Topological structure of population activity in mouse visual cortex encodes densely sampled stimulus rotations.

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.

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

Responses in fast-spiking interneuron firing rates to parameter variations associated with degradation of perineuronal nets.

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 [Formula: see text] and a reduction in the firing rates of PV-cells. In the current work, we explore how changes in [Formula: see text] 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 [Formula: see text] lead to reduced firing, but the experimentally reported increase in [Formula: see text] 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 [Formula: see text], 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 [Formula: see text] that are most likely candidates for explaining the experimentally reported reduction in firing rate.

Optogenetic pacing of medial septum parvalbumin-positive cells disrupts temporal but not spatial firing in grid cells.

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.

CA2 beyond social memory: Evidence for a fundamental role in hippocampal information processing.

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.

Perineuronal nets stabilize the grid cell network.

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.

Selective neuromodulation and mutual inhibition within the CA3-CA2 system can prioritize sequences for replay.

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.

Experimental Pipeline (Expipe): A Lightweight Data Management Platform to Simplify the Steps From Experiment to Data Analysis.

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.

The Glutamine Transporter Slc38a1 Regulates GABAergic Neurotransmission and Synaptic Plasticity.

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.

Aggrecan Directs Extracellular Matrix-Mediated Neuronal Plasticity.

In the adult brain, the extracellular matrix (ECM) influences recovery after injury, susceptibility to mental disorders, and is in general a strong regulator of neuronal plasticity. The proteoglycan aggrecan is a core component of the condensed ECM structures termed perineuronal nets (PNNs), and the specific role of PNNs on neural plasticity remains elusive. Here, we genetically targeted the Acan gene encoding for aggrecan using a novel animal model. This allowed for conditional and targeted loss of aggrecan in vivo, which ablated the PNN structure and caused a shift in the population of parvalbumin-expressing inhibitory interneurons toward a high plasticity state. Selective deletion of the Acan gene in the visual cortex of male adult mice reinstated juvenile ocular dominance plasticity, which was mechanistically identical to critical period plasticity. Brain-wide targeting improved object recognition memory.SIGNIFICANCE STATEMENT The study provides the first direct evidence of aggrecan as the main functional constituent and orchestrator of perineuronal nets (PNNs), and that loss of PNNs by aggrecan removal induces a permanent state of critical period-like plasticity. Loss of aggrecan ablates the PNN structure, resulting in invoked juvenile plasticity in the visual cortex and enhanced object recognition memory.

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

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.

Firing-rate based network modeling of the dLGN circuit: Effects of cortical feedback on spatiotemporal response properties of relay cells.

Visually evoked signals in the retina pass through the dorsal geniculate nucleus (dLGN) 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 (i) a slow (long-delay) and spatially widespread inhibitory feedback, combined with (ii) 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.

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 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.

Removal of perineuronal nets disrupts recall of a remote fear memory.

Throughout life animals learn to recognize cues that signal danger and instantaneously initiate an adequate threat response. Memories of such associations may last a lifetime and far outlast the intracellular molecules currently found to be important for memory processing. The memory engram may be supported by other more stable molecular components, such as the extracellular matrix structure of perineuronal nets (PNNs). Here, we show that recall of remote, but not recent, visual fear memories in rats depend on intact PNNs in the secondary visual cortex (V2L). Supporting our behavioral findings, increased synchronized theta oscillations between V2L and basolateral amygdala, a physiological correlate of successful recall, was absent in rats with degraded PNNs in V2L. Together, our findings suggest a role for PNNs in remote memory processing by stabilizing the neural network of the engram.

Temporal Processing in the Visual Cortex of the Awake and Anesthetized Rat.

The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states. Using chronically implanted tetrodes into primary visual cortex (V1) of rats, we conducted extracellular recordings of single units and followed the same cell ensembles in the awake and anesthetized states. We found that the transition from wakefulness to anesthesia involves unpredictable changes in temporal response characteristics. The latency of single-unit responses to visual stimulation was delayed in anesthesia, with large individual variations between units. Pair-wise correlations between units increased under anesthesia, indicating more synchronized activity. Further, the units within an ensemble show reproducible temporal activity patterns in response to visual stimuli that is changed between states, suggesting state-dependent sequences of activity. The current dataset, with recordings from the same neural ensembles across states, is well suited for validating and testing computational network models. This can lead to testable predictions, bring a deeper understanding of the experimental findings and improve models of neural information processing. Here, we exemplify such a workflow using a Brunel network model.

Differential Expression and Cell-Type Specificity of Perineuronal Nets in Hippocampus, Medial Entorhinal Cortex, and Visual Cortex Examined in the Rat and Mouse.

Perineuronal nets (PNNs) are specialized extracellular matrix (ECM) structures that condense around the soma and proximal dendrites of subpopulations of neurons. Emerging evidence suggests that they are involved in regulating brain plasticity. However, the expression of PNNs varies between and within brain areas. A lack of quantitative studies describing the distribution and cell-specificity of PNNs makes it difficult to reveal the functional roles of PNNs. In the current study, we examine the distribution of PNNs and the identity of PNN-enwrapped neurons in three brain areas with different cognitive functions: the dorsal hippocampus, medial entorhinal cortex (mEC) and primary visual cortex (V1). We compared rats and mice as knowledge from these species are often intermingled. The most abundant expression of PNNs was found in the mEC and V1, while dorsal hippocampus showed strikingly low levels of PNNs, apart from dense expression in the CA2 region. In hippocampus we also found apparent species differences in expression of PNNs. While we confirm that the PNNs enwrap parvalbumin-expressing (PV+) neurons in V1, we found that they mainly colocalize with excitatory CamKII-expressing neurons in CA2. In mEC, we demonstrate that in addition to PV+ cells, the PNNs colocalize with reelin-expressing stellate cells. We also show that the maturation of PNNs in mEC coincides with the formation of grid cell pattern, while PV+ cells, unlike in other cortical areas, are present from early postnatal development. Finally, we demonstrate considerable effects on the number of PSD-95-gephyrin puncta after enzymatic removal of PNNs.

Neuronify: An Educational Simulator for Neural Circuits.

Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

Removal of Perineuronal Nets Unlocks Juvenile Plasticity Through Network Mechanisms of Decreased Inhibition and Increased Gamma Activity.

Perineuronal nets (PNNs) are extracellular matrix structures mainly enwrapping parvalbumin-expressing inhibitory neurons. The assembly of PNNs coincides with the end of the period of heightened visual cortex plasticity in juveniles, whereas removal of PNNs in adults reopens for plasticity. The mechanisms underlying this phenomenon remain elusive. We have used chronic electrophysiological recordings to investigate accompanying electrophysiological changes to activity-dependent plasticity and we report on novel mechanisms involved in both induced and critical period plasticity. By inducing activity-dependent plasticity in the visual cortex of adult rats while recording single unit and population activity, we demonstrate that PNN removal alters the balance between inhibitory and excitatory spiking activity directly. Without PNNs, inhibitory activity was reduced, whereas spiking variability was increased as predicted in a simulation with a Brunel neural network. Together with a shift in ocular dominance and large effects on unit activity during the first 48 h of monocular deprivation (MD), we show that PNN removal resets the neural network to an immature, juvenile state. Furthermore, in PNN-depleted adults as well as in juveniles, MD caused an immediate potentiation of gamma activity, suggesting a novel mechanism initiating activity-dependent plasticity and driving the rapid changes in unit activity. SIGNIFICANCE STATEMENT: Emerging evidence suggests a role for perineuronal nets (PNNs) in learning and regulation of plasticity, but the underlying mechanisms remain unresolved. Here, we used chronic in vivo extracellular recordings to investigate how removal of PNNs opens for plasticity and how activity-dependent plasticity affects neural activity over time. PNN removal caused reduced inhibitory activity and reset the network to a juvenile state. Experimentally induced activity-dependent plasticity by monocular deprivation caused rapid changes in single unit activity and a remarkable potentiation of gamma oscillations. Our results demonstrate how PNNs may be involved directly in stabilizing the neural network. Moreover, the immediate potentiation of gamma activity after plasticity onset points to potential new mechanisms for the initiation of activity-dependent plasticity.

ViSAPy: a Python tool for biophysics-based generation of virtual spiking activity for evaluation of spike-sorting algorithms.

BACKGROUND: New, silicon-based multielectrodes comprising hundreds or more electrode contacts offer the possibility to record spike trains from thousands of neurons simultaneously. This potential cannot be realized unless accurate, reliable automated methods for spike sorting are developed, in turn requiring benchmarking data sets with known ground-truth spike times. NEW METHOD: We here present a general simulation tool for computing benchmarking data for evaluation of spike-sorting algorithms entitled ViSAPy (Virtual Spiking Activity in Python). The tool is based on a well-established biophysical forward-modeling scheme and is implemented as a Python package built on top of the neuronal simulator NEURON and the Python tool LFPy. RESULTS: ViSAPy allows for arbitrary combinations of multicompartmental neuron models and geometries of recording multielectrodes. Three example benchmarking data sets are generated, i.e., tetrode and polytrode data mimicking in vivo cortical recordings and microelectrode array (MEA) recordings of in vitro activity in salamander retinas. The synthesized example benchmarking data mimics salient features of typical experimental recordings, for example, spike waveforms depending on interspike interval. COMPARISON WITH EXISTING METHODS: ViSAPy goes beyond existing methods as it includes biologically realistic model noise, synaptic activation by recurrent spiking networks, finite-sized electrode contacts, and allows for inhomogeneous electrical conductivities. ViSAPy is optimized to allow for generation of long time series of benchmarking data, spanning minutes of biological time, by parallel execution on multi-core computers. CONCLUSION: ViSAPy is an open-ended tool as it can be generalized to produce benchmarking data or arbitrary recording-electrode geometries and with various levels of complexity.

Grid cells require excitatory drive from the hippocampus.

To determine how hippocampal backprojections influence spatially periodic firing in grid cells, we recorded neural activity in the medial entorhinal cortex (MEC) of rats after temporary inactivation of the hippocampus. We report two major changes in entorhinal grid cells. First, hippocampal inactivation gradually and selectively extinguished the grid pattern. Second, the same grid cells that lost their grid fields acquired substantial tuning to the direction of the rat's head. This transition in firing properties was contingent on a drop in the average firing rate of the grid cells and could be replicated by the removal of an external excitatory drive in an attractor network model in which grid structure emerges by velocity-dependent translation of activity across a network with inhibitory connections. These results point to excitatory drive from the hippocampus, and possibly other regions, as one prerequisite for the formation and translocation of grid patterns in the MEC.