Conference on Cognitive Computational Neuroscience (CCN)
Understanding how complex cognitive processes are carried out by the brain is a problem that is bigger than any one discipline. Historically, distinct disciplines have attacked different aspects of the problem, but often in isolation. Each has it’s own strengths and weaknesses. For example, at one end, cognitive neuroscience has provided models that can explain measured neural activity, but are difficult to scale to understand more complex higher-level cognitive behavior. At the other end, cognitive science has developed general computational principles behind human cognition, but are difficult to relate the underlying neural physiology. Machine learning and artificial intelligence have made great strides in engineering artificial system to autonomously solve complex cognitive tasks, but these are difficult to relate to the human brain.
We believe that the time is ripe for a convergence of cognitive neuroscience, cognitive science, and AI. This would ideally result in a new discipline that addresses the questions of cognitive neuroscience by merging its experimental techniques with the powerful concepts and theories that have emerged in cognitive science and AI.
Currently, there are institutional and historical barriers to merging these three disciplines. Researchers in cognitive neuroscience, cognitive science, and AI generally take appointments in different academic departments, attend different conferences, and publish in different journals. Meetings in cognitive science and neuroscience are often atomized, focusing on a specific perceptual modality or cognitive skill. Meanwhile, AI conferences are dominated by engineering-oriented presentations of system architecture and performance, with little reference to neurobiology. As a result, opportunities for researchers in these three disciplines to interact and exchange ideas is far too limited.
We therefore propose Cognitive Computational Neuroscience (CCN), an annual scientific meeting for researchers interested in characterizing the neural computations that underlie complex human behavior. CCN will be organized around a shared commitment to to developing models of brain information processing that are (1) fully computationally defined and implemented in computer simulations, that are (2) neurobiologically plausible, that explain rich measurements of (3) brain activity and (4) behavior for naturalistic stimuli and tasks, and that (5) perform feats of intelligence such as recognition, internal modeling and memory of the environment, language production, decision-making, planning, action, and motor control.
CCN will encourage participation from all experimentalists and theoreticians investigating complex cognitive abilities from a computational point of view. We expect that CCN will draw researchers that address the following challenges:
Understanding brain information processing underlying real-world tasks that involve natural stimuli, rich knowledge, complex inferences, and behavior
Revealing principles of brain connectivity and dynamics at multiple scales
Developing cognitive- or neural-level models of perception, cognition, emotion, and action
Using brain and behavioral data to test these models
Understanding commonalities and differences between neurobiological and artificial systems