Similar neural states, but dissimilar decoding patterns for motor control in parietal cortex.

Discrete neural states are associated with reaching movements across the fronto-parietal network. Here, the Hidden Markov Model (HMM) applied to spiking activity of the somato-motor parietal area PE revealed a sequence of states similar to those of the contiguous visuomotor areas PEc and V6A. Using a coupled clustering and decoding approach, we proved that these neural states carried spatiotemporal information regarding behaviour in all three posterior parietal areas. However, comparing decoding accuracy, PE was less informative than V6A and PEc. In addition, V6A outperformed PEc in target inference, indicating functional differences among the parietal areas. To check the consistency of these differences, we used both a supervised and an unsupervised variant of the HMM, and compared its performance with two more common classifiers, Support Vector Machine and Long-Short Term Memory. The differences in decoding between areas were invariant to the algorithm used, still showing the dissimilarities found with HMM, thus indicating that these dissimilarities are intrinsic in the information encoded by parietal neurons. These results highlight that, when decoding from the parietal cortex, for example, in brain machine interface implementations, attention should be paid in selecting the most suitable source of neural signals, given the great heterogeneity of this cortical sector.

Applying HMMs to spiking activity recorded from the somato-motor parietal area PE revealed discrete neural states related to reaching movements. These states were extremely similar to those present in the neighbouring visuomotor areas PEc and V6A. Our decoding approach showed that these states conveyed spatiotemporal behaviour information across all three posterior parietal areas. However, decoding accuracy was lower in PE compared to V6A and PEc, with V6A excelling in target inference. These differences held true even when changing the decoding algorithm, indicating intrinsic dissimilarities in information encoding by parietal different areas. These findings highlight the importance of selecting the appropriate neural signal sources in applications such as brain machine interfaces and pave the way for further investigation of the nontrivial diversity within the parietal cortex.

Visual sensitivity at the service of action control in posterior parietal cortex.

The posterior parietal cortex (PPC) serves as a crucial hub for the integration of sensory with motor cues related to voluntary actions. Visual input is used in different ways along the dorsomedial and the dorsolateral visual pathways. Here we focus on the dorsomedial pathway and recognize a visual representation at the service of action control. Employing different experimental paradigms applied to behaving monkeys while single neural activity is recorded from the medial PPC (area V6A), we show how plastic visual representation can be, matching the different contexts in which the same object is proposed. We also present data on the exchange between vision and arm actions and highlight how this rich interplay can be used to weight different sensory inputs in order to monitor and correct arm actions online. Indeed, neural activity during reaching or reach-to-grasp actions can be excited or inhibited by visual information, suggesting that the visual perception of action, rather than object recognition, is the most effective factor for area V6A. Also, three-dimensional object shape is encoded dynamically by the neural population, according to the behavioral context of the monkey. Along this line, mirror neuron discharges in V6A indicate the plasticity of visual representation of the graspable objects, that changes according to the context and peaks when the object is the target of one's own action. In other words, object encoding in V6A is a visual encoding for action.

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

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.

Erratum: A Poisson generalized linear model application to disentangle the effects of various parameters on neurophysiological discharges.

[This corrects the article DOI: 10.1016/j.xpro.2021.100413.].

A Poisson generalized linear model application to disentangle the effects of various parameters on neurophysiological discharges.

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).

Neurons Modulated by Action Execution and Observation in the Macaque Medial Parietal Cortex.

The observation of an action evokes discharges in a rich network of cortical areas [1-14]. In the present study, we have evaluated the effect of grasp execution and of the observation of others' grasping on the activity of neurons in the medial parietal area V6A, an area of the reach-to-grasp network never explored to date in this regard. Although V6A neurons are typically active during one's own grasping execution but not while one observes another's grasping, a minority of neurons showed mirror properties, active both when monkeys performed the task and when they observed it being performed by the experimenter. Recent studies have shown that the discharge of mirror neurons may vary from congruent to noncongruent [7, 10, 15-17], but most mirror neurons show a clear relation between the visual action they respond to and the motor response they code [10], thus matching the sensory description of an observed action with its corresponding internal motor representation. In all V6A putative mirror neurons, instead, neural representations during execution and observation were highly dissimilar, discounting the possibility that V6A specifically encodes the grip type performed by another agent. Notably, we have found that in these neurons, the neural representation of an object changed according to whether grasping was allowed or performed and whether the object was the target of another agent's grasping. In other words, rather than code another agent's observed action, V6A neurons appear to primarily encode the relevance, in the grasping context, of the target object.