Spike count analysis for multiplexing inference (SCAMPI).

Understanding how neurons encode multiple simultaneous stimuli is a fundamental question in neuroscience. We have previously introduced a novel theory of stochastic encoding patterns wherein a neuron's spiking activity dynamically switches among its constituent single-stimulus activity patterns when presented with multiple stimuli (Groh et al., 2024). Here, we present an enhanced, comprehensive statistical testing framework for such "multiplexing". As before, our approach evaluates whether dual-stimulus responses can be accounted for as mixtures of Poissons related to single-stimulus benchmarks. Our enhanced framework improves upon previous methods in two key ways. First, it introduces a stronger set of foils for multiplexing, including an "overreaching" category that captures overdispersed activity patterns unrelated to the single-stimulus benchmarks, reducing false detection of multiplexing. Second, it detects continuous mixtures, potentially indicating faster fluctuations - i.e. at sub-trial timescales - that would have been overlooked before. We utilize a Bayesian inference framework, considering the hypothesis with the highest posterior probability as the winner, and employ the predictive recursion marginal likelihood method for non-parametric estimation of the latent mixing distributions. Reanalysis of previous findings confirms the general observation of fluctuating activity and indicates that fluctuations may well occur on faster timescales than previously suggested. We further confirm that multiplexing is more prevalent for (a) combinations of face stimuli than for faces and non-face objects in the inferotemporal face patch system; and (b) distinct vs fused objects in the primary visual cortex.

Duke Scholars

Author Jennifer M. Groh Psychology & Neuroscience

Author Surya Tapas Tokdar Statistical Science