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Interesting #NeuroPreprint about separating “motor” and “cognitive” variables in neural analyses, which should really be given more attention in all studies IMO:
Separating cognitive and motor processes in the behaving mouse

by #EconomoLab

bioRxivSeparating cognitive and motor processes in the behaving mouseThe cognitive processes supporting complex animal behavior are closely associated with ubiquitous movements responsible for our posture, facial expressions, ability to actively sample our sensory environments, and other central processes. These movements are strongly related to neural activity across much of the brain, making it challenging to dissociate the neural dynamics that support cognitive processes from those supporting movements when they are highly correlated in time. Of critical importance is whether the dynamics supporting cognitive processes and related movements are separable, or if they are both driven by common neural mechanisms. Here, we demonstrate how the separability of cognitive and motor processes can be assessed, and, when separable, how each component can be isolated. We establish a novel two-context behavioral task in mice that involves multiple cognitive processes and show that commonly observed dynamics taken to support cognitive processes are strongly contaminated by movements. When cognitive and motor components are isolated using our analytical approach, we find that they exhibit distinct dynamical trajectories. Further, properly accounting for movement revealed that separate populations of cells encode cognitive and motor variables, in contrast to the ‘mixed selectivity’ reported by prior work. Accurately isolating the dynamics associated with particular cognitive and motor processes will be essential for developing conceptual and computational models of neural circuit function and evaluating the function of the cell types of which neural circuits are composed. ### Competing Interest Statement The authors have declared no competing interest.

Another cool #NeuroPreprint (broadly speaking: it’s on #Navigation performance), from #SpiersLab (@hugospiers):
Video gaming, but not reliance on GPS, is associated with spatial navigation performance
I am intrigued by the difference in sample numbers: “n = 822, 280 men, 542 women”? Is that a typo?
And also this:

“There was a significant association between weekly hours of video gaming and navigation performance which was not moderated by gender. After accounting for video game experience, gender was no longer significantly associated with navigation performance.”
Is gender a factor or not? 🤔
#Gaming #Need2Read

bioRxivVideo gaming, but not reliance on GPS, is associated with spatial navigation performanceRecent evidence suggests that greater reliance on GPS-assisted devices is associated with poorer navigation ability. Contrastingly, studies have shown that video gaming can enhance navigation ability. While gender differences in navigation ability in favour of men are well-reported, it remains unclear if the effects of reliance on GPS and video gaming on navigation performance are influenced by gender. We investigated whether gender would influence the effect of gaming experience and reliance on GPS on navigation ability using the mobile app Sea Hero Quest, which has been shown to predict real-world wayfinding performance. Alongside navigation performance assessment we asked a series of self-report questions relating to reliance on GPS, navigation strategies and gaming experience with a group of US-based participants (n = 822, 280 men, 542 women, mean age = 26.3 years, range = 18-52 years). A multivariate linear regression model found no significant association between reliance on GPS and navigation performance for either gender. There was a significant association between weekly hours of video gaming and navigation performance which was not moderated by gender. After accounting for video game experience, gender was no longer significantly associated with navigation performance. These findings have implications for which daily activities may enhance or disrupt specific cognitive abilities. Future studies applying an interventional design and real-world navigation testing would be useful to determine whether video games playing increases navigation skill, or whether those who are good at navigating tend to play more video games. ### Competing Interest Statement The authors have declared no competing interest.

Ooh this seems very interesting:
Lost in time: Relocating the perception of duration outside the brain by #RobbeLab (not on here right?)
#Time #TimeEstimation #RatBehaviour #PlaceCells #TimeCells (added those last two because they seem clearly linked to the contents even though I don’t know if they’re actually discussed)

I did a time-estimation experiment and yes, some of the rats did develop these kind of superstitions… like standing at the delay zone… Not all of them though, at least not that we could see! And time cells seem to tell us that they do have some internal representation of time passed… also if time is actually space does it mean that the #TemporalContexModel is actually a MovementContextModel? @marcwhoward you might be interested in this…
#Need2Read

#NeuroPreprint #DerdickmanLab #PlaceCells #TimeCells #Hippocampus

Time or distance: predictive coding of Hippocampal cells

We show that the type of experiment determined the cells’ encoding, such that in fixed-distance experiments distance-encoding cells dominated, while on fixed-time experiments time-encoding cells dominated.

Seems very interesting! But is it surprising? Place cells are known to encode what is consistent and disregard what is not… 🤔
#Need2Read

Edit: typo

bioRxivTime or distance: predictive coding of Hippocampal cellsThe discovery of place cells within the hippocampus has pointed to the importance of the hippocampus for navigation. The more recent discovery of hippocampal time cells has broadened the perspective of encoding in the hippocampus. An alternative hypothesis to the existence of time cells is based on the notion that hippocampal cells deduce location by integrating travelled distance (“path integration”). According to this alternate hypothesis, time cells, which fire at particular times when animals are running on a treadmill without changing location, actually encode accumulated distance on the treadmill. To examine this hypothesis, Kraus et al.[1][1] performed treadmill experiments in which animals either ran for a fixed time or a fixed distance with varying velocities. Two distinct coding modes of hippocampal principal cells were found. Some cells encoded travelled distance and others elapsed time, thus refuting the notion that all hippocampal cells were performing path integration. Using the data from these experiments, we asked whether the two populations depended on the type of task the rats were engaged in. We show that the type of experiment determined the cells’ encoding, such that in fixed-distance experiments distance-encoding cells dominated, while on fixed-time experiments time-encoding cells dominated. These results suggest that the cells’ encoding contains a predictive element, dependent on the important variables of the experiment. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-1
bioRxivLearning produces a hippocampal cognitive map in the form of an orthogonalized state machineCognitive maps confer animals with flexible intelligence by representing spatial, temporal, and abstract relationships that can be used to shape thought, planning, and behavior. Cognitive maps have been observed in the hippocampus, but their algorithmic form and the processes by which they are learned remain obscure. Here, we employed large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different versions of linear tracks in virtual reality. The results provide a detailed view of the formation of a cognitive map in the hippocampus. Throughout learning, both the animal behavior and hippocampal neural activity progressed through multiple intermediate stages, gradually revealing improved task understanding and behavioral efficiency. The learning process led to progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. We show that a Hidden Markov Model (HMM) and a biologically plausible recurrent neural network trained using Hebbian learning can both capture core aspects of the learning dynamics and the orthogonalized representational structure in neural activity. In contrast, we show that gradient-based learning of sequence models such as Long Short-Term Memory networks (LSTMs) and Transformers do not naturally produce such representations. We further demonstrate that mice exhibited adaptive behavior in novel task settings, with neural activity reflecting flexible deployment of the state machine. These findings shed light on the mathematical form of cognitive maps, the learning rules that sculpt them, and the algorithms that promote adaptive behavior in animals. The work thus charts a course toward a deeper understanding of biological intelligence and offers insights toward developing more robust learning algorithms in artificial intelligence. ### Competing Interest Statement The authors have declared no competing interest.