Some neurons are surrounded by a dense sugar coat, perineuronal nets (green) which limits plasticity in the adult brain. Bottom row show the development of the nets from postnatal day 12 to 30 in entorhinal cortex. Synapses on the cell body are located in the holes in the nets (Christensen, Lensjø et al., 2017).
Perineuronal nets stabilize long-term memories (Thompson et al., PNAS 2018). Perineuronal nets stabilize the grid cell network. (Christensen et al., Nature Communications 2021).
The medial entorhinal cortex (MEC) is pivotal in spatial computations and episodic memory. In particular, an animal’s 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 MEC’s 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.
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.
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.
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.
Beshkov, Kosio; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute
(2024).
Topological structure of population activity in mouse visual cortex encodes densely sampled stimulus rotations.
iScience.
27(4).
doi: 10.1016/j.isci.2024.109370.
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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.
Lepperød, Mikkel Elle; Stöber, Tristan Manfred; Hafting, Torkel; Fyhn, Marianne & Kording, Konrad Paul
(2023).
Inferring causal connectivity from pairwise recordings and optogenetics.
PLoS Computational Biology.
ISSN 1553-734X.
19(11).
doi: 10.1371/journal.pcbi.1011574.
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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.
Beshkov, Kosio; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute Tomas
(2023).
Topological Structure of Population Activity in Mouse Visual Cortex Encodes Densely Sampled Visual Scene Rotations.
Social Science Research Network (SSRN).
doi: 10.2139/ssrn.4495668.
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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 cm and a reduction in the firing rates of PV-cells. In the current work, we explore how changes in cm 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 cm lead to reduced firing, but the experimentally reported increase in cm 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 cm, 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 cm that are most likely candidates for explaining the experimentally reported reduction in firing rate.
Olsen, Elise Reppe; Fjeldstad, Maria Perona; Thon, Sarah Kriebel; Schøyen, Vemund Sigmundson; Lepperød, Mikkel Elle & Sugar, Jørgen Afseth
[Show all 8 contributors for this article](2024).
Exploring social and spatial coding in the lateral and medial entorhinal cortex.
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Fjeldstad, Maria Perona; Lepperød, Mikkel Elle; Schøyen, Vemund Sigmundson; Christensen, Ane Charlotte; Grødem, Sverre & Fyhn, Marianne
[Show all 7 contributors for this article](2024).
Theta modulation of grid cells in the medial entorhinal cortex in a novel environment task.
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Beshkov, Kosio; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute Tomas
(2023).
Topological Structure of Population Activity in Mouse Visual Cortex Encodes Densely Sampled Visual Scene Rotations.
Full text in Research Archive
Beshkov, Kosio; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute Tomas
(2023).
Topological Structure of Population Activity in Mouse Visual Cortex Encodes Densely Sampled Visual Scene Rotations.
Full text in Research Archive
Mammals show remarkable flexibility of behaviors, and have the ability to abstract specific rules and learning sets into generalized low dimensional structures that can be applied in new conditions. Recent work suggests that this generalization could take place in parahippocampal regions, in particular in the entorhinal cortex (EC). Here, the medial part (MEC) seems to extract the structure of state space irrespective of modality (e.g. spatial or sound). The lateral part (LEC) receives input about complex features of objects and multimodal sensory information from association cortices, including the postrhinal cortex (POR). This puts the EC in a unique position to function as an integration center for higher order representations. To investigate the role of the entorhinal cortex in abstracting and generalizing task content, we have established a set of simple association tasks for mice that enable both rule switching and change of sensory modality. We train mice in these tasks over multiple sessions and record large neuronal populations in the EC during and after training using two-photon imaging. To this end, we utilize custom right-angle prism assemblies that cover either the MEC, or POR and LEC. We hypothesize that both MEC and LEC will display learning-induced changes in the low dimensional neural representations over time, with consistent structures that are used and transferred across different tasks. Our preliminary data from mice trained in a visual association task indicate that neurons in both regions develop representations of task structure with training.
Annavini, Eis; Korchynska, Solomiia; Babursah, Ela; Hafting, Torkel & Boccara, Charlotte
(2022).
A benchmark for sharp wave-ripple detection algorithms.
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Lepperød, Mikkel Elle; Stöber, Tristan; Hafting, Torkel; Fyhn, Marianne & Kording, Konrad
(2022).
Inferring causal connectivity from pairwise recordings and optogenetics.
BioRxiv.
ISSN 0362-4331.
doi: 10.1101/463760.
Full text in Research Archive
Annavini, Eis; Korchynska, Solomiia; Babursah, Ela; Hafting, Torkel & Boccara, Charlotte
(2022).
A benchmark for sharp wave-ripple detection algorithms.
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