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.

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.

The pelvic organs (bladder, rectum, and sex organs) have been represented for a century as receiving autonomic innervation from two pathways - lumbar sympathetic and sacral parasympathetic - by way of a shared relay, the pelvic ganglion, conceived as an assemblage of sympathetic and parasympathetic neurons. Using single-cell RNA sequencing, we find that the mouse pelvic ganglion is made of four classes of neurons, distinct from both sympathetic and parasympathetic ones, albeit with a kinship to the former, but not the latter, through a complex genetic signature. We also show that spinal lumbar preganglionic neurons synapse in the pelvic ganglion onto equal numbers of noradrenergic and cholinergic cells, both of which therefore serve as sympathetic relays. Thus, the pelvic viscera receive no innervation from parasympathetic or typical sympathetic neurons, but instead from a divergent tail end of the sympathetic chains, in charge of its idiosyncratic functions.

The inferior colliculus (IC) is a critical computational hub in the central auditory pathway. From its position in the midbrain, the IC receives nearly all the ascending output from the lower auditory brainstem and provides the main source of auditory information to the thalamocortical system. In addition to being a crossroads for auditory circuits, the IC is rich with local circuits and contains more than five times as many neurons as the nuclei of the lower auditory brainstem combined. These results hint at the enormous computational power of the IC, and indeed, systems-level studies have identified numerous important transformations in sound coding that occur in the IC. However, despite decades of effort, the cellular mechanisms underlying IC computations and how these computations change following hearing loss have remained largely impenetrable. In this review, we argue that this challenge persists due to the surprisingly difficult problem of identifying the neuron types and circuit motifs that comprise the IC. After summarizing the extensive evidence pointing to a diversity of neuron types in the IC, we highlight the successes of recent efforts to parse this complexity using molecular markers to define neuron types. We conclude by arguing that the discovery of molecularly identifiable neuron types ushers in a new era for IC research marked by molecularly targeted recordings and manipulations. We propose that the ability to reproducibly investigate IC circuits at the neuronal level will lead to rapid advances in understanding the fundamental mechanisms driving IC computations and how these mechanisms shift following hearing loss.

Maintaining precise synaptic contacts between neuronal partners is critical to ensure the proper functioning of the mammalian central nervous system (CNS). Diverse cell recognition molecules, such as classic cadherins (Cdhs), are part of the molecular machinery mediating synaptic choices during development and synaptic maintenance. Yet, the principles governing neuron-neuron wiring across diverse CNS neuron types remain largely unknown. The retinotectal synapses, connections from the retinal ganglion cells (RGCs) to the superior collicular (SC) neurons, offer an ideal experimental system to reveal molecular logic underlying synaptic choices and formation. This is due to the retina\'s unidirectional and laminar-restricted projections to the SC and the large databases of presynaptic RGC subtypes and postsynaptic SC neuronal types. Here, we focused on determining the role of Type II Cdhs in wiring the retinotectal synapses. We surveyed Cdhs expression patterns at neuronal resolution and revealed that Cdh13 is enriched in the wide-field neurons in the superficial SC (sSC). In either the Cdh13 null mutant or selective adult deletion within the wide-field neurons, there is a significant reduction of spine densities in the distal dendrites of these neurons in both sexes. Additionally, Cdh13 removal from presynaptic RGCs reduced dendritic spines in the postsynaptic wide-field neurons. Cdh13-expressing RGCs use differential mechanisms than αRGCs and On-Off Direction-Selective Ganglion Cells (ooDSGCs) to form specific retinotectal synapses. The results revealed a selective transneuronal interaction mediated by Cdh13 to maintain proper retinotectal synapses in vivo.

The amygdala is a complex brain structure in the vertebrate telencephalon, essential for regulating social behaviors, emotions, and (social) cognition. In contrast to the vast majority of neuron types described in the many nuclei of the mammalian amygdala, little is known about the neuronal diversity in non-mammals, making reconstruction of its evolution particularly difficult. Here, we characterize glutamatergic neuron types in the amygdala of the urodele amphibian Pleurodeles waltl. Our single-cell RNA sequencing data indicate the existence of at least ten distinct types and subtypes of glutamatergic neurons in the salamander amygdala. These neuron types are molecularly distinct from neurons in the ventral pallium (VP), suggesting that the pallial amygdala and the VP are two separate areas in the telencephalon. In situ hybridization for marker genes indicates that amygdalar glutamatergic neuron types are located in three major subdivisions: the lateral amygdala, the medial amygdala, and a newly defined area demarcated by high expression of the transcription factor Sim1. The gene expression profiles of these neuron types suggest similarities with specific neurons in the sauropsid and mammalian amygdala. In particular, we identify Sim1+ and Sim1+ Otp+ expressing neuron types, potentially homologous to the mammalian nucleus of the lateral olfactory tract (NLOT) and to hypothalamic-derived neurons of the medial amygdala, respectively. Taken together, our results reveal a surprising diversity of glutamatergic neuron types in the amygdala of salamanders, despite the anatomical simplicity of their brain. These results offer new insights on the cellular and anatomical complexity of the amygdala in tetrapod ancestors.

Although its dense connections with other brain areas suggests that the claustrum is involved in higher-order brain functions, little is known about the properties of claustrum neurons. Using whole-cell patch clamp recordings in acute brain slices of mice, we characterized the intrinsic electrical properties of more than 300 claustral neurons and used unsupervised clustering of these properties to define distinct cell types. Differences in intrinsic properties permitted separation of interneurons (INs) from projection neurons (PNs). Five subtypes of PNs could be further identified by differences in their adaptation of action potential (AP) frequency and amplitude, as well as their AP firing variability. Injection of retrogradely transported fluorescent beads revealed that PN subtypes differed in their projection targets: one projected solely to subcortical areas while three out of the remaining four targeted cortical areas. INs expressing parvalbumin (PV), somatostatin (SST), or vasoactive intestinal peptide (VIP) formed a heterogenous group. PV-INs were readily distinguishable from VIP-INs and SST-INs, while the latter two were clustered together. To distinguish IN subtypes, an artificial neural network was trained to distinguish the properties of PV-INs, SST-INs, and VIP-INs, as independently identified through their expression of marker proteins. A user-friendly, machine-learning tool that uses intrinsic electrical properties to distinguish these eight different types of claustral cells was developed to facilitate implementation of our classification scheme. Systematic classification of claustrum neurons lays the foundation for future determinations of claustrum circuit function, which will advance our understanding of the role of the claustrum in brain function.

In the central nucleus of the inferior colliculus (ICC), which acts as the hub of the auditory pathways, how the sound is coded by distinct cell types is largely unknown. Large GABAergic cells are tuned broadly to pure tones and respond more strongly to frequency-modulated sweeps than small GABAergic and glutamatergic (GLU) cells. Neurons, especially GLU cells, which share sharpness of tuning and sweep sensitivity, were spatially clustered inside the ICC. The difference in responsiveness between cell types was partially explained by the morphology of dendritic trees and the spatial distributions of excitatory and inhibitory inputs. The results suggest that each ICC cell type has a particular sound-encoding strategy, which is partially determined by the morphological characteristics and location of cells, and contributes information coding in higher centres in different ways.
The rules governing the encoding of sound information in the higher auditory centres are largely unknown. In the central nucleus of the inferior colliculus (ICC), which acts as the hub of the auditory pathways, the presence of functional maps other than tonotopicity has been suggested but not established, due to significant heterogeneity in the response properties of single microdomains. Since each ICC cell type has distinct neuronal circuitry, each cell type might encode sound information differently. Here, juxtacellular recording from rat ICC of both sexes revealed that large GABAergic (LG), small GABAergic (SG) and glutamatergic (GLU) cells encode sound information differently. LG cells are broadly tuned and respond more strongly to frequency-modulated sweeps than SG and GLU cells. At a population level, responsiveness to sweeps is location dependent: the responsiveness to sweeps is shared in local clusters of GLU cells, whereas that to directional selectivity of sweeps is shared in local clusters of LG cells. The difference in responsiveness between cell types was partially explained by the morphology of dendritic trees and the spatial distributions of excitatory and inhibitory inputs. LG neurons had a dense local axonal plexus with projection fibres to multiple distant targets, whereas GLU projection neurons mainly aimed at a single, distant target and had less dense local collaterals. These results suggest that each ICC cell type has a particular sound-encoding strategy, which is partially determined by the morphological characteristics and location of the cell, and the different cell types contribute information coding in higher centres in different ways.

Seeking new insights into the homeostasis, modulation and plasticity of cortical synaptic networks, we have analyzed results from a single-cell RNA-seq study of 22,439 mouse neocortical neurons. Our analysis exposes transcriptomic evidence for dozens of molecularly distinct neuropeptidergic modulatory networks that directly interconnect all cortical neurons. This evidence begins with a discovery that transcripts of one or more neuropeptide precursor (NPP) and one or more neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes are highly abundant in all, or very nearly all, cortical neurons. Individual neurons express diverse subsets of NP signaling genes from palettes encoding 18 NPPs and 29 NP-GPCRs. These 47 genes comprise 37 cognate NPP/NP-GPCR pairs, implying the likelihood of local neuropeptide signaling. Here, we use neuron-type-specific patterns of NP gene expression to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.

The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state-of-the-art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open-source at Hippocampome.org in order to further research across disciplines.