SUMMARY 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.

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 use longitudinal two-photon Ca2+ imaging alongside paired LFP recordings in the hippocampus and cortex, to show that targeted manipulation of PV+ inhibitory neurons in the lateral visual cortex after daily training selectively attenuates cue-specific reactivations and learning, with only minute effects on spontaneous activity and no apparent effect on normal function such as visual cue-elicited responses during training. In control mice, reactivations were biased toward salient cues, persisted for hours after training had ended, and the prevalence of reactivations was aligned with the learning process. Overall, our results underscore a crucial role for cortical reactivations in memory consolidation.

Perineuronal nets (PNNs) are a condensed form of extracellular matrix primarily found around parvalbumin-expressing (PV+) interneurons. The postnatal maturation of PV+ neurons is accompanied with the formation of PNNs and reduced plasticity. Alterations in PNN and PV+ neuron function have been described for mental disorders such as schizophrenia and autism. The formation of PNNs is highly dependent on aggrecan, a proteoglycan encoded by the ACAN gene, but it remains unknown if it is produced by the PV+ neurons themselves. Thus, we established a knockout (KO) mouse model (ACANflx/PVcre) and an adeno-associated virus to specifically eliminate aggrecan production from PV+ neurons, in the germline or adult animals, respectively. The germline KO (ACANflx/PVcre) eliminated the expression of PNNs labeled by Wisteria floribunda agglutinin (WFA), the most commonly used PNN marker. Surprisingly, electrophysiological properties of PV+ interneurons 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 Chondroitinase ABC 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. These findings suggest that compensatory mechanisms may be activated during development in response to the germline loss of aggrecan. Indeed, qPCR of bulk tissue indicates that other PNN components, including neurocan and tenascin-R, are expressed at higher levels in the KO animals. Finally, behavioral testing revealed that ACANflx/PVcre mice had similar long-term memory as controls in the Morris water maze. However, they employed bolder search strategies during spatial learning and showed lower level of anxiety-related behavior in an open field and zero maze.

Understanding the neural mechanisms of sleep is critical for advancing treatments for neurodegenerative diseases. To investigate these mechanisms, long-term electrophysiological studies are conducted in animal models, where brain activity can be monitored continuously under controlled conditions. However, electrophysiological recordings from freely moving animals are typically performed using a tethered setup, which imposes constraints on natural behaviour. As a first step towards a miniaturised, wireless neural implant, we present a low-power system that enables direct control of the Intan RHD2132 electrophysiology chip using a custom FPGA-based stack. A light-weight yet robust implementation ensures efficient operation while maintaining the necessary flexibility for future integration into a compact, wireless system. The FPGA handles real-time signal processing and data transmission, with support for closed-loop stimulation and onboard data compression. Our system achieves continuous multi-channel recording at 20 kHz, with real-time wireless streaming and long-term data storage. The hardware design prioritises low power consumption, with the aim of enabling extended operation for uninterrupted sleep monitoring over 24 hoursClinical Relevance— This technology enables long-term, high-resolution sleep monitoring, which is essential for understanding the role of sleep in neurodegenerative disorders such as Alzheimer’s and Parkinson’s. By providing continuous, naturalistic brain recordings, this system may help identify early electrophysiological biomarkers of cognitive decline, with potential translational and therapeutics applications.

The primary visual cortex is one of the most well understood regions supporting the processing involved in sensory computation. Following the popularization of high-density neural recordings, it has been observed that the activity of large neural populations is often constrained to low dimensional manifolds. In this work, we quantify the structure of such neural manifolds in the visual cortex. We do this by analyzing publicly available two-photon optical recordings of mouse primary visual cortex in response to visual stimuli with a densely sampled rotation angle. Using a geodesic metric along with persistent homology, we discover that population activity in response to such stimuli generates a circular manifold, encoding the angle of rotation. Furthermore, we observe that this circular manifold is expressed differently in subpopulations of neurons with differing orientation and direction selectivity. Finally, we discuss some of the obstacles to reliably retrieving the truthful topology generated by a neural population.

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. ### Competing Interest Statement The authors have declared no competing interest.

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. ### Competing Interest Statement The authors have declared no competing interest.

The medial entorhinal cortex (MEC) is pivotal in spatial computations and episodic memory. 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 MEC 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. ### Competing Interest Statement The authors have declared no competing interest.

The perineuronal nets (PNNs) are sugar coated protein structures that encapsulate certain neurons in the brain, such as parvalbumin positive (PV) inhibitory neurons. As PNNs are theorized to act as a barrier to ion transport, they may effectively increase the membrane charge-separation distance, thereby affecting the membrane capacitance. Tewari et al. (2018) found that degradation of PNNs induced a 25%-50% increase in membrane capacitance c(m) and a reduction in the firing rates of PV-cells. In the current work, we explore how changes in c(m) affects the firing rate in a selection of computational neuron models, ranging in complexity from a single compartment Hodgkin-Huxley model to morphologically detailed PV-neuron models. In all models, an increased c(m) lead to reduced firing, but the experimentally reported increase in c(m) was not alone sufficient to explain the experimentally reported reduction in firing rate. We therefore hypothesized that PNN degradation in the experiments affected not only c(m), but also ionic reversal potentials and ion channel conductances. In simulations, we explored how various model parameters affected the firing rate of the model neurons, and identified which parameter variations in addition to c(m) that are most likely candidates for explaining the experimentally reported reduction in firing rate.

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 naïve techniques suggesting that methods from causal inference can be useful to disentangle neural interactions in the brain.

Abstract The perineuronal nets (PNNs) are sugar coated protein structures that encapsulate certain neurons in the brain, such as parvalbumin positive (PV) inhibitory neurons. As PNNs are theorized to act as a barrier to ion transport, they may effectively increase the membrane charge-separation distance, thereby affecting the membrane capacitance. Tewari et al. (2018) found that degradation of PNNs induced a 25\%-50\% increase in capacitance and a reduction in the firing rates of PV-cells. In the current work, we attempted to reproduce the experiments by Tewari et al. using a selection of computational neuron models, ranging in complexity from a single compartment Hodgkin-Huxley model to morphologically detailed PV-neuron models. In none of these models, the experimentally reported change in capacitance was sufficient to explain the experimentally reported reduction in firing rate. We therefore hypothesized that PNN degradation in the experiments affected cellular reversal potentials and ion channel conductances in addition to the capacitance. In simulations, we explored how various model parameters affected the firing rate of the model neurons, and identified main candidates for explaining the experiments.

Grid cells are part of a widespread network which 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. It has been suggested that PNNs stabilize synaptic connections and long-term memories, but their role in the grid cell network remains elusive. 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.

Grid cells in the medial entorhinal cortex (MEC) exhibit remarkable spatial activity patterns with spikes coordinated by theta oscillations driven by the medial septal area (MSA). Spikes from grid cells progress relative to the theta phase in a phenomenon called phase precession, which is suggested as essential to create the spatial periodicity of grid cells. Here, we show that optogenetic activation of parvalbumin-positive (PV + ) cells in the MSA enabled selective pacing of local field potential (LFP) oscillations in MEC. During optogenetic stimulation, the grid cells were locked to the imposed pacing frequency but kept their spatial patterns. Phase precession was abolished, and speed information was no longer reflected in the LFP oscillations but was still carried by rate coding of individual MEC neurons. Together, these results support that theta oscillations are not critical to the spatial pattern of grid cells and do not carry a crucial velocity signal.

Hippocampal region CA2 has received increased attention due to its importance in social recognition memory. While its specific function remains to be identified, there are indications that CA2 plays a major role in a variety of situations, widely extending beyond social memory. In this targeted review, we highlight lines of research which have begun to converge on a more fundamental role for CA2 in hippocampus-dependent memory processing. We discuss recent proposals that speak to the computations CA2 may perform within the hippocampal circuit.

To make optimal use of previous experiences, important neural activity sequences must be prioritized during hippocampal replay. Integrating insights about the interplay between CA3 and CA2, we propose a conceptual framework that allows the two regions to control which sequences are reactivated. We suggest that neuromodulatory-gated plasticity and mutual inhibition enable discrete assembly sequences in both regions to support each other while suppressing competing sequences. This perspective provides a coherent interpretation for a variety of seemingly disconnected functional properties of CA2 and paves the way for a more general understanding of CA2.

As experimental neuroscience is moving toward more integrative approaches, with a variety of acquisition techniques covering multiple spatiotemporal scales, data management is becoming increasingly challenging for neuroscience laboratories. Often, datasets are too large to practically be stored on a laptop or a workstation. The ability to query metadata collections without retrieving complete datasets is therefore critical to efficiently perform new analyses and explore the data. At the same time, new experimental paradigms lead to constantly changing specifications for the metadata to be stored. Despite this, there is currently a serious lack of agile software tools for data management in neuroscience laboratories. To meet this need, we have developed Expipe, a lightweight data management framework that simplifies the steps from experiment to data analysis. Expipe provides the functionality to store and organize experimental data and metadata for easy retrieval in exploration and analysis throughout the experimental pipeline. It is flexible in terms of defining the metadata to store and aims to solve the storage and retrieval challenges of data/metadata due to ever changing experimental pipelines. Due to its simplicity and lightweight design, we envision Expipe as an easy-to-use data management solution for experimental laboratories, that can improve provenance, reproducibility, and sharing of scientific projects.

GABA signaling sustains fundamental brain functions, from nervous system development to the synchronization of population activity and synaptic plasticity. Despite these pivotal features, molecular determinants underscoring the rapid and cell-autonomous replenishment of the vesicular neurotransmitter GABA and its impact on synaptic plasticity remain elusive. Here, we show that genetic disruption of the glutamine transporter Slc38a1 in mice hampers GABA synthesis, modifies synaptic vesicle morphology in GABAergic presynapses and impairs critical period plasticity. We demonstrate that Slc38a1-mediated glutamine transport regulates vesicular GABA content, induces high-frequency membrane oscillations and shapes cortical processing and plasticity. Taken together, this work shows that Slc38a1 is not merely a transporter accumulating glutamine for metabolic purposes, but a key component regulating several neuronal functions.

Despite half-a-century of research since the seminal work of Hubel and Wiesel, the role of the dorsal lateral geniculate nucleus (dLGN) in shaping the visual signals is not properly understood. Placed on route from retina to primary visual cortex in the early visual pathway, a striking feature of the dLGN circuit is that both the relay cells (RCs) and interneurons (INs) not only receive feedforward input from retinal ganglion cells, but also a prominent feedback from cells in layer 6 of visual cortex. This feedback has been proposed to affect synchronicity and other temporal properties of the RC firing. It has also been seen to affect spatial properties such as the center-surround antagonism of thalamic receptive fields, i.e., the suppression of the response to very large stimuli compared to smaller, more optimal stimuli. Here we explore the spatial effects of cortical feedback on the RC response by means of a a comprehensive network model with biophysically detailed, single-compartment and multicompartment neuron models of RCs, INs and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY). We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed ('push-pull') and phase-matched ('push-push'), as well as different spatial extents of the corticothalamic projection pattern. Our simulation results support that a phase-reversed arrangement provides a more effective way for cortical feedback to provide the increased center-surround antagonism seen in experiments both for flashing spots and, even more prominently, for patch gratings. This implies that ON-center RCs receive direct excitation from OFF-dominated cortical cells and indirect inhibitory feedback from ON-dominated cortical cells. The increased center-surround antagonism in the model is accompanied by spatial focusing, i.e., the maximum RC response occurs for smaller stimuli when feedback is present.

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 HDF5's complex specification have initiated a discussion for an improved replacement. We propose a novel alternative, the Experimental Directory Structure (Exdir), an open specification 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 easy access to raw data from external applications. Exdir, on 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 specification 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. 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 specification that will serve as many needs as possible and as a foundation for open access to and exchange of data.

OBJECTIVE:A 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.APPROACH:Based 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.MAIN RESULTS:The system robustly visualizes real-time tracking and reliably recovers tracking information recorded from a range of sampling frequencies (30-1000 Hz). 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.SIGNIFICANCE:Expanding 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.