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ABSTRACT To comprehend the complex world around us, our brains are tasked with the remarkable job of integrating multiple features into a cohesive whole. While previous studies have primarily focused on the processing and integration of independent features, here we investigated the simultaneous encoding of the interdependent features, specifically head direction (HD) and its temporal derivative, angular head velocity (AHV), by first employing computational modeling on HD systems to explore emergent algorithms and then validated its biological plausibility with empirical data from mice’s HD systems. Our analysis revealed two distinct neuron populations: those with multiphasic tuning curves for HD compromised their HD encoding capacity to better capture AHV dynamics, while those with monophasic tuning curves primarily encoded HD. This pattern of functional dissociation was observed in both artificial HD systems and the cortical and subcortical regions upstream of biological HD systems, suggesting a general principle for encoding interdependent features. Further, exploration of the underlying mechanisms involved examining neural manifolds embedded within the representational space constructed by these neurons. We found that the manifold by neurons with multiphasic tuning curves was locally jagged and complex, which effectively expanded the dimensionality of the neural representation space and in turn facilitated a high-precision representation of AHV. Therefore, the encoding strategy for HD and AHV likely integrates characteristics of both dense and sparse coding schemes to achieve a balance between preserving specificity for individual features and maintaining their interdependency nature, marking a significant departure from the encoding of independent features and thus advocating future research delving into the encoding strategies of interdependent features.
Cai et al. (Fri,) studied this question.