Chou, P.-J. | Search bioRxiv

bioRxiv2025

Purkinje cell branch morphology determines effect of inhibition and SK2 modulation on somatic pauses

Characterized by a highly complex branching of their dendrites, Purkinje cells (PCs) have a unique architecture that enables them to receive impressive amounts of sensorimotor information through their parallel fiber (PF) input. They are tasked to encode this information with high accuracy. In this work, we discuss the mechanisms through which PCs encode this information, and we show how they multiplex between linear-rate and burst-pause coding. Particularly, somatic pauses are of utmost importance due to their involvement in learning. Using a novel heterogeneous model, we show that all branches can achieve a burst-pause response in response to branch-specific PF clustered input. We quantify the somatic pauses obtained and propose various mechanisms to alter the pause duration. Firstly, our results show that increasing local SK2 channel conductance density systematically increases pause duration. In four branches somatic pauses occurred only when SK2 conductance was increased. Interestingly, when adding feed-forward inhibition via stellate cells, our results show either an increase or a decrease in somatic pauses, highlighting the important role of branch morphology and branch location within the PC. Significance statementPurkinje cells are characterized by highly intricate dendritic branches, which enables them to encode sensorimotor information with great accuracy. Their somatic pauses following excitatory input have been shown to have a strong impact in learning. However, little is known about the impact of morphology and inhibitory input on somatic pauses and implicitly on the learning capacity. In this study, we propose a heterogeneous Purkinje cell model which highlights the importance of branch-specific dendritic morphology on somatic responses. We uncover two different mechanisms for modulating the length of the somatic pauses: density of SK2 channels and feed-forward inhibition via stellate cells.

neuroscience↗

bioRxiv2024

Unique Asymmetric Branching of Drosophila Neurons Optimizes Temporal Dendritic Computation

Neurons execute a versatile array of computations through a complex interplay of factors, including their morphology and synaptic architecture. Dendritic branching encodes upstream inputs into diverse spike patterns transmitted via downstream axons. While earlier studies highlighted the distinct morphologies and functions of a few representative neurons, the availability of large-scale electron microscopy and fluorescent imaging now enables comprehensive data analysis and further simulations to explore structure-function relationships more broadly. This study investigates the general morphological characteristics of diverse neuron types in the fly model. By employing the Strahler Order (SO) metric, we identified a specific bias towards asymmetry in neuronal branching and further investigated the effect of the asymmetry on computational capabilities. Specifically, symmetric branching enhances coincidence detection capability, whereas asymmetric branching increases input order-selectivity. While certain neurons exhibit extreme symmetry or asymmetry optimized for specific tasks, most neurons strike a balance between these computational strategies. This balance underscores the intricate relationship between neuronal structure and function. In contrast to the wide range of branching symmetries found in random bifurcation models, neurons across different species exhibit species-specific asymmetry, suggesting shared underlying branching mechanisms. Our findings provide a fresh perspective on the exploration of neuronal morphologies and their computational roles. Significance StatementThis study reveals a novel structure-function relationship by analyzing the asymmetric branching patterns of fly neurons using extensive morphological data. While certain neurons display extreme symmetry or asymmetry for specialized computational roles, most converge toward a characteristic degree of asymmetry to balance their computational demands, especially in larger branching structures. This suggests that neuronal branching may be governed by intrinsic principles that support both developmental and functional needs.

neuroscience↗