Summary The activity of neurons of the medial posterior parietal area V6A in macaque monkeys is modulated by many aspects of reach task. In the past, research was mostly focused on modulating the effect of single parameters upon the activity of V6A cells. Here, we used Generalized Linear Models (GLMs) to simultaneously test the contribution of several factors upon V6A cells during a fix-to-reach task. This approach resulted in the definition of a representative “functional fingerprint” for each neuron. We first studied how the features are distributed in the population. Our analysis highlighted the virtual absence of units strictly selective for only one factor and revealed that most cells are characterized by “mixed selectivity.” Then, exploiting our GLM framework, we investigated the dynamics of spatial parameters encoded within V6A. We found that the tuning is not static, but changed along the trial, indicating the sequential occurrence of visuospatial transformations helpful to guide arm movement.

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The projections from the claustrum to cortical areas within and adjacent to the superior parietal lobule were studied in 10 macaque monkeys, using retrograde tracers, computerized reconstructions, and quantitative methods. In contrast with the classical view that posterior parietal areas receive afferents primarily from the dorsal and posterior regions of the claustrum, we found that these areas receive more extensive projections, including substantial afferents from the anterior and ventral regions of the claustrum. Moreover, our findings uncover a previously unsuspected variability in the precise regions of the claustrum that originate the projections, according to the target areas. For example, areas dominated by somatosensory inputs for control of body movements tend to receive most afferents from the dorsal-posterior claustrum, whereas those which also receive significant visual inputs tend to receive more afferents from the ventral claustrum. In addition, different areas within these broadly defined groups differ in terms of quantitative emphasis in the origin of projections. Overall, these results argue against a simple model whereby adjacency in the cortex determines adjacency in the sectors of claustral origin of projections and indicate that subnetworks defined by commonality of function may be an important factor in defining claustrocortical topography.

Summary The protocol provides an extensive guide to apply the generalized linear model framework to neurophysiological recordings. This flexible technique can be adapted to test and quantify the contributions of many different parameters (e.g., kinematics, target position, choice, reward) on neural activity. To weight the influence of each parameter, we developed an intuitive metric (“w-value”) that can be used to build a “functional fingerprint” characteristic for each neuron. We also provide suggestions to extract complementary useful information from the method. For complete details on the use and execution of this protocol, please refer to Diomedi et al. (2020) .

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.