Information Jitter Derivative Method: A Novel Approach to the Analysis of Multiplexed Neural Codes

Abstract

Recent studies have pointed out that the neural code may use multiplexing to encode unique information at different temporal. Here we investigate in detail the information encoded in the spiking activity of a neuron by computing the unique contribution that each temporal scale makes to it. We do this by analytically inferring the derivative of the information with respect to the precision with which the neural response is measured. We propose the Information Jitter Derivative (IJD) method, which uses a jitter approach to modify the precision of the neural response. The IJD allows to infer the temporal scales playing a relevant role in the encoding of the information contained in the response of a neuron to a given set of stimuli. We validated the IJD on simulated data. We further demonstrated its usefulness on real neural responses recorded from the retinal ganglion cells (RGCs) of the axolotl salamander and show that these cells carry the information about fine and coarse features of a visual scene using different temporal scales. ; Presented at the BARCCSYN Conference, Barcelona, June, 2017. http://www.crm.cat/en/Activities/Curs_2016-2017/Pages/BARCCSYN-2017.aspx This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 699829 and under the Marie Sklodowska-Curie grant agreement No 659227.