalk model produces more exact predictions than any model could by predicting only a static fixation density the way saliency models do. Additionally, the likelihood based evaluation differentiates model variants, which produced indistinguishable predictions on hitherto used statistics. Beyond the application to scanpath models, a direct computation of the likelihood might be an interesting approach for any other models which predict sequentially dependent human behaviour.http://events.berkeley.edu/index.php/calendar/sn/HWNI.html?event_ID=109178&date=2017-05-30Neural circuits for goal-directed sensorimotor transformation, May 30
http://events.berkeley.edu/index.php/calendar/sn/HWNI.html?event_ID=108299&date=2017-05-30
A key function of the brain is to interpret incoming sensory information in the context of learned associations in order to guide adaptive behavior. However, the precise neuronal circuits and causal mechanisms underlying goal-directed sensorimotor transformations remain to be clearly defined for the mammalian brain. Technological advances in mouse genetics to define cell-types, in optogenetics to control neuronal activity, and in electrophysiological and imaging techniques to precisely measure neuronal activity now begin to make it possible to obtain a detailed mechanistic understanding of the neuronal circuits driving learned goal-directed sensorimotor transformations. Here, I will discuss my laboratoryâ€™s efforts to characterize a simple whisker-cued operant behavior. Although we are very far from a complete understanding, we find evidence for cell-type specific contributions of different neurons in both neocortex and striatum, which are likely to participate causally in both learning and execution of this reward-motivated sensorimotor task.http://events.berkeley.edu/index.php/calendar/sn/HWNI.html?event_ID=108299&date=2017-05-30