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Dimensions of Neural Coding, Computation and Communication


Recent advances in multi-electrode and optical imaging technologies enable simultaneous recordings of hundreds or even thousands of neurons. Since neural coding, computation, and communication likely rely on coordinated activity patterns across large cell populations, such data facilitate the study of the global structure of neural function, which cannot be revealed by analyzing functional attributes at the single-neuron level.

While the activity of a population with N neurons can be pictured in an N-dimensional space, their behaviorally relevant dynamics under natural conditions may reside within much lower-dimensional manifolds. Methods for dimensionality estimation and dimensionality reduction can help to identify these manifolds, study the collective neural dynamics and understand population codes and their intrinsic correlation structures and trial-to-trial variability. This approach is relevant for exploratory research and when testing hypotheses, from neural connectivity to overall neural functionality.

As concepts and methods related to this topic are rapidly developing, this workshop cannot (and should not try to) provide a single polished view but will rather present complementary views about how to define effective spaces of neural activity and how to interpret the experimentally observed phenomena. We will do so from various angles and for different (sensory, motor, and cognitive) neuronal systems - and hope to trigger discussions amongst and between theorists and experimentalists.