New insights on single-neuron selectivity in the era of population-level approaches

Abstract

In the past, neuroscience was focused on individual neurons seen as the functional units of the nervous system, but this approach fell short over time to account for new experimental evidence, especially for what concerns associative and motor cortices. For this reason and thanks to great technological advances, a part of modern research has shifted the focus from the responses of single neurons to the activity of neural ensembles, now considered the real functional units of the system. However, on a microscale, individual neurons remain the computational components of these networks, thus the study of population dynamics cannot prescind from studying also individual neurons which represent their natural substrate. In this new framework, ideas such as the capability of single cells to encode a specific stimulus (neural selectivity) may become obsolete and need to be profoundly revised. One step in this direction was made by introducing the concept of “mixed selectivity,” the capacity of single cells to integrate multiple variables in a flexible way, allowing individual neurons to participate in different networks. In this review, we outline the most important features of mixed selectivity and we also present recent works demonstrating its presence in the associative areas of the posterior parietal cortex. Finally, in discussing these findings, we present some open questions that could be addressed by future studies.

CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning

“…The analysis of the selectivity of neuronal activity at single neuron and network levels has contributed greatly to our understanding of cognitive function and representations learned by the brain (Watkins and Berkley, 1974 ; De Baene et al, 2008 ; Decramer et al, 2019 ; Packheiser et al, 2021 ; Vaccari et al, 2022 ). Similarly, understanding the representations learned by Deep RL agents and how they relate to the current task has been of great interest early on (Mnih et al, 2015 ), and they have proven to be a useful tool in understanding the emergence of spatial representations, e.g., grid cells (Banino et al, 2018 ) and place cells (Vijayabaskaran and Cheng, 2022 ), and units encoding for other task-relevant variables (Wang et al, 2018 ; Cross et al, 2021 ), e.g., time cells (Lin and Richards, 2021 ).…”

Encoding of interdependent features of head direction and angular head velocity in navigation

“…Previous studies have primarily focused on the processing and integration of independent features (Campo et al, 2021; Spence, 2020), such as smell, touch, taste, and sight, to fabricate the unified experience of enjoying a cup of coffee. This integration typically involves neurons that exhibit either pure selectivity, allowing for precise processing and interpretation of specific features (Vaccari et al, 2022; Weinberger, 1995), or mixed selectivity, encoding combinations of multiple features to enhance the brain’s computational flexibility and efficiency (Fusi et al, 2016; Kira et al, 2023; Ledergerber et al, 2021; Ma et al, 2023; Rigotti et al, 2013). Nonetheless, the simultaneous encoding of interdependent features, exemplified by HD and its temporal derivative, AHV, poses a great challenge, which requires a delicate balance between preserving specificity for each individual feature to prevent mutual interference and maintaining their interdependency nature, given the crucial role of AHV in updating HD.…”