Cognitive Computational Neuroscience (CCN) is an annual scientific conference for neuroscientists characterizing the neural computations that underlie complex behavior. The goal is to develop computationally defined models of brain information processing that explain rich measurements of brain activity and behavior. To explain complex cognition and behavior, such models will ultimately have to perform feats of intelligence such as perception, internal modelling and memory of the environment, decision-making, planning, action, and motor control under naturalistic conditions.
Why is this conference necessary?
Cognitive science has developed computational models at the cognitive level to explain aspects of complex behavior. Computational neuroscience has developed neurobiologically plausible computational models to explain neuronal responses to sensory stimuli and certain low-dimensional decision, memory, and control processes. Cognitive neuroscience has mapped a broad range of cognitive processes onto brain regions. Artificial intelligence has developed models that perform feats of intelligence. Building on these advances, we now need to put the pieces of the puzzle together. CCN is unique in its focus on the intersection between these fields, where comprehensive brain-computational models are beginning to explain high-level neural representations and dynamics, and complex feats of intelligent behavior that involve rich world knowledge.
What topics are appropriate for this conference?
We encourage participation from experimentalists and theoreticians investigating complex brain computations in humans and animals. CCN will draw researchers that address challenges including:
- 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 such models
- Understanding commonalities and differences between biological and artificial intelligent systems
- Using techniques from machine learning and artificial intelligence to model brain information processing, and, conversely, incorporating neurobiological principles in machine learning and artificial intelligence
- Measuring brain activity at multiple spatial scales in humans, nonhuman primates, and other animals
- Using psychophysical techniques to relate sensory inputs to behavioral responses
The conference will have a single-track format that includes opportunities for presentation of new research, discussions, and tutorials. To make the content of the conference available to the broader neuroscience community, we will videotape and post talks to a public web site that includes infrastructure for commentary and discussion.