Matlab implementation of the Information Jitter Derivative (IJD) method, which allows to decompose the information encoded in the temporal structure of a spike train into the unique, complementary information contained in its different temporal scale components. IJD uses a jitter procedure to gradually decrease the precision of the neural activity and allows to precisely identifying the relevant timescales in the encoding of the stimulus information (see Fig. 1).
Figure 1. Application of IJD to obtain the contribution of each temporal scale to the information contained in the simulated response of a neuron to a given set of stimuli. Top panel: the curve represents the information as a function of the precision with which the neural activity is measured. Insets: Neuron's average firing rates elicited by the two stimuli (red and blue) for different jitter values = 0, 10, 20, 30 and 40~ms from left to right. Bottom panel: Scale Contribution Curve (SCC) corresponding to the curve shown in the top panel. The SCC peaks at around 20ms which is, by construction, the timescale encoding most of the stimulus information.
The script demo will produce three figures showing Iprecision, the Scale Contribution Curve and the jittered firing rates (see figure above).
This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 699829 (ETIC).
