PyTCI
A python toolbox to analyze the temporal integration windows of neural networks that process time-series input. Integration windows are defined as the time window within which stimuli alter a sensory response and outside of which stimuli have little effect. Integration windows provide a simple and general way to define the analysis timescale of a response. We estimate integration windows by presenting segments of natural stimuli in two different pseudorandom orders, such that the same segment occurs in two different contexts (is surrounded by different segments). We then estimate the smallest segment duration outside of which stimuli have little effect on the response.
The TCI paradigm was initially developed to estimate integration windows for biological neural systems (Norman-Haignere et al, 2022). The method however can be applied to any sensory response, and we have recently used the method to understand how deep speech recognition systems learn to flexibly integrate across multiple timescales (Keshishian et al, 2021).
Related publications
Understanding Adaptive, Multiscale Temporal Integration In Deep Speech Recognition Systems. Advances in Neural Information Processing Systems (2021)