Parvalbumin Interneurons Are Differentially Connected to Principal Cells in Inhibitory Feedback Microcircuits along the Dorsoventral Axis of the Medial Entorhinal Cortex

Global modules robustly emerge from local interactions and smooth gradients

Modular structure and function are ubiquitous in biology, from the organization of animal brains and bodies to the scale of ecosystems. However, the mechanisms of modularity emergence from non-modular precursors remain unclear. Here we introduce the principle of peak selection, a process by which purely local interactions and smooth gradients can drive the self-organization of discrete global modules. The process combines strengths of the positional and Turing pattern-formation mechanisms into a model for morphogenesis. Applied to the grid-cell system of the brain, peak selection results in the self-organization of functionally distinct modules with discretely spaced spatial periods. Applied to ecological systems, it results in discrete multispecies niches and synchronous spawning across geographically distributed coral colonies. The process exhibits self-scaling with system size and 'topological robustness'1, which renders module emergence and module properties insensitive to most parameters. Peak selection ameliorates the fine-tuning requirement for continuous attractor dynamics in single grid-cell modules and it makes a detail-independent prediction that grid module period ratios should approximate adjacent integer ratios, providing a highly accurate match to the available data. Predictions for grid cells at the transcriptional, connectomic and physiological levels promise to elucidate the interplay of molecules, connectivity and function emergence in brains.

Medial entorhinal-hippocampal desynchronization parallels the emergence of memory impairment in a mouse model of Alzheimer's disease pathology

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive impairments in episodic and spatial memory, as well as circuit and network-level dysfunction. While functional impairments in medial entorhinal cortex (MEC) and hippocampus (HPC) have been observed in patients and rodent models of AD, it remains unclear how communication between these regions breaks down in disease, and what specific physiological changes are associated with the onset of memory impairment. We used silicon probes to simultaneously record neural activity in MEC and hippocampus before or after the onset of spatial memory impairment in the 3xTg mouse model of AD pathology. We found that reduced hippocampal theta power, reduced MEC-CA1 theta coherence, and altered phase locking of MEC and hippocampal neurons all coincided with the emergence of spatial memory impairment in 3xTg mice. Together, these findings indicate that disrupted temporal coordination of neural activity in the MEC-hippocampal system parallels the emergence of memory impairment in a model of AD pathology.

Patch-walking, a coordinated multi-pipette patch clamp for efficiently finding synaptic connections

Significant technical challenges exist when measuring synaptic connections between neurons in living brain tissue. The patch clamping technique, when used to probe for synaptic connections, is manually laborious and time-consuming. To improve its efficiency, we pursued another approach: instead of retracting all patch clamping electrodes after each recording attempt, we cleaned just one of them and reused it to obtain another recording while maintaining the others. With one new patch clamp recording attempt, many new connections can be probed. By placing one pipette in front of the others in this way, one can 'walk' across the mouse brain slice, termed 'patch-walking.' We performed 136 patch clamp attempts for two pipettes, achieving 71 successful whole cell recordings (52.2%). Of these, we probed 29 pairs (i.e. 58 bidirectional probed connections) averaging 91 μm intersomatic distance, finding three connections. Patch-walking yields 80-92% more probed connections, for experiments with 10-100 cells than the traditional synaptic connection searching method.

Synaptic interactions between stellate cells and parvalbumin interneurons in layer 2 of the medial entorhinal cortex are organized at the scale of grid cell clusters

Interactions between excitatory and inhibitory neurons are critical to computations in cortical circuits but their organization is difficult to assess with standard electrophysiological approaches. Within the medial entorhinal cortex, representation of location by grid and other spatial cells involves circuits in layer 2 in which excitatory stellate cells interact with each other via inhibitory parvalbumin expressing interneurons. Whether this connectivity is structured to support local circuit computations is unclear. Here, we introduce strategies to address the functional organization of excitatory-inhibitory interactions using crossed Cre- and Flp-driver mouse lines to direct targeted presynaptic optogenetic activation and postsynaptic cell identification. We then use simultaneous patch-clamp recordings from postsynaptic neurons to assess their shared input from optically activated presynaptic populations. We find that extensive axonal projections support spatially organized connectivity between stellate cells and parvalbumin interneurons, such that direct connections are often, but not always, shared by nearby neurons, whereas multisynaptic interactions coordinate inputs to neurons with greater spatial separation. We suggest that direct excitatory-inhibitory synaptic interactions may operate at the scale of grid cell clusters, with local modules defined by excitatory-inhibitory connectivity, while indirect interactions may coordinate activity at the scale of grid cell modules.

Inter and intralaminar excitation of parvalbumin interneurons in mouse barrel cortex

Parvalbumin (PV) interneurons are inhibitory fast-spiking cells with essential roles in directing the flow of information through cortical circuits. These neurons set the balance between excitation and inhibition and control rhythmic activity. PV interneurons differ between cortical layers in their morphology, circuitry, and function, but how their electrophysiological properties vary has received little attention. Here we investigate responses of PV interneurons in different layers of primary somatosensory barrel cortex (BC) to different excitatory inputs. With the genetically-encoded hybrid voltage sensor, hVOS, we recorded voltage changes in many L2/3 and L4 PV interneurons simultaneously, with stimulation applied to either L2/3 or L4. A semi-automated procedure was developed to identify small regions of interest corresponding to single responsive PV interneurons. Amplitude, half-width, and rise-time were greater for PV interneurons residing in L2/3 compared to L4. Stimulation in L2/3 elicited responses in both L2/3 and L4 with longer latency compared to stimulation in L4. These differences in latency between layers could influence their windows for temporal integration. Thus, PV interneurons in different cortical layers of BC respond in a layer specific and input specific manner, and these differences have potential roles in cortical computations.

A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology

Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal is to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software (version 1.0) is released as open source to enable broad community reuse and encourage novel applications.

A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology

Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal was to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software is released as open source to enable broad community reuse and encourage novel applications.

Laminar and dorsoventral organization of layer 1 interneuronal microcircuitry in superficial layers of the medial entorhinal cortex

Layer 1 (L1) interneurons (INs) participate in various brain functions by gating information flow in the neocortex, but their role in the medial entorhinal cortex (MEC) is still unknown, largely due to scant knowledge of MEC L1 microcircuitry. Using simultaneous triple-octuple whole-cell recordings and morphological reconstructions, we comprehensively depict L1IN networks in the MEC. We identify three morphologically distinct types of L1INs with characteristic electrophysiological properties. We dissect intra- and inter-laminar cell-type-specific microcircuits of L1INs, showing connectivity patterns different from those in the neocortex. Remarkably, motif analysis reveals transitive and clustered features of L1 networks, as well as over-represented trans-laminar motifs. Finally, we demonstrate the dorsoventral gradient of L1IN microcircuits, with dorsal L1 neurogliaform cells receiving fewer intra-laminar inputs but exerting more inhibition on L2 principal neurons. These results thus present a more comprehensive picture of L1IN microcircuitry, which is indispensable for deciphering the function of L1INs in the MEC.

Cell-Type Specific Inhibition Controls the High-Frequency Oscillations in the Medial Entorhinal Cortex

The medial entorhinal cortex (mEC) plays a critical role for spatial navigation and memory. While many studies have investigated the principal neurons within the entorhinal cortex, much less is known about the inhibitory circuitries within this structure. Here, we describe for the first time in the mEC a subset of parvalbumin-positive (PV+) interneurons (INs)-stuttering cells (STUT)-with morphological, intrinsic electrophysiological, and synaptic properties distinct from fast-spiking PV+ INs. In contrast to the fast-spiking PV+ INs, the axon of the STUT INs also terminated in layer 3 and showed subthreshold membrane oscillations at gamma frequencies. Whereas the synaptic output of the STUT INs was only weakly reduced by a μ-opioid agonist, their inhibitory inputs were strongly suppressed. Given these properties, STUT are ideally suited to entrain gamma activity in the pyramidal cell population of the mEC. We propose that activation of the μ-opioid receptors decreases the GABA release from the PV+ INs onto the STUT, resulting in disinhibition of the STUT cell population and the consequent increase in network gamma power. We therefore suggest that the opioid system plays a critical role, mediated by STUT INs, in the neural signaling and oscillatory network activity within the mEC.

Cortical control of chandelier cells in neural codes

Various cortical functions arise from the dynamic interplay of excitation and inhibition. GABAergic interneurons that mediate synaptic inhibition display significant diversity in cell morphology, electrophysiology, plasticity rule, and connectivity. These heterogeneous features are thought to underlie their functional diversity. Emerging attention on specific properties of the various interneuron types has emphasized the crucial role of cell-type specific inhibition in cortical neural processing. However, knowledge is still limited on how each interneuron type forms distinct neural circuits and regulates network activity in health and disease. To dissect interneuron heterogeneity at single cell-type precision, we focus on the chandelier cell (ChC), one of the most distinctive GABAergic interneuron types that exclusively innervate the axon initial segments (AIS) of excitatory pyramidal neurons. Here we review the current understanding of the structural and functional properties of ChCs and their implications in behavioral functions, network activity, and psychiatric disorders. These findings provide insights into the distinctive roles of various single-type interneurons in cortical neural coding and the pathophysiology of cortical dysfunction.

Microcircuits for spatial coding in the medial entorhinal cortex

The hippocampal formation is critically involved in learning and memory and contains a large proportion of neurons encoding aspects of the organism's spatial surroundings. In the medial entorhinal cortex (MEC), this includes grid cells with their distinctive hexagonal firing fields as well as a host of other functionally defined cell types including head direction cells, speed cells, border cells, and object-vector cells. Such spatial coding emerges from the processing of external inputs by local microcircuits. However, it remains unclear exactly how local microcircuits and their dynamics within the MEC contribute to spatial discharge patterns. In this review we focus on recent investigations of intrinsic MEC connectivity, which have started to describe and quantify both excitatory and inhibitory wiring in the superficial layers of the MEC. Although the picture is far from complete, it appears that these layers contain robust recurrent connectivity that could sustain the attractor dynamics posited to underlie grid pattern formation. These findings pave the way to a deeper understanding of the mechanisms underlying spatial navigation and memory.

Kinetics and Connectivity Properties of Parvalbumin- and Somatostatin-Positive Inhibition in Layer 2/3 Medial Entorhinal Cortex

Parvalbumin-positive (Pvalb⁺) and somatostatin-positive (Sst⁺) cells are the two largest subgroups of inhibitory interneurons. Studies in visual cortex indicate that synaptic connections between Pvalb⁺ cells are common while connections between Sst⁺ interneurons have not been observed. The inhibitory connectivity and kinetics of these two interneuron subpopulations, however, have not been characterized in medial entorhinal cortex (mEC). Using fluorescence-guided paired recordings in mouse brain slices from interneurons and excitatory cells in layer 2/3 mEC, we found that, unlike neocortical measures, Sst⁺ cells inhibit each other, albeit with a lower probability than Pvalb⁺ cells (18% vs 36% for unidirectional connections). Gap junction connections were also more frequent between Pvalb⁺ cells than between Sst⁺ cells. Pvalb⁺ cells inhibited each other with larger conductances, smaller decay time constants, and shorter delays. Similarly, synaptic connections between Pvalb⁺ and excitatory cells were more likely and expressed faster decay times and shorter delays than those between Sst⁺ and excitatory cells. Inhibitory cells exhibited smaller synaptic decay time constants between interneurons than on their excitatory targets. Inhibition between interneurons also depressed faster, and to a greater extent. Finally, inhibition onto layer 2 pyramidal and stellate cells originating from Pvalb⁺ interneurons were very similar, with no significant differences in connection likelihood, inhibitory amplitude, and decay time. A model of short-term depression fitted to the data indicates that recovery time constants for refilling the available pool are in the range of 50-150 ms and that the fraction of the available pool released on each spike is in the range 0.2-0.5.

Entorhinal cortical Island cells regulate temporal association learning with long trace period

Temporal association learning (TAL) allows for the linkage of distinct, nonsynchronous events across a period of time. This function is driven by neural interactions in the entorhinal cortical-hippocampal network, especially the neural input from the pyramidal cells in layer III of medial entorhinal cortex (MECIII) to hippocampal CA1 is crucial for TAL. Successful TAL depends on the strength of event stimuli and the duration of the temporal gap between events. Whereas it has been demonstrated that the neural input from pyramidal cells in layer II of MEC, referred to as Island cells, to inhibitory neurons in dorsal hippocampal CA1 controls TAL when the strength of event stimuli is weak, it remains unknown whether Island cells regulate TAL with long trace periods as well. To understand the role of Island cells in regulating the duration of the learnable trace period in TAL, we used Pavlovian trace fear conditioning (TFC) with a 60-sec long trace period (long trace fear conditioning [L-TFC]) coupled with optogenetic and chemogenetic neural activity manipulations as well as cell type-specific neural ablation. We found that ablation of Island cells in MECII partially increases L-TFC performance. Chemogenetic manipulation of Island cells causes differential effectiveness in Island cell activity and leads to a circuit imbalance that disrupts L-TFC. However, optogenetic terminal inhibition of Island cell input to dorsal hippocampal CA1 during the temporal association period allows for long trace intervals to be learned in TFC. These results demonstrate that Island cells have a critical role in regulating the duration of time bridgeable between associated events in TAL.

Spatially structured inhibition defined by polarized parvalbumin interneuron axons promotes head direction tuning

In cortical microcircuits, it is generally assumed that fast-spiking parvalbumin interneurons mediate dense and nonselective inhibition. Some reports indicate sparse and structured inhibitory connectivity, but the computational relevance and the underlying spatial organization remain unresolved. In the rat superficial presubiculum, we find that inhibition by fast-spiking interneurons is organized in the form of a dominant super-reciprocal microcircuit motif where multiple pyramidal cells recurrently inhibit each other via a single interneuron. Multineuron recordings and subsequent 3D reconstructions and analysis further show that this nonrandom connectivity arises from an asymmetric, polarized morphology of fast-spiking interneuron axons, which individually cover different directions in the same volume. Network simulations assuming topographically organized input demonstrate that such polarized inhibition can improve head direction tuning of pyramidal cells in comparison to a "blanket of inhibition." We propose that structured inhibition based on asymmetrical axons is an overarching spatial connectivity principle for tailored computation across brain regions.