Neuronal populations

Support large-scale molecular profiling of neuronal populations and synapses in the Brain Proteome Organization team. Contribute to experimental workflows studying how proteins and their interactions shape synaptic physiology and brain-circuit connectivity.

…It asks how expanded neuronal populations and cell-type diversity support the behaviour of social species such as ants. Mechanisms in insect mushroom bodies are compared with changes in the mammalian cortex.

…about visual information encoded by neuronal populations.

Mean-field models of neuronal populations in the brain have proven extremely useful to understand network dynamics and response to stimuli, but these models generally lack a faithful description of the fluctuations in the biologically relevant case of finite network size and connection probabilities $p<1$ (non-full connectivity). To gain insight into the mechanisms underlying the response and variability of the dynamics of populations of spiking neurons, we derive here a nonlinear stochastic mean-field model for a network of spiking Poisson neurons with quenched random connectivity. We treat

Understanding the functional consequences of synaptic heterogeneity within neuronal populations is a critical challenge in neuroscience. Recent connectomic reconstructions, such as the complete fly brain connectome, provide key insights into the structural constraints on neural networks. One prominent observation from these datasets is the variability in synaptic connection strengths among functionally similar neurons, where certain neurons exhibit significantly more synapses than others. However, the role of this heterogeneity in neural function remains unclear. To address this, we present a

Real-world perception is thought to arise from the coordinated activity of neuronal populations reflecting a combination of sensory inputs, existing knowledge, and contextually modulated expectations. We hypothesize that this integration is realized in the temporal dynamics of perceptual population responses. Here, we use latent-space modeling and dynamical systems analysis to characterize activity across populations of simultaneously recorded neurons in a secondary auditory forebrain region of songbirds (European starlings), engaged in an expectation-cued song-syllable categorization task. We

Understanding how neuronal populations interact to process information and generate behaviour is a central goal of neuroscience. This can be challenging due to the high dimensionality of the neuronal populations, the dense interactions between them and the unobserved factors that influence the dynamics. The neuronal manifold hypothesis tackles high dimensionality by positing that the relevant dynamics occurs on a lower-dimensional manifold. While the neuronal manifold may be useful in visualising global dynamics, it tells us very little about how various subsystems interact to produce these dy

Synaptic and representational drift describe the dynamic changes in neuronal connections and activity over time. However, the relationship between these two processes remains elusive. Here, we integrated representational dynamics and synaptic drift into a firing rate model to explore the influence of synaptic drift on representational drift.In mouse auditory cortex, neuronal populations respond to stimuli in a clustered manner (Aschauer et al., 2022). Our model, comprising inhibitory and excitatory neurons, reproduces these response patterns, especially under inhibition-dominated conditions. W

…assays.Our analysis revealed various neuronal populations with specific subclusters that gradually decreased in complexity over time, while astrocyte populations became more prominent. We hy

…nAChRs are expressed by different neuronal populations with different effects on behavior. To better understand functional role of beta2* nAChRs, we need to know which neuronal types in the PFC express them and what is the effect of these receptors on behavior. In the present project, we used FISH to map the expressi

Light-Sheet Fluorescence Microscopy (LSFM) is an excellent tool to study neuronal populations in large samples such as the whole mouse brain, thanks to its inherent three-dimensional nature and its high resolution down to the single cell. In this work we report on our latest technological developments in both hardware and software and their applications in several projects aiming at reconstructing the neuronal organization in both the human cortex and in whole mouse brain.Our latest LSFM setup is capable of simultaneous excitation and acquisition at four wavelengths, thus enabling fast reconst

…Previously, we identified populations of basolateral amygdala (BLA) neurons in the freely-moving rat that were highly tuned to distinct naturalistic stimuli such as male or female conspecifics and food. These neuronal populations showed stable reactivation across multiple presentations of stimuli in the same environment. To understand whether experience can modify this social representation, we recorded from large populations of ne

Spatiotemporal properties of motor cortex neuronal activity have been extensively studied, with the interpretations of voluntary movement mechanisms being the key issue. Two competing models, representational and dynamical, explain the conversion of neuronal activity into limb movements. The dynamical model uses the joint PCA method, which holistically characterizes neuronal oscillations by maximizing data rotational features. Different interpretations tried to explain the presence of rotational dynamics in neuronal patterns (Fig. 1a, c). Yet, their exact nature remains poorly understood.Here

…measured the activity of neuronal populations in the retrosplenial cortex (RSC) using two-photon calcium imaging during the performance of a context-choice association task. We find that the activity

…of effective interactions in large neuronal populations. We show that the optimal network -- in a precise information-theoreti

…study the representational geometry of neuronal populations. Though derived from individual neuronal selectivity,

Systems neuroscience experiments generate multimodal data streams, including neuronal activity, stimuli, movement, and behavior events. In analysis, it can be beneficial to avoid hypothesis-driven models and instead take a data-driven approach. For this, we need a general purpose framework to handle the diverse data, and identify relationships across space, time, and modalities in a computationally efficient manner. In this work, we introduce such a framework. Our model, called the Neuroformer, can simulate neuronal populations, and jointly process an arbitrary number of additional modalities

…The finite size of neuronal populations and synaptic interactions is a core determinant of neural dynamics, being responsible for non-zero correlation in the spiking activity and

The origins, mechanisms and consequences of epilepsy in the developing brain are incompletely understood. Many developmental epilepsies have a genetic basis and their mechanisms stem from deficits in the function of one or numerous genes. Others, such as those that follow prolonged febrile seizures or severe birth asphyxia in a ‘normal’ brain may depend on the interaction of the insult with the rapidly evolving brain cells and circuits. Yet, how early-life insults may provoke epilepsy is unclear, and requires multiple levels of analysis: behavior, circuits, cells [neurons, glia] and molecules.

How psychedelic drugs change the activity of cortical neuronal populations is not well understood. It is also not clear which changes are specific to transition into the psychedelic brain state and which are shared with other brain state transitions. Here, we used Neuropixels probes to record from large populations of neurons in prefrontal cortex of mice given the psychedelic drug TCB-2. The primary effect of drug ingestion was stretching of the distribution of log firing rates of the recorded population. This phenomenon was previously observed across transitions between sleep and wakefulness,

The brain exhibits a rich repertoire of oscillatory patterns organized in space, time and frequency. However, despite ever more-detailed characterizations of spectrally-resolved network patterns, the principles governing oscillatory activity at the system-level remain unclear. Here, we propose that the transient emergence of spatially organized brain rhythms are signatures of weakly stable synchronization between subsets of brain areas, naturally occurring at reduced collective frequencies due to the presence of time delays. To test this mechanism, we build a reduced network model representing

Technological advances have increased the availability of recordings from large populations of neurons across multiple brain areas. Coupling these recordings with dimensionality reduction techniques, recent work has led to new proposals for how populations of neurons can send and receive signals selectively and flexibly. Advancement of these proposals depends, however, on untangling the bidirectional, parallel communication between neuronal populations. Because our current data analytic tools struggle to achieve this task, we have recently validated and presented a novel dimensionality reducti

…models with a single or a handful of neuronal populations consisting of identical neurons with statistically identical connection properties. However, real cortical networks have heterogeneous neural populations and much more structured synaptic connections. Unlike typical simplified cortical network models, real network