Abstract:The vast tree-like dendritic structure of neurons allows them to receive and integrate input from many neurons. A wide variety of neuronal morphologies exist, however, their role in dendritic integration, and how it shapes the response of the neuron, is not yet fully understood. Here, we study the evolution and interactions of dendritic spikes in excitable neurons with complex real branch structures. We focus on dozens of digitally reconstructed illustrative neurons from the online repository NeuroMorpho.org, … Show more
“…6 a that for both “symmetric” and “totally asymmetric” dendrites of the same number of nodes, the dynamic range increases with number of branches as well as the interaction probability . This is consistent with published results 24 for neurons from the online repository NeuroMorpho database, though total number of nodes may vary among neurons there. Figure 6 ( a ) The dynamic range of symmetric (solid lines) and totally asymmetric (dashed lines) dendrites of nodes with various number of somatic branches at different values; from top to bottom lines (orange), 0.8 (red), 0.5 (blue) respectively.…”
Section: Resultssupporting
confidence: 92%
“…Dendrites generating spikes for enhancement of activity and the spiking interactions require energy. We compute relative energy consumption E (see “ Methods ” for details) which quantifies how many times on average dendritic compartments activate for each somatic spike as in 24 . A higher energy consumption E indicates less efficiency in spiking transmission.…”
Section: Resultsmentioning
confidence: 99%
“…The relative energy consumption as described in 24 , 29 quantifies how active the whole dendritic tree is compared to the soma, that is, how many times, on average, dendritic compartments activate for each somatic spike. Explicitly, for a dentritic tree with N nodes including somatic node, its relative energy consumptions is defined as where and respectively represent the spiking number of dendrites and soma respectively within a time window considered.…”
Section: Methodsmentioning
confidence: 99%
“…Particularly for dendrites (often modelled as composition of binary trees connecting to the soma), it has been suggested that larger binary trees have larger dynamic range and that blocking of active dendritic branchlets in binary trees would lead to a decrease in dynamic range 16 , 23 using both mean-field approximation and stochastic simulations. More recently, using digital reconstructions of neurons from the NeuroMorpho database, it has been suggested that the location of the soma and the number of somatic branches are key topological factors in determining both the neuron’s dynamic range as well as its energy consumption 24 .…”
Dendrites receive and process signals from other neurons. The range of signal intensities that can be robustly distinguished by dendrites is quantified by the dynamic range. We investigate the dynamic range and information transmission efficiency of dendrites in relation to dendritic morphology. We model dendrites in a neuron as multiple excitable binary trees connected to the soma where each node in a tree can be excited by external stimulus or by receiving signals transmitted from adjacent excited nodes. It has been known that larger dendritic trees have a higher dynamic range. We show that for dendritic tress of the same number of nodes, the dynamic range increases with the number of somatic branches and decreases with the asymmetry of dendrites, and the information transmission is more efficient for dendrites with more somatic branches. Moreover, our simulated data suggest that there is an exponential association (decay resp.) of overall relative energy consumption (dynamic range resp.) in relation to the number of somatic branches. This indicates that further increasing the number of somatic branches (e.g. beyond 10 somatic branches) has limited ability to improve the transmission efficiency. With brain-wide neuron digital reconstructions of the pyramidal cells, 90% of neurons have no more than 10 dendrites. These suggest that actual brain-wide dendritic morphology is near optimal in terms of both dynamic range and information transmission.
“…6 a that for both “symmetric” and “totally asymmetric” dendrites of the same number of nodes, the dynamic range increases with number of branches as well as the interaction probability . This is consistent with published results 24 for neurons from the online repository NeuroMorpho database, though total number of nodes may vary among neurons there. Figure 6 ( a ) The dynamic range of symmetric (solid lines) and totally asymmetric (dashed lines) dendrites of nodes with various number of somatic branches at different values; from top to bottom lines (orange), 0.8 (red), 0.5 (blue) respectively.…”
Section: Resultssupporting
confidence: 92%
“…Dendrites generating spikes for enhancement of activity and the spiking interactions require energy. We compute relative energy consumption E (see “ Methods ” for details) which quantifies how many times on average dendritic compartments activate for each somatic spike as in 24 . A higher energy consumption E indicates less efficiency in spiking transmission.…”
Section: Resultsmentioning
confidence: 99%
“…The relative energy consumption as described in 24 , 29 quantifies how active the whole dendritic tree is compared to the soma, that is, how many times, on average, dendritic compartments activate for each somatic spike. Explicitly, for a dentritic tree with N nodes including somatic node, its relative energy consumptions is defined as where and respectively represent the spiking number of dendrites and soma respectively within a time window considered.…”
Section: Methodsmentioning
confidence: 99%
“…Particularly for dendrites (often modelled as composition of binary trees connecting to the soma), it has been suggested that larger binary trees have larger dynamic range and that blocking of active dendritic branchlets in binary trees would lead to a decrease in dynamic range 16 , 23 using both mean-field approximation and stochastic simulations. More recently, using digital reconstructions of neurons from the NeuroMorpho database, it has been suggested that the location of the soma and the number of somatic branches are key topological factors in determining both the neuron’s dynamic range as well as its energy consumption 24 .…”
Dendrites receive and process signals from other neurons. The range of signal intensities that can be robustly distinguished by dendrites is quantified by the dynamic range. We investigate the dynamic range and information transmission efficiency of dendrites in relation to dendritic morphology. We model dendrites in a neuron as multiple excitable binary trees connected to the soma where each node in a tree can be excited by external stimulus or by receiving signals transmitted from adjacent excited nodes. It has been known that larger dendritic trees have a higher dynamic range. We show that for dendritic tress of the same number of nodes, the dynamic range increases with the number of somatic branches and decreases with the asymmetry of dendrites, and the information transmission is more efficient for dendrites with more somatic branches. Moreover, our simulated data suggest that there is an exponential association (decay resp.) of overall relative energy consumption (dynamic range resp.) in relation to the number of somatic branches. This indicates that further increasing the number of somatic branches (e.g. beyond 10 somatic branches) has limited ability to improve the transmission efficiency. With brain-wide neuron digital reconstructions of the pyramidal cells, 90% of neurons have no more than 10 dendrites. These suggest that actual brain-wide dendritic morphology is near optimal in terms of both dynamic range and information transmission.
“…This evolutionary acceleration in complexity is recapitulated ontogenically in the rapid increase in Purkinje cell dendritic complexity during fetal development and infancy [ 107 ]. The resulting exuberant dendritic tree confers to Purkinje cells a surface area that is several orders of magnitude larger than any other neuronal cell type [ 108 , 109 ]. The cerebellum is highly cellular and may contain over four times more neurons than the neocortex [ 110 ].…”
Section: Symptom Progression In Late-onset Gm2 Gangliosidosesmentioning
Aging is a main risk factor for neurodegenerative disorders including Alzheimer's disease. It is often accompanied by reduced cognitive functions, gray-matter volume, and dendritic integrity. Although age-related brain structural changes have been observed across multiple scales, their functional implications remain largely unknown. Here we simulate the aging effects on neuronal morphology as dendritic pruning and characterize its dynamical implications. Utilizing a detailed computational modeling approach, we simulate the dynamics of digitally reconstructed neurons obtained from Neuromorpho.org. We show that dendritic pruning affects neuronal integrity: firing rate is reduced, causing a reduction in energy consumption, energy efficiency, and dynamic range. Pruned neurons require less energy but their function is often impaired, which can explain the diminished ability to distinguish between similar experiences (pattern separation) in older people. Our measures indicate that the resilience of neuronal dynamics is neuron-specific, heterogeneous, and strongly affected by dendritic topology and the position of the soma. Based on the emergent neuronal dynamics, we propose to classify the effects of dendritic deterioration, and put forward a topological measure of “neuronal reserve” that quantifies the resilience of neuronal dynamics to dendritic pruning. Moreover, our findings suggest that increasing dendritic excitability could partially mitigate the dynamical effects of aging.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
