Biology subjects

Fyhn, M.

Publications and source records attributed to Fyhn, M..

16 recordsLinked to original sources

bioRxiv2026

Large-scale volumetric two-photon calcium imaging enables cellular-resolution mesoscopic mapping of marmoset cortical areas

Primate neocortex is organized into functionally defined domains. Understanding how the functional organization within each domain emerges from single-cell tuning properties has been hampered by the lack of imaging tools that can capture large-scale activity at different depths of the scattering primate brain at cellular resolution and physiological timescales. Here, we demonstrate mesoscopic and volumetric cellular-resolution calcium imaging of entire cortical areas in the marmoset brain at multi-Hertz rates by combining fluorescence lifetime-limited temporally multiplexed two-photon recording based on Light Beads Microscopy (LBM) with optimized calcium indicator targeting. Using this approach, we perform volumetric recording of neuronal population activity within cortical volumes spanning up to 4 x 4 x 0.3 mm3 capturing the simultaneous activity of over 96,000 cells, including more than 46,000 stimulus-responsive neurons. In auditory cortex, we found fine-grain heterogeneous tuning of individual neurons to frequencies within broader tonotopic gradients. In the middle temporal area (MT) we found direction tuning along functional continua rather than discrete categories, encompassing uni-, bi-, and pandirectional tuning, where cells with similar tuning tend to cluster. Tuning direction follows a columnar organization with a lateral periodicity of [~]500 {micro}m and low variability across depth. Columns are organized by a pinwheel structure. Noise correlations between cells, revealing functional interactions, decay isotropically and monotonically with distance but remain significant even at lateral distances exceeding 3 mm and thus the scale of multiple columns. Together, these discoveries reveal the need for neurotechnologies bridging scales from single cells to multi-columnar systems. The technological advancements open the door for future large-scale, cellular-resolution investigations of functional organization and population dynamics across entire cortical areas in the primate brain.

neuroscience↗

bioRxiv2026

Raster photostimulation of large-scale neural populations

Neural computations are implemented by distributed neural populations that typically span multiple brain areas. Simultaneous recording and photo-activation experiments can reveal the structure and logic of these neural computations, but such methods are typically limited to small subsets of neurons in restricted fields of view. Here we describe a new system called raster photostimulation for activating and recording thousands of neurons, over a short 300 ms time window and over a large 5 mm field-of-view on a two-photon mesoscope. The photo-activation is precisely matched to the neural recording configuration, as it uses the same optical path, although with a different laser that is independently gated. We demonstrate pixel-level precision, frame-by-frame mask updating, and single-frame photostimulation of thousands of neurons. While this method lacks the precise temporal control of alternative methods, it compensates with ease-of-use, spatial precision, cost of implementation and by pushing the limits on the number of near-simultaneously stimulated neurons.

neuroscience↗

bioRxiv2026

Perineuronal nets reflect a continuum of fast-spiking specialization in adult parvalbumin interneurons

Perineuronal nets (PNNs) preferentially enwrap parvalbumin (PV) interneurons, regulating plas-ticity and circuit function. The molecular differences between PNN-positive and PNN-negative PV neurons remain unknown. We combined Xenium spatial transcriptomics with PNN-labeling in adult mouse cortex (378,349 cells) and found that 97% of PNNs enwrap PV neurons; sub-stantially higher than previous estimates. PNN status reflected a transcriptional continuum rather than discrete subtypes. A classifier trained on Xenium data (AUC = 0.87) applied to Allen Brain scRNA-seq data, enabled genome-wide analysis of 34,326 cortical PV neurons. PNN-positive PV neurons expressed mature fast-spiking markers: Kv3 channels, mature NMDA subunits (Grin2a), fast-kinetic GABA-A receptors (Gabra1), oxidative phosphorylation genes, and gap junctions (Gjd2). PNN-negative PV neurons expressed neuropeptides and GABA-A subunits typical of Sst interneurons, suggesting a transcriptomic boundary between cell types and potentially ele-vated plasticity. This establishes PNN status as a molecular correlate of PV cell specialization, with implications for therapeutic strategies targeting cortical plasticity.

neuroscience↗

bioRxiv2025

Spatial predictive coding in visual cortical neurons

Predictive coding is a theoretical framework that can explain how animals build internal models of their sensory environments by predicting sensory inputs. Predictive coding may capture either spatial or temporal relationships between sensory objects. While the original theory by Rao and Ballard, 1999 described spatial predictive coding, much of the recent experimental data has been interpreted as evidence for temporal predictive coding. Here we directly tested whether the "mismatch" neural responses in sensory cortex are due to a spatial or a temporal internal model. We adopted two common paradigms to study predictive coding: one based on virtual-reality and one based on static images. After training mice with repeated visual stimulation for several days, we performed multiple manipulations, including: 1) we introduced a novel stimulus, 2) we replaced a stimulus with a novel gray wall, 3) we duplicated a trained stimulus, or 4) we altered the order of the stimuli. The first two manipulations induced a substantial mismatch response in neural populations of up to 20,000 neurons recorded across primary and higher-order visual cortex, while the third and fourth ones did not. Thus, a mismatch response only occurred if a new spatial - not temporal - pattern was introduced.

neuroscience↗

bioRxiv2024

Task and Behavior-Related Variables Are Encoded by the Postrhinal and Medial Entorhinal Cortex During Non-Spatial Associative Learning

The medial entorhinal cortex (MEC) is pivotal in spatial computations and episodic memory. In particular, an animals position can be decoded from the activity of entorhinal grid cells. However, it remains elusive whether MEC could play a more general role in different types of associative learning and how the representations develop during the learning process. It has been shown that the postrhinal cortex (POR), which is directly connected to MEC, integrates visual stimuli with salient outcomes. Here, we use a non-spatial visual association task to investigate whether MEC neurons represent low-level visual cues during learning. Using a Go/NoGo visual association task, we recorded neural activity in MEC and POR throughout the learning phase as mice associated drifting gratings with rewarded, aversive, or neutral outcomes. Our findings reveal that the neural tuning curves in both the POR and MEC change with the learning of the task. From the start of training, the POR neurons exhibited response tuning to the visual cues, and the tuning was stable to cue orientations during learning. In contrast, MEC neurons did not initially respond very strongly to visual cues but developed a robust tuning toward the rewarded trials. While the MECs representation of visual information was limited, it encoded other task elements. A large fraction of the neurons formed distinct functional clusters that were either activated or suppressed by reward-related behavior. Remarkably, these clusters segregated anatomically in MEC and maintained strong within-cluster correlations before and after training. Notably, although the same functional clusters were apparent in the POR, they did not show any anatomical structure as in the MEC. Task reversal induced significant changes in network responses across both regions, with a decrease in overall task-responsive neurons but a slight increase in stimulus representation. Strikingly, information about the choice to lick emerged with learning in both brain areas, and most significantly within the functional cell clusters representing reward consumption and plus-cue stimulus. Our results demonstrate that although neurons in MEC and POR develop behavior-modulated tuning during learning of a non-spatial visual association task, the MEC exhibits stronger within-cluster correlations and anatomical organization. Conversely, the POR population exhibits less structural organization and more specific stimulus-tuning, which is reflective of being a higher visual association area. Our findings reveal that the MEC can encode task- and behavior-related variables beyond spatial information.

neuroscience↗

bioRxiv2024

Cortical Reactivations Modulated by Local InhibitoryCircuits Mediate Memory Consolidation

Highly salient events activate neurons across various brain regions. During subsequent rest or sleep, the activity patterns of these neurons often correlate with those observed during the preceding experience. Growing evidence suggests that these reactivations play a crucial role in memory consolidation, the process by which experiences are solidified in cortical networks for long-term storage. Here, we demonstrate that reactivations in the lateral visual cortex are vital for the consolidation of visual association learning. By employing longitudinal two-photon Ca2+ imaging alongside paired LFP recordings in the hippocampus and cortex, we show that targeted manipulation of PV+ inhibitory neurons in the lateral visual cortex after daily training selectively attenuated cue-specific reactivations and learning, with no apparent effect on normal network function during training. In contrast, reactivations in the control group were biased towards salient cues, aligned with learning process and persisted for hours after training had ended. Overall, our results underscore a crucial role for cortical reactivations in memory consolidation.

neuroscience↗

bioRxiv2024

Deciphering the Role of Aggrecan in Parvalbumin Interneurons: Unexpected Outcomes from a Conditional ACAN Knockout That Eliminates WFA+ Perineuronal Nets

The transition from juvenile to adult is accompanied by the maturation of inhibitory parvalbumin-positive (PV+) neurons and reduced plasticity. This transition involves the formation of perineuronal nets (PNNs), a dense configuration of the extracellular matrix that predominantly envelops parvalbumin-positive (PV+) neurons. Aggrecan, a proteoglycan encoded by the ACAN gene, has been shown to have a key role in the PNNs as knock-out of ACAN in the adult brain reactivates juvenile plasticity, but the contribution of different cell populations is unknown Here, we establish and characterize a mouse model in which ACAN is selectively knocked out (KO) in PV+ neurons (ACANflx/PVcre). Moreover, we develop a viral tool to perform similar cell-type directed KO in adult mice. Both models are compared with the traditional method of PNN removal, namely enzymatic degradation of PNNs with Chondroitinase ABC (chABC). We show that PV+ neurons in adult ACANflx/PVcre mice do not produce PNNs that are labeled by Wisteria floribunda agglutinin (WFA), the most commonly used PNN marker. Surprisingly, electrophysiological properties of PV+ interneurons in the visual cortex (V1) and ocular dominance plasticity of adult ACANflx/PVcre mice were similar to controls. In contrast, AAV-mediated ACAN knockout in adult mice increased ocular dominance plasticity. Moreover, in vivo chABC treatment of KO mice resulted in reduced firing rate of PV+ cells and increased frequency of spontaneous excitatory postsynaptic currents (sEPSC), a phenotype associated with chABC treatment of WT animals. This suggests compensatory mechanisms in the germline KO. Indeed, qPCR of bulk tissue indicates that other PNN components are expressed at higher levels in the KO animals. Finally, we perform memory-and behavioral testing to see if the lack of ACAN from PV+ neurons throughout development affected stereotypic behaviors and memory processing. ACANflx/PVcre mice have learning and memory abilities similar to controls, but use bold search strategies during navigation in the Morris water maze. The low level of anxiety-related behavior is confirmed in an open field and zero maze, where they spent nearly twice the time in open areas.

neuroscience↗

bioRxiv2024

Hexagons all the way down: Grid cells as a conformal isometric map of space

The brains ability to navigate is often attributed to spatial cells in the hippocampus and entorhinal cortex. Grid cells, found in the entorhinal cortex, are known for their hexagonal spatial activity patterns and are traditionally believed to be the neural basis for path integration. However, recent studies have cast grid cells as a distance-preserving representation. We further investigate this role in a model of grid cells based on a superposition of plane waves. In a module of such grid cells, we optimise their phases to form a conformal isometry (CI) of two-dimensional flat space. With this setup, we demonstrate that a module of at least seven grid cells can achieve a CI, with phases forming a regular hexagonal arrangement. This pattern persists when increasing the number of cells, significantly diverging from a random uniform distribution. In particular, when optimised for CI, the phase distribution becomes distinctly regular and hexagonal, offering a clear experimentally testable prediction. Moreover, grid modules encoding a CI maintain constant energy expenditure across space, providing a new perspective on the role of energy constraints in normative models of grid cells. Finally, we investigate the minimum number of grid cells required for various spatial encoding tasks, including a unique representation of space, the population activity forming a torus, and achieving a CI, where we find that all three are achieved when the module encodes a CI. Our study not only underscores the versatility of grid cells beyond path integration but also highlights the importance of geometric principles in neural representations of space.

neuroscience↗

bioRxiv2023

Competition and Cooperation of Assembly Sequences in Recurrent Neural Networks

Neural activity sequences are ubiquitous in the brain and play pivotal roles in functions such as long-term memory formation and motor control. While conditions for storing and reactivating individual sequences have been thoroughly characterized, it remains unclear how multiple sequences may interact when activated simultaneously in recurrent neural networks. This question is especially relevant for weak sequences, comprised of fewer neurons, competing against strong sequences. Using a non-linear rate model with discrete, pre-configured assemblies, we demonstrate that weak sequences can compensate for their competitive disadvantage either by increasing excitatory connections between subsequent assemblies or by cooperating with other co-active sequences. Further, our model suggests that such cooperation can negatively affect sequence speed unless subsequently active assemblies are paired. Our analysis, validated by an analytically tractable linear approximation, characterizes the conditions for successful sequence progression in isolated, competing, and cooperating sequences, and identifies the distinct contributions of recurrent and feed-forward projections. This proof-of-principle study shows how even disadvantaged sequences can be prioritized for reactivation, a process which has recently been implicated in hippocampal memory processing.

neuroscience↗

bioRxiv2021

Functional characterisation of single nucleotide variants of the psychiatric risk gene cacna1c in the zebrafish

Several genome-wide association studies have associated CACNA1C variants with psychiatric disorders. The molecular mechanisms involved are poorly understood. Taking advantage of the zebrafish larva as a model, we investigated how two different mutations in cacna1c - sa10930 (nonsense mutation) and sa15296 (splice site mutation), affect neuronal function. We characterized changes in cacna1c mRNA, neurotransmitter levels and behaviour, as well as whole-brain activity using single electrode local field potential recordings. Both point mutations resulted in a significant reduction in cacna1c mRNA, as well as social behaviour and prepulse inhibition deficits. Whereas sa15296 mutants displayed abnormal locomotor and open-field behaviour, we observed normal behaviour in the sa10930 mutants. Brain recordings from both mutants had lower spectral power while sa15296 displayed significant seizure-like activity. Finally, sa10930 homozygotes showed increased dopamine and serotonin levels, decreased gamma-aminobutyric acid (GABA) levels, and unchanged glutamate levels while homozygous sa15296 larvae showed increased levels of serotonin and glutamate, and unaffected levels of GABA and dopamine. Our work provides new insights into the functional role of CACNA1C in behavioural, electrophysiological and biochemical traits linked to psychiatric disorders. We show a functional role for the non-coding mutation (sa15296) in the cacna1c in vivo animal model. Consistent with existing hypotheses, our data suggest that disruption of gene expression, neurotransmission, and cortical excitability are involved in CACNA1C-related mechanisms of psychiatric disorders.

neuroscience↗

bioRxiv2021

An updated suite of viral vectors for in vivo calcium imaging using local and retro-orbital injections

Calcium imaging using genetically encoded Ca2+ indicators (GECIs) is a widely adopted method to measure neural activity in modern neuroscience. Here, we explore the use of systemically administered viral vectors for brain-wide expression of GECIs, and adapt novel GECIs to optimize signal-to-noise. We show that systemic injections of PHP.eB AAVs to express GECIs is a highly promising technique for imaging neural activity and circumvent the need for transgenic GECI expressing mouse lines. We also establish the use of soma-targeted GECIs that outperform current Ca2+ indicators using both systemic and local virus injections.

neuroscience↗

bioRxiv2019

Perineuronal nets stabilize the grid cell network

Grid cells are part of a widespread network that supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. To reveal the relationship between stable spatial representations and the presence of PNNs we recorded from populations of neurons in adult rats. We show that removal of PNNs leads to lower inhibitory spiking activity, and reduces grid cells ability to create stable representations of a novel environment. Furthermore, in animals with disrupted PNNs, exposure to a novel arena corrupted the spatiotemporal relationships within grid cell modules, and the stored representations of a familiar arena. Finally, we show that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons. Together this work suggests that PNNs provide a key stabilizing element for the grid cell network.

neuroscience↗

bioRxiv2018

Inferring causal connectivity from pairwise recordings and optogenetics

To understand the neural mechanisms underlying brain function, neuroscientists aim to quantify causal interactions between neurons, for instance by perturbing the activity of neuron A and measuring the effect on neuron B. Recently, manipulating neuron activity using light-sensitive opsins, optogenetics, has increased the specificity of neural perturbation. However, using widefield optogenetic interventions, multiple neurons are usually perturbed, producing a confound - any of the stimulated neurons can have affected the postsynaptic neuron making it challenging to discern which neurons produced the causal effect. Here, we show how such confounds produce large biases in interpretations. We explain how confounding can be reduced by combining instrumental variables (IV) and difference in differences (DiD) techniques from econometrics. Combined, these methods can estimate (causal) effective connectivity by exploiting the weak, approximately random signal resulting from the interaction between stimulation and the absolute refractory period of the neuron. In simulated neural networks, we find that estimates using ideas from IV and DiD outperform naive techniques suggesting that methods from causal inference can be useful to disentangle neural interactions in the brain.

neuroscience↗

bioRxiv2018

Open source modules for tracking animal behavior and closed-loop stimulation based on Open Ephys and Bonsai

ObjectiveA major goal in systems neuroscience is to determine the causal relationship between neural activity and behavior. To this end, methods that combine monitoring neural activity, behavioral tracking, and targeted manipulation of neurons in closed-loop are powerful tools. However, commercial systems that allow these types of experiments are usually expensive and rely on non-standardized data formats and proprietary software which may hinder user-modifications for specific needs. In order to promote reproducibility and data-sharing in science, transparent software and standardized data formats are an advantage. Here, we present an open source, low-cost, adaptable, and easy to set-up system for combined behavioral tracking, electrophysiology and closed-loop stimulation.\n\nApproachBased on the Open Ephys system (www.open-ephys.org) we developed multiple modules to include real-time tracking and behavior-based closed-loop stimulation. We describe the equipment and provide a step-by-step guide to set up the system. Combining the open source software Bonsai (bonsai-rx.org) for analyzing camera images in real time with the newly developed modules in Open Ephys, we acquire position information, visualize tracking, and perform tracking-based closed-loop stimulation experiments. To analyze the acquired data we provide an open source file reading package in Python.\n\nMain resultsThe system robustly visualizes real-time tracking and reliably recovers tracking information recorded from a range of sampling frequencies (30-1000Hz). We combined electrophysiology with the newly-developed tracking modules in Open Ephys to record place cell and grid cell activity in the hippocampus and in the medial entorhinal cortex, respectively. Moreover, we present a case in which we used the system for closed-loop optogenetic stimulation of entorhinal grid cells.\n\nSignificanceExpanding the Open Ephys system to include animal tracking and behavior-based closed-loop stimulation extends the availability of high-quality, low-cost experimental setup within standardized data formats serving the neuroscience community.

neuroscience↗

bioRxiv2018

Experimental Directory Structure (Exdir): An alternative to HDF5 without introducing a new file format

Natural sciences generate an increasing amount of data in a wide range of formats developed by different research groups and commercial companies. At the same time there is a growing desire to share data along with publications in order to enable reproducible research. Open formats have publicly available specifications which facilitate data sharing and reproducible research. Hierarchical Data Format 5 (HDF5) is a popular open format widely used in neuroscience, often as a foundation for other, more specialized formats. However, drawbacks related to HDF5s complex specification have initiated a discussion for an improved replacement. We propose a novel alternative, the Experimental Directory Structure (Exdir), an open standard for data storage in experimental pipelines which amends drawbacks associated with HDF5 while retaining its advantages. HDF5 stores data and metadata in a hierarchy within a complex binary file which, among other things, is not human-readable, not optimal for version control systems, and lacks support for storing raw data. Exdir, one the other hand, uses file system directories to represent the hierarchy, with metadata stored in human-readable YAML files, datasets stored in binary NumPy files, and raw data stored directly in subdirectories. Furthermore, storing data in multiple files makes it easier to track for version control systems. Exdir is not a file format in itself, but a standard for organizing files in a directory structure. Exdir uses the same abstractions as HDF5 and is compatible with the HDF5 Abstract Data Model. Several research groups are already using data stored in a directory hierarchy as an alternative to HDF5, but no common standard exists in the field. This complicates and limits the opportunity for data sharing and development of common tools for reading, writing, and analyzing data. Exdir facilitates improved data storage, data sharing, reproducible research, and novel insight from interdisciplinary collaboration. With the publication of Exdir, we invite the scientific community to join the development to create an open standard that will serve as many needs as possible and that will serve as a foundation for open access to and exchange of data.\n\nSIGNIFICANCE STATEMENTExperimental Directory Structure (Exdir) is a proposal to standardize a storage solution that has become an increasingly popular alternative to Hierarchical Data Format 5 (HDF5), namely to use directories to define a hierarchy, store data in binary files, and metadata in text files. While this strategy is deployed locally by several research groups, no common standard exists. We envision the establishment of such a standard and present Exdir to the community as a starting point.

bioinformatics↗

bioRxiv2018

Firing-Rate Based Network Modeling of the dLGN Circuit: Effects of Cortical Feedback on Spatiotemporal Response Properties of Relay Cells

Visual signals originating in the retina pass through the dorsal geniculate nucleus (dLGN), the visual part of thalamus, on the way to the visual cortex. This is however not a simple feedforward flow of information: there is a significant feedback from cortical cells back to both relay cells and interneurons in the dLGN. Despite four decades of experimental and theoretical studies, the functional role of this feedback is still debated. Here we use a firing-rate model, the extended difference-of-gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. For this model the responses are found by direct evaluation of two- or three-dimensional integrals allowing for fast and comprehensive studies of putative effects of different candidate organizations of the cortical feedback. Our analysis identifies a special mixed configuration of excitatory and inhibitory cortical feedback which seems to best account for available experimental data. This configuration consists of a slow (long-delay) and spatially widespread inhibitory feedback, combined with a fast (short-delayed) and spatially narrow excitatory feedback, where (iii) the excitatory/inhibitory ON-ON connections are accompanied respectively by inhibitory/excitatory OFF-ON connections, i.e. following a phase-reversed arrangement. The recent development of optogenetic and pharmacogenetic methods has provided new tools for more precise manipulation and investigation of the thalamocortical circuit, in particular for mice. Such data will expectedly allow the eDOG model to be better constrained by data from specific animal model systems than has been possible until now for cat. We have therefore made the Python tool pyLGN which allows for easy adaptation of the eDOG model to new situations.\n\nAuthor SummaryOn route from the retina to primary visual cortex, visually evoked signals have to pass through the dorsal lateral geniculate nucleus (dLGN). However, this is not an exclusive feed forward flow of information as feedback exists from neurons in the cortex back to both relay cells and interneurons in the dLGN. The functional role of this feedback remains mostly unresolved. Here, we use a firing-rate model, the extended difference-of-gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. Our analysis indicates that a particular mix of excitatory and inhibitory cortical feedback agrees best with available experimental observations. In this configuration ON-center relay cells receive both excitatory and (indirect) inhibitory feedback from ON-center cortical cells (ON-ON feedback) where the excitatory feedback is fast and spatially narrow while the inhibitory feedback is slow and spatially widespread. In addition to the ON-ON feedback, the connections are accompanied by OFF-ON connections following a so-called phase-reversed (push-pull) arrangement. To facilitate further applications of the model, we have made the Python tool pyLGN which allows for easy modification and evaluation of the a priori quite general eDOG model to new situations.

neuroscience↗