[CogSci] Inviting submissions for a special issue of Computational Brain and Behavior on Sensory Prediction

[Scott Brown]

Computational Brain and Behavior is holding a Special Issue on Sensory Prediction: Engineered and Evolved with a submission deadline of August 1, 2024. Papers will be published as they come; simply navigate to https://link.springer.com/journal/42113<https://link.springer.com/journal/42113>, click "Submit your manuscript", and note that you'd like to be considered for this Special Issue during the submission process. Please consider submitting! Details below. The Guest Editors for this special issue are, Sarah Marzen<https://www.sarahmarzen.com/people.html>, James Crutchfield<https://csc.ucdavis.edu/~chaos/>, Jared Salisbury<https://biophysics.princeton.edu/people/jared-salisbury> and Marc Howard<https://www.bu.edu/psych/profile/marc-howard-ph-d/>.

Engineers try to predict future input from past input; this can take the form of prediction of natural video, natural audio, or text, which has famously led to such products as Generative Pre-trained Transformer 3 (GTP3) and proprietary algorithms for stock market prediction. Organisms and parts of organisms may have evolved to efficiently predict their input as well, and the hypothesis that they do is a cornerstone of theoretical neuroscience, theoretical biology, and cognitive science. How one can design systems to predict input is still a matter of debate, especially when one has continuous-time input—input that has a state at every point in time, not just at specially sampled points. We aim to bring together research that approaches the question of how to design systems to predict input through the lens of biology with machine learning, information theory, and dynamical systems. This knowledge will help establish a foundation of theoretical neuroscience and theoretical biology to enable the scientific community to better calibrate and understand prediction products.

We hope that this Special Issue will cover a wide range of topics.  Some can examine evolved neural systems, including the study of neurons in the retina, hippocampus, the visual cortex, and striatum as well as longstanding learning theory from mathematical psychology. Some can study engineering systems to better predict input through reservoir computing and training recurrent neural networks, in which reservoir weights are trained as well. These are just examples of what we might hope to solicit.

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