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.

The extracellular matrix and perineuronal nets in memory.

All components of the CNS are surrounded by a diffuse extracellular matrix (ECM) containing chondroitin sulphate proteoglycans (CSPGs), heparan sulphate proteoglycans (HSPGs), hyaluronan, various glycoproteins including tenascins and thrombospondin, and many other molecules that are secreted into the ECM and bind to ECM components. In addition, some neurons, particularly inhibitory GABAergic parvalbumin-positive (PV) interneurons, are surrounded by a more condensed cartilage-like ECM called perineuronal nets (PNNs). PNNs surround the soma and proximal dendrites as net-like structures that surround the synapses. Attention has focused on the role of PNNs in the control of plasticity, but it is now clear that PNNs also play an important part in the modulation of memory. In this review we summarize the role of the ECM, particularly the PNNs, in the control of various types of memory and their participation in memory pathology. PNNs are now being considered as a target for the treatment of impaired memory. There are many potential treatment targets in PNNs, mainly through modulation of the sulphation, binding, and production of the various CSPGs that they contain or through digestion of their sulphated glycosaminoglycans.

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.

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.

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.

Alterations in Schizophrenia-Associated Genes Can Lead to Increased Power in Delta Oscillations.

Genome-wide association studies have implicated many ion channels in schizophrenia pathophysiology. Although the functions of these channels are relatively well characterized by single-cell studies, the contributions of common variation in these channels to neurophysiological biomarkers and symptoms of schizophrenia remain elusive. Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta-frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a deficit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia.

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.

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.

Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells.

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.

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.

Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification.

Neural circuits typically consist of many different types of neurons, and one faces a challenge in disentangling their individual contributions in measured neural activity. Classification of cells into inhibitory and excitatory neurons and localization of neurons on the basis of extracellular recordings are frequently employed procedures. Current approaches, however, need a lot of human intervention, which makes them slow, biased, and unreliable. In light of recent advances in deep learning techniques and exploiting the availability of neuron models with quasi-realistic three-dimensional morphology and physiological properties, we present a framework for automatized and objective classification and localization of cells based on the spatiotemporal profiles of the extracellular action potentials recorded by multielectrode arrays. We train convolutional neural networks on simulated signals from a large set of cell models and show that our framework can predict the position of neurons with high accuracy, more precisely than current state-of-the-art methods. Our method is also able to classify whether a neuron is excitatory or inhibitory with very high accuracy, substantially improving on commonly used clustering techniques. Furthermore, our new method seems to have the potential to separate certain subtypes of excitatory and inhibitory neurons. The possibility of automatically localizing and classifying all neurons recorded with large high-density extracellular electrodes contributes to a more accurate and more reliable mapping of neural circuits. NEW & NOTEWORTHY We propose a novel approach to localize and classify neurons from their extracellularly recorded action potentials with a combination of biophysically detailed neuron models and deep learning techniques. Applied to simulated data, this new combination of forward modeling and machine learning yields higher performance compared with state-of-the-art localization and classification methods.

Primary auditory cortex regulates threat memory specificity.

Distinguishing threatening from nonthreatening stimuli is essential for survival and stimulus generalization is a hallmark of anxiety disorders. While auditory threat learning produces long-lasting plasticity in primary auditory cortex (Au1), it is not clear whether such Au1 plasticity regulates memory specificity or generalization. We used muscimol infusions in rats to show that discriminatory threat learning requires Au1 activity specifically during memory acquisition and retrieval, but not during consolidation. Memory specificity was similarly disrupted by infusion of PKMζ inhibitor peptide (ZIP) during memory storage. Our findings show that Au1 is required at critical memory phases and suggest that Au1 plasticity enables stimulus discrimination.

Frequency of gamma oscillations routes flow of information in the hippocampus.

Gamma oscillations are thought to transiently link distributed cell assemblies that are processing related information, a function that is probably important for network processes such as perception, attentional selection and memory. This 'binding' mechanism requires that spatially distributed cells fire together with millisecond range precision; however, it is not clear how such coordinated timing is achieved given that the frequency of gamma oscillations varies substantially across space and time, from approximately 25 to almost 150 Hz. Here we show that gamma oscillations in the CA1 area of the hippocampus split into distinct fast and slow frequency components that differentially couple CA1 to inputs from the medial entorhinal cortex, an area that provides information about the animal's current position, and CA3, a hippocampal subfield essential for storage of such information. Fast gamma oscillations in CA1 were synchronized with fast gamma in medial entorhinal cortex, and slow gamma oscillations in CA1 were coherent with slow gamma in CA3. Significant proportions of cells in medial entorhinal cortex and CA3 were phase-locked to fast and slow CA1 gamma waves, respectively. The two types of gamma occurred at different phases of the CA1 theta rhythm and mostly on different theta cycles. These results point to routeing of information as a possible function of gamma frequency variations in the brain and provide a mechanism for temporal segregation of potentially interfering information from different sources.

Hippocampus-independent phase precession in entorhinal grid cells.

Theta-phase precession in hippocampal place cells is one of the best-studied experimental models of temporal coding in the brain. Theta-phase precession is a change in spike timing in which the place cell fires at progressively earlier phases of the extracellular theta rhythm as the animal crosses the spatially restricted firing field of the neuron. Within individual theta cycles, this phase advance results in a compressed replication of the firing sequence of consecutively activated place cells along the animal's trajectory, at a timescale short enough to enable spike-time-dependent plasticity between neurons in different parts of the sequence. The neuronal circuitry required for phase precession has not yet been established. The fact that phase precession can be seen in hippocampal output stuctures such as the prefrontal cortex suggests either that efferent structures inherit the precession from the hippocampus or that it is generated locally in those structures. Here we show that phase precession is expressed independently of the hippocampus in spatially modulated grid cells in layer II of medial entorhinal cortex, one synapse upstream of the hippocampus. Phase precession is apparent in nearly all principal cells in layer II but only sparsely in layer III. The precession in layer II is not blocked by inactivation of the hippocampus, suggesting that the phase advance is generated in the grid cell network. The results point to possible mechanisms for grid formation and raise the possibility that hippocampal phase precession is inherited from entorhinal cortex.

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.

Hippocampal remapping and grid realignment in entorhinal cortex.

A fundamental property of many associative memory networks is the ability to decorrelate overlapping input patterns before information is stored. In the hippocampus, this neuronal pattern separation is expressed as the tendency of ensembles of place cells to undergo extensive 'remapping' in response to changes in the sensory or motivational inputs to the hippocampus. Remapping is expressed under some conditions as a change of firing rates in the presence of a stable place code ('rate remapping'), and under other conditions as a complete reorganization of the hippocampal place code in which both place and rate of firing take statistically independent values ('global remapping'). Here we show that the nature of hippocampal remapping can be predicted by ensemble dynamics in place-selective grid cells in the medial entorhinal cortex, one synapse upstream of the hippocampus. Whereas rate remapping is associated with stable grid fields, global remapping is always accompanied by a coordinate shift in the firing vertices of the grid cells. Grid fields of co-localized medial entorhinal cortex cells move and rotate in concert during this realignment. In contrast to the multiple environment-specific representations coded by place cells in the hippocampus, local ensembles of grid cells thus maintain a constant spatial phase structure, allowing position to be represented and updated by the same translation mechanism in all environments encountered by the animal.

Coherently remapping toroidal cells but not Grid cells are responsible for path integration in virtual agents.

It is widely believed that grid cells provide cues for path integration, with place cells encoding an animal's location and environmental identity. When entering a new environment, these cells remap concurrently, sparking debates about their causal relationship. Using a continuous attractor recurrent neural network, we study spatial cell dynamics in multiple environments. We investigate grid cell remapping as a function of global remapping in place-like units through random resampling of place cell centers. Dimensionality reduction techniques reveal that a subset of cells manifest a persistent torus across environments. Unexpectedly, these toroidal cells resemble band-like cells rather than high grid score units. Subsequent pruning studies reveal that toroidal cells are crucial for path integration while grid cells are not. As we extend the model to operate across many environments, we delineate its generalization boundaries, revealing challenges with modeling many environments in current models.

An updated suite of viral vectors for in vivo calcium imaging using intracerebral and retro-orbital injections in male mice.

Genetically encoded Ca2+ indicators (GECIs) are widely used to measure neural activity. Here, we explore the use of systemically administered PHP.eB AAVs for brain-wide expression of GECIs and compare the expression properties to intracerebrally injected AAVs in male mice. We show that systemic administration is a promising strategy for imaging neural activity. Next, we establish the use of EE-RR- (soma) and RPL10a (Ribo) soma-targeting peptides with the latest jGCaMP and show that EE-RR-tagged jGCaMP8 gives rise to strong expression but limited soma-targeting. In contrast, Ribo-tagged jGCaMP8 lacks neuropil signal, but the expression rate is reduced. To combat this, we modified the linker region of the Ribo-tag (RiboL1-). RiboL1-jGCaMP8 expresses faster than Ribo-jGCaMP8 but remains too dim for reliable use with systemic virus administration. However, intracerebral injections of the RiboL1-tagged jGCaMP8 constructs provide strong Ca2+ signals devoid of neuropil contamination, with remarkable labeling density.

Microstructure of a spatial map in the entorhinal cortex.

The ability to find one's way depends on neural algorithms that integrate information about place, distance and direction, but the implementation of these operations in cortical microcircuits is poorly understood. Here we show that the dorsocaudal medial entorhinal cortex (dMEC) contains a directionally oriented, topographically organized neural map of the spatial environment. Its key unit is the 'grid cell', which is activated whenever the animal's position coincides with any vertex of a regular grid of equilateral triangles spanning the surface of the environment. Grids of neighbouring cells share a common orientation and spacing, but their vertex locations (their phases) differ. The spacing and size of individual fields increase from dorsal to ventral dMEC. The map is anchored to external landmarks, but persists in their absence, suggesting that grid cells may be part of a generalized, path-integration-based map of the spatial environment.