Biology subjects

Voges, N.

Publications and source records attributed to Voges, N..

3 recordsLinked to original sources

bioRxiv2022

Decomposing neural circuit function into information processing primitives

Cognitive functions arise from the coordinated activity of neural populations distributed over large-scale brain networks. However, it is challenging to understand and measure how specific aspects of neural dynamics translate into operations of information processing, and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple transformations of the information carried by neural activity. Here, we propose that distinct neural circuit functions, such as stimulus representation, working memory, or selective attention stem from different combinations and types of low-level manipulations of information, or information processing primitives. To test this hypothesis, we combine approaches from information theory with computational simulations of canonical neural circuits involving one or more interacting brain regions that emulate well-defined cognitive functions. More specifically, we track the dynamics of information emergent from dynamic patterns of neural activity, using suitable quantitative metrics to detect where and when information is actively buffered ("active information storage"), transferred ("information transfer") or non-linearly merged ("information modification"), as possible modes of low-level processing. We find that neuronal subsets maintaining representations in working memory or performing attention-related gain modulation are signaled by their boosted involvement in operations of active information storage or information modification, respectively. Thus, information dynamics metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, i.e., through which type of primitive computation, a capability that may be exploited for the parsing of actual experimental recordings.

neuroscience↗

bioRxiv2020

Long-range coordination patterns in cortex change withbehavioral context

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet this massively parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

neuroscience↗

bioRxiv2020

On the complexity of resting state spiking activity in monkey motor cortex

Resting state has been established as a classical paradigm of brain activity studies, mostly based on large scale measurements such as fMRI or M/EEG. This term typically refers to a behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks claim to mimic such idle states, but compare their results to task- or stimulus-driven experiments, which might lead to misleading conclusions. To provide a proper basis for comparing physiological and simulated network dynamics, we characterize simultaneously recorded single neurons spiking activity in monkey motor cortex and show the differences from spontaneous and task-induced movement conditions. The resting state shows a higher dimensionality, reduced firing rates and less balance between population level excitation and inhibition than behavior-related states. Additionally, our results stress the importance of distinguishing between rest with eyes open and closed.

neuroscience↗