Biology subjects

Dahmen, D.

Publications and source records attributed to Dahmen, D..

8 recordsLinked to original sources

bioRxiv2026

Task Engagement Gates Interareal Communication Geometry in the Mouse Thalamocortical-Midbrain Visual Circuit

Visual processing unfolds across hierarchically organized brain circuits. Existing theories largely explain changes in population geometry through local shifts in gain, firing-rate statistics, or recurrent dynamics, yet do not account for how interareal coordination interacts with local population geometry to constrain downstream population states. We used task engagement, compared to a passive condition, to probe this coordination in Neuropixels recordings spanning the mouse visual thalamocortical-midbrain circuit. Engagement reduced network activity, response participation, and dimensionality across the hierarchy. To account for this circuit-level organization, we developed a theoretical framework in which afferent population geometry interacts with local recurrent dynamics to constrain the accessible dynamics of downstream populations. Across the thalamocortical stages, population-wide afferent statistics predicted downstream activity and dimensionality. At the cortex--midbrain interface, engagement instead reorganized interareal communication geometry. Together, these results identify interareal input geometry as a key constraint on neural population dynamics and uncover a general principle by which behavioral engagement constrains neural state spaces across distributed visual circuits.

neuroscience↗

bioRxiv2025

Transient recurrent dynamics shape representations in mice

Brain networks are high-dimensional and interacting complex systems that exhibit substantial structural heterogeneity as well as temporal variability. Yet, when exposed to a stimulus, their recurrent circuits perform reliable computations. The mechanisms underlying this robustness are, however, still mostly unknown. Here, we combine analyses of Neuropixels recordings of awake, behaving mice with models and theory of recurrent neural networks to identify three core computational characteristics that emerge from the interplay of many network constituents and drive dynamic, reliably classifiable stimulus representations. We find that the level of recurrent inhibition in circuits and the microscopic chaos of dynamics respectively drive mean population responses as well as the within- and across-class stimulus response similarities. These core characteristics in turn non-trivially interact to predict and shape the experimentally observed separability of visual and tactile stimulus representations in mouse superior colliculus. Using these characteristics to assess the information transmitted through the network for multiple stimuli reveals a trade-off in coding space: increasing the number of stimuli conveys more information but also reduces their separability due to their larger overlap in the finitedimensional neuronal space. Our analysis predicts that, only for the experimentally observed small population activity, information keeps increasing with the number of stimuli, revealing another crucial advantage of sparse coding.

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bioRxiv2024

On the validity of electric brain signal predictions based on population firing rates

Neural activity at the population level is commonly studied experimentally through measurements of electric brain signals like local field potentials (LFPs), or electroencephalography (EEG) signals. To allow for comparison between observed and simulated neural activity it is therefore important that simulations of neural activity can accurately predict these brain signals. Simulations of neural activity at the population level often rely on point-neuron network models or firing-rate models. While these simplified representations of neural activity are computationally efficient, they lack the explicit spatial information needed for calculating LFP/EEG signals. Different heuristic approaches have been suggested for overcoming this limitation, but the accuracy of these approaches has not fully been assessed. One such heuristic approach, the so-called kernel method, has previously been applied with promising results and has the additional advantage of being well-grounded in the bio-physics underlying electric brain signal generation. It is based on calculating rate-to-LFP/EEG kernels for each synaptic pathway in a network model, after which LFP/EEG signals can be obtained directly from population firing rates. This amounts to a massive reduction in the computational effort of calculating brain signals because the brain signals are calculated for each population instead of for each neuron. Here, we investigate how and when the kernel method can be expected to work, and present a theoretical framework for predicting its accuracy. We show that the relative error of the brain signal predictions is a function of the single-cell kernel heterogeneity and the spike-train correlations. Finally, we demonstrate that the kernel method is most accurate for the dominating brain signal contributions. We thereby further establish the kernel method as a promising approach for calculating electric brain signals from large-scale neural simulations.

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bioRxiv2022

Signal denoising through topographic modularity of neural circuits

Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We demonstrate that in biologically constrained networks, such a denoising behavior is contingent on recurrent inhibition. We show that this is a robust and generic structural feature that enables a broad range of behaviorally-relevant operating regimes, and provide an in-depth theoretical analysis unravelling the dynamical principles underlying the mechanism.

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bioRxiv2020

Strong coupling and local control of dimensionality across brain areas

The brain contains an astronomical number of neurons, but it is their collective activity that underlies brain function. The number of degrees of freedom that this collective activity explores - its dimensionality - is therefore a fundamental signature of neural dynamics and computation (1-7). However, it is not known what controls this dimensionality in the biological brain - and in particular whether and how recurrent synaptic networks play a role (8-10). Through analysis of high-density Neuropixels recordings (11), we argue that areas across the mouse cortex operate in a sensitive regime that gives these synaptic networks a very strong role in controlling dimensionality. We show that this control is expressed across time, as cortical activity transitions among states with different dimensionalities. Moreover, we show that the control is mediated through highly tractable features of synaptic networks. We then analyze these key features via a massive synaptic physiology dataset (12). Quantifying these features in terms of cell-type specific network motifs, we find that the synaptic patterns that impact dimensionality are prevalent in both mouse and human brains. Thus local circuitry scales up systematically to help control the degrees of freedom that brain networks may explore and exploit.

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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.

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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.

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bioRxiv2019

The covariance perceptron: A new framework for classification and processing of time series in recurrent neural networks

Learning in neuronal networks has developed in many directions, in particular to reproduce cognitive tasks like image recognition and speech processing. Implementations have been inspired by stereotypical neuronal responses like tuning curves in the visual system, where, for example, ON/OFF cells fire or not depending on the contrast in their receptive fields. Classical models of neuronal networks therefore map a set of input signals to a set of activity levels in the output of the network. Each category of inputs is thereby predominantly characterized by its mean. In the case of time series, fluctuations around this mean constitute noise in this view. For this paradigm, the high variability exhibited by the cortical activity may thus imply limitations or constraints, which have been discussed for many years. For example, the need for averaging neuronal activity over long periods or large groups of cells to assess a robust mean and to diminish the effect of noise correlations. To reconcile robust computations with variable neuronal activity, we here propose a conceptual change of perspective by employing variability of activity as the basis for stimulus-related information to be learned by neurons, rather than merely being the noise that corrupts the mean signal. In this new paradigm both afferent and recurrent weights in a network are tuned to shape the input-output mapping for covariances, the second-order statistics of the fluctuating activity. When including time lags, covariance patterns define a natural metric for time series that capture their propagating nature. We develop the theory for classification of time series based on their spatio-temporal covariances, which reflect dynamical properties. We demonstrate that recurrent connectivity is able to transform information contained in the temporal structure of the signal into spatial covariances. Finally, we use the MNIST database to show how the covariance perceptron can capture specific second-order statistical patterns generated by moving digits. Author summaryThe dynamics in cortex is characterized by highly fluctuating activity: Even under the very same experimental conditions the activity typically does not reproduce on the level of individual spikes. Given this variability, how then does the brain realize its quasi-deterministic function? One obvious solution is to compute averages over many cells, assuming that the mean activity, or rate, is actually the decisive signal. Variability across trials of an experiment is thus considered noise. We here explore the opposite view: Can fluctuations be used to actually represent information? And if yes, is there a benefit over a representation using the mean rate? We find that a fluctuation-based scheme is not only powerful in distinguishing signals into several classes, but also that networks can efficiently be trained in the new paradigm. Moreover, we argue why such a scheme of representation is more consistent with known forms of synaptic plasticity than rate-based network dynamics.

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