Branching morphology determines signal propagation dynamics in neurons

Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice

The brain is an intricate system that controls a variety of functions. It consists of a vast number of cells that exhibit diverse characteristics. To understand brain function in health and disease, it is crucial to classify neurons accurately. Recent advancements in machine learning have provided a way to classify neurons based on their electrophysiological activity. This paper presents a deep-learning framework that classifies neurons solely on this basis. The framework uses data from the Allen Cell Types database, which contains a survey of biological features derived from single-cell recordings from mice and humans. The shared information from both sources is used to classify neurons into their broad types with the help of a joint model. An accurate domain-adaptive model, integrating electrophysiological data from both mice and humans, is implemented. Furthermore, data from mouse neurons, which also includes labels of transgenic mouse lines, is further classified into subtypes using an interpretable neural network model. The framework provides state-of-the-art results in terms of accuracy and precision while also providing explanations for the predictions.

Skin Innervation

All layers and appendages of the skin are densely innervated by afferent and efferent neurons providing sensory information and controlling skin perfusion and sweating. In mice, neuronal functions have been comprehensively linked to unique single-cell expression patterns and to characteristic arborization of nerve endings in skin and dorsal horn, whereas for humans, specific molecular markers for functional classes of afferent neurons are still lacking. Moreover, bidirectional communication between sensory neurons and local skin cells has become of particular interest, resulting in a broader physiological understanding of sensory function but also of trophic functions and immunomodulation in disease states.

Spike transmission failures in axons from mouse cortical pyramidal neurons in vivo

The propagation of action potentials along axons is traditionally considered to be reliable, as a consequence of the high safety factor of action potential propagation. However, numerical simulations have suggested that, at high frequencies, spikes could fail to invade distal axonal branches. Given the complex morphologies of axonal trees, with extensive branching and long-distance projections, spike propagation failures could be functionally important. To explore this experimentally in vivo, we used an axonal-targeted calcium indicator to image action potentials at axonal terminal branches in superficial layers from mouse somatosensory cortical pyramidal neurons. We activated axons with an extracellular electrode, varying stimulation frequencies, and computationally extracted axonal morphologies and associated calcium responses. We find that axonal boutons have higher calcium accumulations than their parent axons, as was reported in vitro. But, contrary to previous in vitro results, our data reveal spike failures in a significant subset of branches, as a function of branching geometry and spike frequency. The filtering is correlated with the geometric ratio of the branch diameters, as expected by cable theory. These findings suggest that axonal morphologies contribute to signal processing in the cortex.

Neuronal branching is increasingly asymmetric near synapses, potentially enabling plasticity while minimizing energy dissipation and conduction time

Neurons' primary function is to encode and transmit information in the brain and body. The branching architecture of axons and dendrites must compute, respond and make decisions while obeying the rules of the substrate in which they are enmeshed. Thus, it is important to delineate and understand the principles that govern these branching patterns. Here, we present evidence that asymmetric branching is a key factor in understanding the functional properties of neurons. First, we derive novel predictions for asymmetric scaling exponents that encapsulate branching architecture associated with crucial principles such as conduction time, power minimization and material costs. We compare our predictions with extensive data extracted from images to associate specific principles with specific biophysical functions and cell types. Notably, we find that asymmetric branching models lead to predictions and empirical findings that correspond to different weightings of the importance of maximum, minimum or total path lengths from the soma to the synapses. These different path lengths quantitatively and qualitatively affect energy, time and materials. Moreover, we generally observe that higher degrees of asymmetric branching-potentially arising from extrinsic environmental cues and synaptic plasticity in response to activity-occur closer to the tips than the soma (cell body).

Neuronal Branching is Increasingly Asymmetric Near Synapses, Potentially Enabling Plasticity While Minimizing Energy Dissipation and Conduction Time

Neurons' primary function is to encode and transmit information in the brain and body. The branching architecture of axons and dendrites must compute, respond, and make decisions while obeying the rules of the substrate in which they are enmeshed. Thus, it is important to delineate and understand the principles that govern these branching patterns. Here, we present evidence that asymmetric branching is a key factor in understanding the functional properties of neurons. First, we derive novel predictions for asymmetric scaling exponents that encapsulate branching architecture associated with crucial principles such as conduction time, power minimization, and material costs. We compare our predictions with extensive data extracted from images to associate specific principles with specific biophysical functions and cell types. Notably, we find that asymmetric branching models lead to predictions and empirical findings that correspond to different weightings of the importance of maximum, minimum, or total path lengths from the soma to the synapses. These different path lengths quantitatively and qualitatively affect energy, time, and materials. Moreover, we generally observe that higher degrees of asymmetric branching- potentially arising from extrinsic environmental cues and synaptic plasticity in response to activity- occur closer to the tips than the soma (cell body).

Does the Brain Function as a Quantum Phase Computer Using Phase Ternary Computation?

Here we provide evidence that the fundamental basis of nervous communication is derived from a pressure pulse/soliton capable of computation with sufficient temporal precision to overcome any processing errors. Signalling and computing within the nervous system are complex and different phenomena. Action potentials are plastic and this makes the action potential peak an inappropriate fixed point for neural computation, but the action potential threshold is suitable for this purpose. Furthermore, neural models timed by spiking neurons operate below the rate necessary to overcome processing error. Using retinal processing as our example, we demonstrate that the contemporary theory of nerve conduction based on cable theory is inappropriate to account for the short computational time necessary for the full functioning of the retina and by implication the rest of the brain. Moreover, cable theory cannot be instrumental in the propagation of the action potential because at the activation-threshold there is insufficient charge at the activation site for successive ion channels to be electrostatically opened. Deconstruction of the brain neural network suggests that it is a member of a group of Quantum phase computers of which the Turing machine is the simplest: the brain is another based upon phase ternary computation. However, attempts to use Turing based mechanisms cannot resolve the coding of the retina or the computation of intelligence, as the technology of Turing based computers is fundamentally different. We demonstrate that that coding in the brain neural network is quantum based, where the quanta have a temporal variable and a phase-base variable enabling phase ternary computation as previously demonstrated in the retina.

Chemical Modulation of Mitochondria-Endoplasmic Reticulum Contact Sites

Contact sites between mitochondria and endoplasmic reticulum (ER) are points in which the two organelles are in close proximity. Due to their structural and functional complexity, their exploitation as pharmacological targets has never been considered so far. Notwithstanding, the number of compounds described to target proteins residing at these interfaces either directly or indirectly is rising. Here we provide original insight into mitochondria–ER contact sites (MERCs), with a comprehensive overview of the current MERCs pharmacology. Importantly, we discuss the considerable potential of MERCs to become a druggable target for the development of novel therapeutic strategies.

Adult-Born Hippocampal Neurons Undergo Extended Development and Are Morphologically Distinct from Neonatally-Born Neurons

During immature stages, adult-born neurons pass through critical periods for survival and plasticity. It is generally assumed that by 2 months of age adult-born neurons are mature and equivalent to the broader neuronal population, raising questions of how they might contribute to hippocampal function in old age when neurogenesis has declined. However, few have examined adult-born neurons beyond the critical period or directly compared them to neurons born in infancy. Here, we used a retrovirus to visualize functionally relevant morphological features of 2- to 24-week-old adult-born neurons in male rats. From 2 to 7 weeks, neurons grew and attained a relatively mature phenotype. However, several features of 7-week-old neurons suggested a later wave of growth: these neurons had larger nuclei, thicker dendrites, and more dendritic filopodia than all other groups. Indeed, between 7 and 24 weeks, adult-born neurons gained additional dendritic branches, formed a second primary dendrite, acquired more mushroom spines, and had enlarged mossy fiber presynaptic terminals. Compared with neonatal-born neurons, old adult-born neurons had greater spine density, larger presynaptic terminals, and more putative efferent filopodial contacts onto inhibitory neurons. By integrating rates of cell birth and growth across the life span, we estimate that adult neurogenesis ultimately produces half of the cells and the majority of spines in the dentate gyrus. Critically, protracted development contributes to the plasticity of the hippocampus through to the end of life, even after cell production declines. Persistent differences from neonatal-born neurons may additionally endow adult-born neurons with unique functions even after they have matured.SIGNIFICANCE STATEMENT Neurogenesis occurs in the hippocampus throughout adult life and contributes to memory and emotion. It is generally assumed that new neurons have the greatest impact on behavior when they are immature and plastic. However, since neurogenesis declines dramatically with age, it is unclear how they might contribute to behavior later in life when cell proliferation has slowed. Here we find that newborn neurons mature over many months in rats and may end up with distinct morphological features compared with neurons born in infancy. Using a mathematical model, we estimate that a large fraction of neurons is added in adulthood. Moreover, their extended growth produces a reserve of plasticity that persists even after neurogenesis has declined to low rates.

Measurements of neuronal morphological variation across the rat neocortex

Neuron morphology is highly variable across the mammalian brain. It is thought that these attributes of neuronal cell shape, such as soma surface area and branching frequency, are determined by biological function and information processing. In this study, a large data set of neurons across the rat neocortex were clustered by their anatomical characters for evidence of distinctiveness among neocortical regions and the somatosensory layers. This data set of neuronal morphologies was compiled from 31 different lab sources with a validation procedure so that data records are potentially comparable across research studies. With this large set of heterogeneous data and by clustering analysis, this study shows that neuronal morphological traits overlap among neocortical and somatosensory regions. In the context of past neuroanatomical studies, this result is not congruent with tissue level analysis and strongly suggests further sampling of neuronal data to lessen the effect of confounding factors, such as the influence of different methodologies from use of heterogeneous samples of neuronal data.

Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification

Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions.

Abnormal Reinnervation of Denervated Areas Following Nerve Injury Facilitates Neuropathic Pain

An injury to peripheral nerves leads to skin denervation, which often is followed by increased pain sensitivity of the denervated areas and the development of neuropathic pain. Changes in innervation patterns during the reinnervation process of the denervated skin could contribute to the development of neuropathic pain. Here, we examined the changes in the innervation pattern during reinnervation and correlated them with the symptoms of neuropathic pain. Using a multispectral labeling technique—PainBow, which we developed, we characterized dorsal root ganglion (DRG) neurons innervating distinct areas of the rats’ paw. We then used spared nerve injury, causing partial denervation of the paw, and examined the changes in innervation patterns of the denervated areas during the development of allodynia and hyperalgesia. We found that, differently from normal conditions, during the development of neuropathic pain, these areas were mainly innervated by large, non-nociceptive neurons. Moreover, we found that the development of neuropathic pain is correlated with an overall decrease in the number of DRG neurons innervating these areas. Importantly, treatment with ouabain facilitated reinnervation and alleviated neuropathic pain. Our results suggest that local changes in peripheral innervation following denervation contribute to neuropathic pain development. The reversal of these changes decreases neuropathic pain.

Plasticity in striatal dopamine release is governed by release-independent depression and the dopamine transporter

Mesostriatal dopaminergic neurons possess extensively branched axonal arbours. Whether action potentials are converted to dopamine output in the striatum will be influenced dynamically and critically by axonal properties and mechanisms that are poorly understood. Here, we address the roles for mechanisms governing release probability and axonal activity in determining short‐term plasticity of dopamine release, using fast‐scan cyclic voltammetry in the ex vivo mouse striatum. We show that brief short‐term facilitation and longer short term depression are only weakly dependent on the level of initial release, i.e. are release insensitive. Rather, short-term plasticity is strongly determined by mechanisms which govern axonal activation, including K⁺‐gated excitability and the dopamine transporter, particularly in the dorsal striatum. We identify the dopamine transporter as a master regulator of dopamine short‐term plasticity, governing the balance between release‐dependent and independent mechanisms that also show region‐specific gating.

Dopamine release in the striatum has important roles in action selection and in disorders such as Parkinson’s disease. The authors here show that short-term plasticity of dopamine release is strongly determined by axonal activation and dopamine transporters.

Axon Collaterals and Brain States

Multiple mechanisms have been identified as relevant to plasticity, functional stability, and reliable processing across brain states. In the context of stability under "ever-changing conditions" (this Topic), the role of axons has been relatively under-investigated. The highly branched topologies of many axons, however, seem well designed to differentially recruit and regulate distributed postsynaptic groups, possibly in a state-dependent fashion. In this Perspective, I briefly discuss several examples of axon collateralization, and then some of the branch-specific features that might subserve differential recruitment and whole brain activation. An emerging principle is that the number of collaterals and number of target structures are not stereotyped. Rather, axons originating from one defined source typically send branches to diversified subsets of target areas. This could achieve heterogeneous inputs, with different degrees of synchronicity. Variability of neuronal responses has been suggested as inversely proportional to the degree of temporally correlated input. Increased input homogeneity, driven by sensory stimulation or behavioral conditions, is reported to reduce neuronal variability, with axon collateralization potentially having an important role.

Age-related changes in the structure and function of mammalian neuromuscular junctions

As mammals age, their neuromuscular junctions (NMJs) change their form, with an increasingly complex system of axonal branches innervating increasingly fragmented regions of postsynaptic differentiation. It has been suggested that this remodeling is associated with impairment of neuromuscular transmission and that this contributes to age-related muscle weakness in mammals, including humans. Here, we review previous work on NMJ aging, most of which has focused on either structure or function, as well as a new study aimed at seeking correlation between the structure and function of individual NMJs. While it is clear that extensive structural changes occur as part of the aging process, it is much less certain how, if at all, these are correlated with an impairment of function. This leaves open the question of whether loss of NMJ function is a significant cause of age-related muscle weakness.