Workshop on Methods of Information Theory in Computational NeuroscienceMarco CelottoIstituto Italiano di Tecnologia (IIT)
Developing and testing the concept of intersection information using PID
To crack the neural code used during a perceptual decision-making process, it is fundamental to determine not only how information about sensory stimuli (encoding stage) is encoded in neural activity, but also how this information is read out to inform the behavioral decision [1].
In previous work, our group used the concept of redundancy, as defined into the mathematical framework of Partial Information Decomposition (PID), to develop an information-theoretic measure capable of quantifying that part of the information which is at the intersection between the mutual information of the stimulus S and the neural response R, and the mutual information of R and the consequent behavioral choice C [2]. We called this measure "Information-theoretic intersection information" or II(S;R;C).
In this talk, we present our latest progress on how to use II(S;R;C) to study neural coding. We examine in detail its conceptual properties, and we show the results it provides both on simulated and on real neural data (the latter to test the role of spike timing in perceptual decision making). Furthermore, we discuss how to test the significance of the measure through a proper statistical null hypothesis.
[1] Panzeri et al. 2017 Neuron, [2] Pica et al. 2017 NIPS
