Bouss, P. | Search bioRxiv

bioRxiv2025

Transient recurrent dynamics shape representations in mice

Brain networks are high-dimensional and interacting complex systems that exhibit substantial structural heterogeneity as well as temporal variability. Yet, when exposed to a stimulus, their recurrent circuits perform reliable computations. The mechanisms underlying this robustness are, however, still mostly unknown. Here, we combine analyses of Neuropixels recordings of awake, behaving mice with models and theory of recurrent neural networks to identify three core computational characteristics that emerge from the interplay of many network constituents and drive dynamic, reliably classifiable stimulus representations. We find that the level of recurrent inhibition in circuits and the microscopic chaos of dynamics respectively drive mean population responses as well as the within- and across-class stimulus response similarities. These core characteristics in turn non-trivially interact to predict and shape the experimentally observed separability of visual and tactile stimulus representations in mouse superior colliculus. Using these characteristics to assess the information transmitted through the network for multiple stimuli reveals a trade-off in coding space: increasing the number of stimuli conveys more information but also reduces their separability due to their larger overlap in the finitedimensional neuronal space. Our analysis predicts that, only for the experimentally observed small population activity, information keeps increasing with the number of stimuli, revealing another crucial advantage of sparse coding.

neuroscience↗

bioRxiv2021

Generating surrogates for significance estimation ofspatio-temporal spike patterns

The generation of surrogate data, i.e., the modification of original data to destroy a certain feature, is used for the implementation of a null-hypothesis whenever an analytical approach is not feasible. Thus, surrogate data generation has been extensively used to assess the significance of spike correlations in parallel spike trains. In this context, one of the main challenges is to properly construct the desired null-hypothesis distribution and to avoid a bias in the null-hypothesis by altering the spike train statistics. A classical surrogate technique is uniform dithering (UD), which displaces spikes locally and uniformly. In this study, we compare UD against five surrogate techniques (two newly introduced) in the context of the detection of significant spatio-temporal spike patterns. We evaluate the surrogates for their performance, first on spike trains based on point process models with constant firing rate, and second on modeled non-stationary artificial data serving as ground truth to assess the pattern detection in a more complex and realistic setting. We determine which statistical features of the original spike trains are modified and to which extent. Moreover, we find that UD fails as an appropriate surrogate because it leads to a loss of spikes in the context of binning and clipping, and thus to a large number of false-positive patterns. The other surrogates achieve a better performance in detecting precisely timed higher-order correlations. Based on these insights, we analyze experimental data from pre-/motor cortex of macaque monkeys during a reaching-and-grasping task for spatio-temporal spike patterns. Significance statementTemporal jittering or dithering of single spikes or subsections of spike trains is a common method of generating surrogate data for the statistical analysis of temporal spike correlations. We discovered a serious problem with the classical and widely used method of uniform dithering that can lead to an overestimation of significance, i.e., to false positives in the statistical evaluation of spatio-temporal spike patterns. Therefore we consider 5 other dithering methods, compare and evaluate their statistical properties. Finally, we apply a much better method (trial shifting) to the analysis of experimental multiple-unit recordings and find several highly significant patterns that also reflect different experimental situations.

neuroscience↗