A simulation study on the effects of dendritic morphology on layer V prefrontal pyramidal cell firing behavior

How neuronal morphology impacts the synchronisation state of neuronal networks

The biophysical properties of neurons not only affect how information is processed within cells, they can also impact the dynamical states of the network. Specifically, the cellular dynamics of action-potential generation have shown relevance for setting the (de)synchronisation state of the network. The dynamics of tonically spiking neurons typically fall into one of three qualitatively distinct types that arise from distinct mathematical bifurcations of voltage dynamics at the onset of spiking. Accordingly, changes in ion channel composition or even external factors, like temperature, have been demonstrated to switch network behaviour via changes in the spike onset bifurcation and hence its associated dynamical type. A thus far less addressed modulator of neuronal dynamics is cellular morphology. Based on simplified and anatomically realistic mathematical neuron models, we show here that the extent of dendritic arborisation has an influence on the neuronal dynamical spiking type and therefore on the (de)synchronisation state of the network. Specifically, larger dendritic trees prime neuronal dynamics for in-phase-synchronised or splayed-out activity in weakly coupled networks, in contrast to cells with otherwise identical properties yet smaller dendrites. Our biophysical insights hold for generic multicompartmental classes of spiking neuron models (from ball-and-stick-type to anatomically reconstructed models) and establish a connection between neuronal morphology and the susceptibility of neural tissue to synchronisation in health and disease.

Neuroanatomical, electrophysiological, and morphological characterization of melanin-concentrating hormone cells coexpressing cocaine- and amphetamine-regulated transcript

Melanin-concentrating hormone (MCH) cells in the hypothalamus regulate fundamental physiological functions like energy balance, sleep, and reproduction. This diversity may be ascribed to the neurochemical heterogeneity among MCH cells. One prominent subpopulation of MCH cells coexpresses cocaine- and amphetamine-regulated transcript (CART), and as MCH and CART can have opposing actions, MCH/CART+ and MCH/CART- cells may differentially modulate behavioral outcomes. However, it is not known if there are differences in the cellular properties underlying their functional differences; thus, we compared the neuroanatomical, electrophysiological, and morphological properties of MCH cells in male and female Mch-cre;L10-Egfp reporter mice. Half of MCH cells expressed CART and were most prominent in the medial hypothalamus. Whole-cell patch-clamp recordings revealed differences in their passive and active membrane properties in a sex-dependent manner. Female MCH/CART+ cells had lower input resistances, but male cells largely differed in their firing properties. All MCH cells increased firing when stimulated, but their firing frequency decreases with sustained stimulation. MCH/CART+ cells showed stronger spike rate adaptation than MCH/CART- cells. The kinetics of excitatory events at MCH cells also differed by cell type, as the rising rate of excitatory events was slower at MCH/CART+ cells. By reconstructing the dendritic arborization of our recorded cells, we found no sex differences, but male MCH/CART+ cells had less dendritic length and fewer branch points. Overall, distinctions in topographical division and cellular properties between MCH cells add to their heterogeneity and help elucidate their response to stimuli or effect on modulating their respective neural networks.

Spatial integration of dendrites in fast-spiking basket cells

Dendrites of fast-spiking basket cells (FS BCs) impact neural circuit functions in brain with both supralinear and sublinear integration strategies. Diverse spatial synaptic inputs and active properties of dendrites lead to distinct neuronal firing patterns. How the FS BCs with this bi-modal dendritic integration respond to different spatial dispersion of synaptic inputs remains unclear. In this study, we construct a multi-compartmental model of FS BC and analyze neuronal firings following simulated synaptic protocols from fully clustered to fully dispersed. Under these stimulation protocols, we find that supralinear dendrites dominate somatic firing of FS BC, while the preference for dispersing is due to sublinear dendrites. Moreover, we find that dendritic diameter and Ca2+-permeable AMPA conductance play an important role in it, while A-type K+ channel and NMDA conductance have little effect. The obtained results may give some implications for understanding dendritic computation.

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Sub-cellular Computation

Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. Recent research has shown that cascade models can capture single-cell computation well, but there are still a number of sub-cellular, regenerative dendritic phenomena that they cannot capture, such as the interaction between sodium, calcium, and NMDA spikes in different compartments. Here, we propose that it is possible to capture these additional phenomena using parallel, recurrent cascade models, wherein an individual neuron is modelled as a cascade of parallel linear and non-linear operations that can be connected recurrently, akin to a multi-layer, recurrent, artificial neural network. Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.

Variation in Pyramidal Cell Morphology Across the Human Anterior Temporal Lobe

Pyramidal neurons are the most abundant and characteristic neuronal type in the cerebral cortex and their dendritic spines are the main postsynaptic elements of cortical excitatory synapses. Previous studies have shown that pyramidal cell structure differs across layers, cortical areas, and species. However, within the human cortex, the pyramidal dendritic morphology has been quantified in detail in relatively few cortical areas. In the present work, we performed intracellular injections of Lucifer Yellow at several distances from the temporal pole. We found regional differences in pyramidal cell morphology, which showed large inter-individual variability in most of the morphological variables measured. However, some values remained similar in all cases. The smallest and least complex cells in the most posterior temporal region showed the greatest dendritic spine density. Neurons in the temporal pole showed the greatest sizes with the highest number of spines. Layer V cells were larger, more complex, and had a greater number of dendritic spines than those in layer III. The present results suggest that, while some aspects of pyramidal structure are conserved, there are specific variations across cortical regions, and species.

Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders

The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.

Illuminating dendritic function with computational models

Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires - and drives - new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.

ShuTu: Open-Source Software for Efficient and Accurate Reconstruction of Dendritic Morphology

Neurons perform computations by integrating inputs from thousands of synapses-mostly in the dendritic tree-to drive action potential firing in the axon. One fruitful approach to studying this process is to record from neurons using patch-clamp electrodes, fill the recorded neurons with a substance that allows subsequent staining, reconstruct the three-dimensional architectures of the dendrites, and use the resulting functional and structural data to develop computer models of dendritic integration. Accurately producing quantitative reconstructions of dendrites is typically a tedious process taking many hours of manual inspection and measurement. Here we present ShuTu, a new software package that facilitates accurate and efficient reconstruction of dendrites imaged using bright-field microscopy. The program operates in two steps: (1) automated identification of dendritic processes, and (2) manual correction of errors in the automated reconstruction. This approach allows neurons with complex dendritic morphologies to be reconstructed rapidly and efficiently, thus facilitating the use of computer models to study dendritic structure-function relationships and the computations performed by single neurons.

Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators

Interneurons are critical for the proper functioning of neural circuits. While often morphologically complex, their dendrites have been ignored for decades, treating them as linear point neurons. Exciting new findings reveal complex, non-linear dendritic computations that call for a new theory of interneuron arithmetic. Using detailed biophysical models, we predict that dendrites of FS basket cells in both hippocampus and prefrontal cortex come in two flavors: supralinear, supporting local sodium spikes within large-volume branches and sublinear, in small-volume branches. Synaptic activation of varying sets of these dendrites leads to somatic firing variability that cannot be fully explained by the point neuron reduction. Instead, a 2-stage artificial neural network (ANN), with sub- and supralinear hidden nodes, captures most of the variance. Reduced neuronal circuit modeling suggest that this bi-modal, 2-stage integration in FS basket cells confers substantial resource savings in memory encoding as well as the linking of memories across time.

Basal tree complexity shapes functional pathways in the prefrontal cortex

While the morphology of basal dendritic trees in cortical pyramidal neurons varies, the functional implications of this diversity are just starting to emerge. In layer 5 pyramidal neurons of the prefrontal cortex, for example, increased basal tree complexity determines the recruitment of these neurons into functional circuits. Here, we use a modeling approach to investigate whether and how the morphology of the basal tree mediates the functional output of neurons. We implemented 57 basal tree morphologies of layer 5 prefrontal pyramidal neurons of the rat and identified morphological types that were characterized by different response features, forming distinct functional types. These types were robust to a wide range of manipulations (distribution of active ionic mechanisms, NMDA conductance, somatic and apical tree morphology, or the number of activated synapses) and supported different temporal coding schemes at both the single neuron and the microcircuit level. We predict that the basal tree morphological diversity among neurons of the same class mediates their segregation into distinct functional pathways. Extension of our approach/findings to other cortical areas and/or layers or under pathological conditions may provide a generalized role of the basal trees for neuronal function.NEW & NOTEWORTHY Our results suggest that the segregation of neurons to different functional types based on their basal tree morphology is in large part independent of the distribution of active ionic mechanisms, NMDA conductance, somatic and apical tree morphology, and the number of activated synapses; different functional types support distinct temporal coding schemes. This can be exploited to create networks with diverse coding characteristics, thus contributing to the functional heterogeneity within the same layer and area.

Inhibitory interneurons and their circuit motifs in the many layers of the barrel cortex

Recent years have seen substantial progress in studying the structural and functional properties of GABAergic interneurons and their roles in the neuronal networks of barrel cortex. Although GABAergic interneurons represent only about 12% of the total number of neocortical neurons, they are extremely diverse with respect to their structural and functional properties. It has become clear that barrel cortex interneurons not only serve the maintenance of an appropriate excitation/inhibition balance but also are directly involved in sensory processing. In this review we present different interneuron types and their axonal projection pattern framework in the context of the laminar and columnar organization of the barrel cortex. The main focus is here on the most prominent interneuron types, i.e. basket cells, chandelier cells, Martinotti cells, bipolar/bitufted cells and neurogliaform cells, but interneurons with more unusual axonal domains will also be mentioned. We describe their developmental origin, their classification with respect to molecular, morphological and intrinsic membrane and synaptic properties. Most importantly, we will highlight the most prominent circuit motifs these interneurons are involved in and in which way they serve feed-forward inhibition, feedback inhibition and disinhibition. Finally, this will be put into context to their functional roles in sensory signal perception and processing in the whisker system and beyond.

Area-Specific Features of Pyramidal Neurons-a Comparative Study in Mouse and Rhesus Monkey

A principal challenge of systems neuroscience is to understand the unique characteristics of cortical neurons and circuits that enable area- and species-specific sensory encoding, motor function, cognition, and behavior. To address this issue, we compared properties of layer 3 pyramidal neurons in 2 cortical areas that span a broad range of cortical function-primary sensory (V1), to cognitive (frontal)-in the mouse and the rhesus monkey. Hierarchical clustering and discriminant analyses of 15 physiological and 25 morphological variables revealed 2 fundamental principles. First, V1 and frontal neurons are remarkably similar with regard to nearly every property in the mouse, while the opposite is true in the monkey, with V1 and frontal neurons exhibiting significant differences in nearly every property assessed. Second, neurons within visual and frontal areas differ significantly between the mouse and the monkey. Neurons in mouse and monkey V1 are the same size, but differ in nearly every other way; mouse frontal cortical neurons are smaller than those in the monkey and also differ substantially with regard to most other properties. These findings have broad implications for understanding the differential contributions of heterogeneous neuronal types in construction of cortical microcircuitry in diverse brain areas and species.

Synaptic plasticity in dendrites: complications and coping strategies

The elaborate morphology, nonlinear membrane mechanisms and spatiotemporally varying synaptic activation patterns of dendrites complicate the expression, compartmentalization and modulation of synaptic plasticity. To grapple with this complexity, we start with the observation that neurons in different brain areas face markedly different learning problems, and dendrites of different neuron types contribute to the cell's input-output function in markedly different ways. By committing to specific assumptions regarding a neuron's learning problem and its input-output function, specific inferences can be drawn regarding the synaptic plasticity mechanisms and outcomes that we 'ought' to expect for that neuron. Exploiting this assumption-driven approach can help both in interpreting existing experimental data and designing future experiments aimed at understanding the brain's myriad learning processes.