Invited Talks

• John K. Tsotsos
  Department of Computer Science and
  Centre for Vision Research,
  York University, Toronto, Canada
  Correspondence to tsotsos@cs.yorku.ca
  
  Distributed Saliency Computations Solve the Feature Binding Problem
  
  Computational vision has a long history of proposing methods for decomposing a visual signal into its components. For example, many good strategies have appeared for decomposing visual motion signals. What has been far more elusive is how to recombine those components into a whole. This problem has even merited its own name - the binding problem. To date no realizable process has appeared to solve the binding problem, even in part, although several proposals are being studied. This presentation will focus on a new strategy utilizing a novel distributed
  saliency computation mechanism that solves at least one aspect of the binding problem, namely the binding of features from separate representations into a whole. Several examples will be drawn from a new, biologically realistic, motion analysis system, one that attends to complex motion patterns. An example of how this approach even yields Treisman-style illusory conjunctions is included. The entire process is implemented and operates on real image sequences. The implications for the neurobiology of visual attention will round out the presentation.
  
  
• Gustavo Deco
  Institucio Catalana de Recerca i Estudis Avancats (ICREA)
  Universitat Pompeu Fabra, Barcelona, Spain
  Correspondence to Gustavo.Deco@upf.edu
  
  The Computational Neuroscience of Visual Cognition: Attention, Memory and Reward
  
  Cognitive behaviour requires complex context-dependent processing of information that emerges from the links between attentional perceptual processes, working memory and reward-based evaluation of the performed actions. We describe a computational neuroscience theoretical framework which shows how an attentional state held in a short term memory in the prefrontal cortex can by top-down processing influence ventral and dorsal stream cortical areas using biased competition to account for many aspects of visual attention. We also show how within the prefrontal cortex an attentional bias can influence the mapping of sensory inputs to motor outputs, and thus play an important role in decision making. We also show how the absence of expected rewards can switch the attentional bias signal, and thus rapidly and flexibly alter cognitive performance. This theoretical framework incorporates spiking and synaptic dynamics which enable single neuron responses, fMRI activations, psychophysical results, the effects of pharmacological agents, and the effects of damage to parts of the system, to be explicitly simulated and predicted. This computational neuroscience framework provides an approach for integrating different levels of investigation of brain function, and for understanding the relations between them. The models also directly address how bottom-up and top-down processes interact in visual cognition, and show how some apparently serial processes reflect the operation of interacting parallel distributed systems.