Biology subjects

Jin, D. Z.

Publications and source records attributed to Jin, D. Z..

6 recordsLinked to original sources

bioRxiv2025

Temperature robustness of the timing network within songbird premotor nucleus HVC

Many neuronal processes are temperature-sensitive. Cooling by 10 {degrees}C typically slows ion channel dynamics by more than a factor of two (Q10 > 2). Nevertheless, behaviors can remain robust despite variations in brain temperature. For instance, cooling the premotor nucleus HVC in zebra finches by 10 {degrees}C slows song production by only a factor of Q10 [~] 1.3. Here we examine the temperature robustness of the synaptic chain network within HVC. Burst spike propagation along such a chain network is postulated to control the tempo of the song. We show that the dynamics of this network are resilient to cooling and that the slowing of burst propagation exhibits a Q10 similar to that observed for the song. We identify two key factors underlying this robustness: the reliance on axonal delays, which are more resistant to temperature changes than ion channels, and enhanced synaptic efficacy at lower temperatures. We propose that these mechanisms represent general principles by which neural circuits maintain functional stability despite temperature fluctuations in the brain. Significance StatementMany animal behaviors remain robust despite temperature fluctuations in the brain. By studying timing circuits in songbirds, we identify key circuit elements that contribute to this resilience, including axonal delays and synaptic integration. Our work highlights how these mechanisms interact to maintain stable neuronal dynamics in response to temperature changes.

neuroscience↗

bioRxiv2022

A method of inferring partially observable Markov models from syllable sequences reveals the effects of deafening on Bengalese finch song syntax

Generative models have broad applications, ranging from language processing to analyzing bird-song. In this study, we demonstrate how a statistical test, designed to prevent overgeneralization in sequence generation, can be used to deduce minimal models for the syllable sequences in Bengalese finch songs. We focus on the partially observable Markov model (POMM), which consists of states and the probabilistic transitions between them. Each state is associated with a specific syllable, with the possibility of multiple states being associated to a single syllable. This feature sets the POMM apart from a standard Markov model, where each syllable is associated to just one state. This multiplicity suggests that syllable transitions are influenced by the specific contexts in which the transitions appear. We apply this method to analyze the songs of six adult male Bengalese finches, both before and after they are deafened. Our findings indicate that auditory feedback is crucial in shaping the context-dependent syllable transitions characteristic of Bengalese finch songs. SignificanceGenerative models are adept at representing sequences where the order of elements, such as words or birdsong syllables, depends on the context. In this study, we demonstrate that a probabilistic model, inspired by neural encoding of song production in songbirds, effectively captures context-dependent transitions of syllables in Bengalese finch songs. Our findings indicate that the absence of auditory input, as seen in deafened finches, diminishes these context dependencies. This implies that auditory feedback is vital for establishing context-based sequencing in their songs. Our method can be applied to various behavioral sequences, offering insights into the neural underpinnings that govern statistical patterns in these sequences.

neuroscience↗

bioRxiv2022

Cortical Betz cells analogue in songbirds utilizes Kv3.1 to generate ultranarrow spikes

Complex motor skills in vertebrates require specialized upper motor neurons with precise action potential (AP) firing. To examine how diverse populations of upper motor neurons subserve distinct functions and the specific repertoire of ion channels involved, we conducted a thorough study of the excitability of upper motor neurons controlling somatic motor function in the zebra finch. We found that robustus arcopallialis projection neurons (RAPNs), key command neurons for song production, exhibit ultranarrow spikes and higher firing rates compared to neurons controlling non-vocal somatic motor functions (AId neurons). This striking difference was primarily due to the expression of a high threshold, fast-activating voltage-gated K+ channel, Kv3.1 (KCNC1). RAPN properties thus mirror those of the sparse, specialized Betz cells in the motor cortex of humans and other primates, which also fire ultranarrow spikes enabled by Kv3.1 expression. These large layer 5 pyramidal neurons are involved in fine digit control and are notably absent in rodents. Our study thus provides evidence that songbird RAPNs and primate Betz cells have convergently evolved the use of Kv3.1 to ensure precise, rapid AP firing required for fast and complex motor skills.

neuroscience↗

bioRxiv2020

Addition of new neurons and the emergence of a local neural circuit for precise timing

During development, neurons arrive at local brain areas in extended period of time, but how they form local neural circuits is unknown. Here we computationally model the emergence of a network for precise timing in the premotor nucleus HVC in songbird. We show that new motor projection neurons, mostly added to HVC before and during song learning, are recruited to the end of a growing feedforward network. High spontaneous activity of new neurons makes them the prime targets for recruitment in a self-organized process via synaptic plasticity. Once recruited, the new neurons fire readily at precise times, and they become mature. Neurons that are not recruited become silent and replaced by new immature neurons. Our model incorporates realistic HVC features such as interneurons, spatial distributions of neurons, and distributed axonal delays. The model predicts that the birth order of the projection neurons correlates with their burst timing during the song. Significance StatementFunctions of local neural circuits depend on their specific network structures, but how the networks are wired is unknown. We show that such structures can emerge during development through a self-organized process, during which the network is wired by neuron-by-neuron recruitment. This growth is facilitated by steady supply of immature neurons, which are highly excitable and plastic. We suggest that neuron maturation dynamics is an integral part of constructing local neural circuits.

neuroscience↗

bioRxiv2019

Local axonal conduction delays underlie precise timing of a neural sequence

Sequential activation of neurons has been observed during various behavioral and cognitive processes and is thought to play a critical role in their generation. Here, we studied a circuit in the songbird forebrain that drives the performance of adult courtship song. In this region, known as HVC, neurons are sequentially active with millisecond precision in relation to behavior. Using large-scale network models, we found that HVC sequences could only be accurately produced if sequentially active neurons were linked with long and heterogeneous axonal conduction delays. Although such latencies are often thought to be negligible in local microcircuits, we empirically determined that HVC interconnections were surprisingly slow, generating delays up to 22 ms. An analysis of anatomical reconstructions suggests that similar processes may also occur in rat neocortex, supporting the notion that axonal conduction delays can sculpt the dynamical repertoire of a range of local circuits.

neuroscience↗

bioRxiv2017

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 understanding this process is to record from neurons using patch-clamp electrodes, fill the recorded neuron with a substance that allows subsequent staining, reconstruct the three-dimensional architecture 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 process, 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.\n\nSignificance StatementWe developed a software package - ShuTu - that integrates automated reconstruction of stained neurons with manual error correction. This package facilitates rapid reconstruction of the three-dimensional geometry of neuronal dendritic trees, often needed for computational simulations of the functional properties of these structures.

neuroscience↗