Michael W. Reimann

Objective. We aim to determine whether commonly-used current source density (CSD) estimation techniques provide valid results when used with recordings from the latest generation of high-density arrays and how the density affects signal interpretation. Approach. We first develop an analytic formulation for the contributions of Gaussian current sources to an estimated CSD as a function of current source position and array density. We then simulate local field potential recordings in a biophysically detailed model of a subvolume of rat somatosensory cortex, and compare estimated current source densities to the ground truth current densities averaged over relevant tissue volumes. Main results. Commonly used methods to estimate the CSD can produce spurious results when the inter-electrode spacing is small relative to the width of the current source, confusing sources and sinks. For high-density recording electrodes, the estimated CSD diverges from the volume-averaged ground-truth current distribution that the CSD estimation method attempts to reconstruct. Both confusion of sinks and sources and amplification of local current fluctuations play a role in this divergence. Careful choice of CSD reconstruction parameters can be used to preferentially reveal the information content on a local or higher-level scale. Significance. Our results shed light on high-spatial-frequency oscillations observed in estimated current source densities from in vivo experiments. Care must be taken when choosing a reconstruction method and interpreting the results from high density arrays, but proper parameter tuning allows to focus on specific information-of-interest. In addition, it was found that detailed network models, including active conductances and background noise, can be necessary to accurately simulate high-frequency current fluctuations.

Dendritic spines, small protrusions covering the dendrites of most neurons, are fundamental elements of synaptic connectivity, yet their network-level organization remains poorly understood. Here we leverage the large-scale MICrONS volumetric electron microscopy dataset of mouse primary visual cortex to explore the connectivity logic of dendritic spines across multiple spatial scales. Our analysis provides structural support for the “connectivity and diversity” hypothesis, showing that, for both excitatory and inhibitory neurons, dendritic spine density correlates with the number and the diversity of presynaptic partners. We further show that the preference for excitatory axons to form synapses on spines increases with distance from the soma. In addition, we find that spines serve as postsynaptic targets for high-output neurons (“out hubs”). We finally uncover an input/output correlation, in which neurons that share their input also preferentially target spines through their axonal output. These correlations persist across network sizes, consistent with a scale-invariant structural organization of cortical connectivity. We hypothesize that dendritic spines provide a structural substrate for network-level cooperation, by enabling distributed cortical activity to propagate through parallel synchronous chains, potentially amplified by nonlinear voltage responses at the spine and cellular level. These findings suggest that dendritic spines are key structural mechanisms for neuronal ensembles, attractor dynamics, and pattern completion.

Comparative studies have established differences between the electrophysiology and anatomy of human and rodent cortical circuits. A consistent finding is that human neuronal morphologies display more elaborate neurite shapes than those of rodents, a feature that cannot be accounted for merely by their larger size according to recent findings. Here, we study the impact of these neurite shapes on the structure of synaptic connectivity in their local microcircuitry. Our approach is based on the idea that axonal and dendritic geometries constrain the locations of afferent and efferent synaptic contacts (potential connectivity). Although the mechanisms by which potential connectivity translates into actual synaptic connectivity are manifold and complex, the potential connectivity is nevertheless highly informative for the final structure of a biological connectome. We found that connectomes predicted from human reconstructed morphologies have higher complexity according to several measures that have been demonstrated to be functionally relevant. Going beyond a simple comparison, we demonstrate mechanistically how the shapes of neuron morphologies give rise to non-random and clustered structures observed in experimentally measured connectomes, and how the specific shapes of human neurons strengthen the process. Finally, we conceptually examine how synapse formation processes may interact with potential connectivity, showing that a process compatible with Hebbian plasticity leads to the highest complexity and best match experimentally observed patterns.

Cortical dynamics underlie many cognitive processes and emerge from complex multiscale interactions, which are challenging to study in vivo. Large-scale, biophysically detailed models offer a tool that can complement laboratory approaches. We present a model comprising eight somatosensory cortex subregions, 4.2 million morphological and electrically detailed neurons, and 13.2 billion local and mid-range synapses. In silico tools enabled reproduction and extension of complex laboratory experiments under a single parameterization, providing strong validation. The model reproduced millisecond-precise stimulus-responses, stimulus-encoding under targeted optogenetic activation, and selective propagation of stimulus-evoked activity to downstream areas. The model’s direct correspondence with biology generated predictions about how multiscale organization shapes activity; for example, how cortical activity is shaped by high-dimensional connectivity motifs in local and mid-range connectivity, and spatial targeting rules by inhibitory subpopulations. The latter was facilitated using a rewired connectome that included specific targeting rules observed for different inhibitory neuron types in electron microscopy. The model also predicted the role of inhibitory interneuron types and different layers in stimulus encoding. Simulation tools and a large subvolume of the model are made available to enable further community-driven improvement, validation, and investigation.

The function of the neocortex is fundamentally determined by its repeating microcircuit motif, but also by its rich, interregional connectivity. We present a data-driven computational model of the anatomy of non-barrel primary somatosensory cortex of juvenile rat, integrating whole-brain scale data while providing cellular and subcellular specificity. The model consists of 4.2 million morphologically detailed neurons, placed in a digital brain atlas. They are connected by 14.2 billion synapses, comprising local, mid-range and extrinsic connectivity. We delineated the limits of determining connectivity from neuron morphology and placement, finding that it reproduces targeting by Sst+ neurons, but requires additional specificity to reproduce targeting by PV+ and VIP+ interneurons. Globally, connectivity was characterized by local clusters tied together through hub neurons in layer 5, demonstrating how local and interegional connectivity are complicit, inseparable networks. The model is suitable for simulation-based studies, and the model is made openly available to the community.

Neuronal networks are characterized by complex and functionally relevant connectivity motifs. We developed an intuitive explanation for its emergence: While a class of neurons on average innervates its entire surroundings, each individual one can only cover a small part of the space. That region is different for each neuron, but not completely random, as it is physically constrained by the spatial continuity of the axon. This hypothesis was successfully tested against a morphologically-detailed model and an electron-microscopic reconstruction of cortical connectivity. We distilled it into a stochastic algorithm that generates networks, which accurately match the reference data. Our work bridges previous efforts to capture network complexity with top-down or bottom-up methods, that is, by adding complexity constraints to simple stochastic models or by predicting synapses from neuron appositions. It may improve the understanding of the impact of neuron malformations and the functional role of non-random network structure in simplified models.

Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have revealed mechanisms and protocols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biological neuronal networks to ensure the emergence of learning remains poorly understood. Simulation and modeling have emerged as important tools to study learning in plastic networks, but have yet to achieve a scale that incorporates realistic network structure, active dendrites, and multi-synapse interactions, key determinants of synaptic plasticity. To rise to this challenge, we endowed an existing large-scale cortical network model, incorporating data-constrained dendritic processing and multi-synaptic connections, with a calcium-based model of functional plasticity that captures the diversity of excitatory connections extrapolated to in vivo-like conditions. This allowed us to study how dendrites and network structure interact with plasticity to shape stimulus representations at the microcircuit level. In our exploratory simulations, plasticity acted sparsely and specifically, firing rates and weight distributions remained stable without additional homeostatic mechanisms. At the circuit level, we found plasticity was driven by co-firing stimulus-evoked functional assemblies, spatial clustering of synapses on dendrites, and the topology of the network connectivity. As a result of the plastic changes, the network became more reliable with more stimulus-specific responses. We confirmed our testable predictions in the MICrONS datasets, an openly available electron microscopic reconstruction of a large volume of cortical tissue. Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.

Neuronal connectivity has been characterized at various scales and with respect to various structural aspects. In models of connectivity, it has so far remained difficult to match all of them at once, in particular the higher-order structure appears to be elusive. Here we introduce a new type of graph model that matches non-random structure characterized and described as relevant in biological neuronal networks. The structure emerges because the algorithm considers the need for axons to physically bridge the gap from soma to dendrites. If it targets one neuron, probabilities that it also targets other nearby neurons increase. We demonstrate that the algorithm can be successfully fit to complex, biologically relevant reference connectomes. Furthermore, we outline an intuitive expansion of the model from merely local to a combination of local and long-range connectivity. We provide a performant implementation that can be used to instantiate point neuron or morphologically-detailed network models at whole-cortex scale.

Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have revealed mechanisms and protocols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biological neuronal networks to ensure the emergence of learning remains poorly understood. Simulation and modeling have emerged as important tools to study learning in plastic networks, but have yet to achieve a scale that incorporates realistic network structure, active dendrites, and multi-synapse interactions, key determinants of synaptic plasticity. To rise to this challenge, we endowed an existing large-scale cortical network model, incorporating data-constrained dendritic processing and multi-synaptic connections, with a calcium-based model of functional plasticity that captures the diversity of excitatory connections extrapolated to in vivo-like conditions. This allowed us to study how dendrites and network structure interact with plasticity to shape stimulus representations at the microcircuit level. In our exploratory simulations, plasticity acted sparsely and specifically, firing rates and weight distributions remained stable without additional homeostatic mechanisms. At the circuit level, we found plasticity was driven by co-firing stimulus-evoked functional assemblies, spatial clustering of synapses on dendrites, and the topology of the network connectivity. As a result of the plastic changes, the network became more reliable with more stimulus-specific responses. We confirmed our testable predictions in the MICrONS datasets, an openly available electron microscopic reconstruction of a large volume of cortical tissue. Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.