Diverse Community Structures in the Neuronal-Level Connectome of the Drosophila Brain

Shih, C. T.; Lin, Yen-Jen; Wang, Cheng-Te; Wang, Ting-Yuan; Chen, Chi‐Chih; Su, Ta-Shun; Lo, Chung-Chuan; Chiang, Ann‐Shyn

doi:10.1007/s12021-019-09443-w

Cited by 15 publications

(17 citation statements)

References 67 publications

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“…This is inline with past research, as connections between inhibitory neurons has been shown in many different contexts [27,28,29,34,35,54]. It is interesting to note that in a recent study [39] that reconstructed singlecell level brain-wide connectome of fruit flies [55,56,57], putative inhibitory neurons receive more inhibitory than the excitatory inputs. Moreover, studies in the hippocampus of rats have found a plethora of mutually inhibitory parvalbumin interneurons [34].…”

Section: Discussionsupporting

confidence: 86%

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Liu

White

2020

Preprint

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One of the most intriguing observations of recurrent neural circuits is their flexibility. Seemingly, this flexibility extends far beyond the ability to learn, but includes the ability to use learned procedures to respond to novel situations. Here, we report that this flexibility arises from the synergistic interplay between recurrent mutual excitation and recurrent mutual inhibition. Specifically, we show that mutual inhibition is critical in expanding the functionality of the circuit, far beyond what feedback inhibition alone can accomplish. By taking advantage of dynamical systems theory and bifurcation analysis, we show mutual inhibition doubles the number of cusp bifurcations in the system in small neural circuits. As a concrete example, we build a simulation model of a class of functional motifs we call Coupled Recurrent inhibitory and Recurrent excitatory Loops (CRIRELs). These CRIRELs have the advantage of being multi-functional, performing a plethora of functions, including decisions, switches, toggles, central pattern generators, depending solely on the input type. We then use bifurcation theory to show how mutual inhibition gives rise to this broad repertoire of possible functions. Finally, we demonstrate how this trend also holds for larger networks, and how mutual inhibition greatly expands the amount of information a recurrent network can hold.

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Section: Discussionsupporting

confidence: 86%

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Liu

White

2020

Preprint

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“…1d) reveal that connectivity-based classification, neurotransmitter, and birth time are practically independent of each other (≈ 0 − 0.20 NMI). While connectivity- based classification largely explains (0.84 NMI) functional-anatomical communities, the reverse is not true (0.43 NMI), suggesting that connectivity-based classification constitutes a high-resolution refinement of functional-anatomical communities [9]. As expected, connectivity-based classification reflects certain aspects of the spatial neuropil distribution (≈ 0.60 NMI).…”

Section: Resultsmentioning

confidence: 93%

“…We begin with a neuron-to-neuron brain-wide connectome of the Drosophila brain comprised of 19,902 single neurons, obtained from the recently released FlyCircuit v1.2 database [9]. The connectome consists of a list of reconstructed neurons, each co-registered to a common brain atlas, and of their axonal-dendritic spatial overlaps identifying potential synaptic connections [10].…”

Section: Methodsmentioning

confidence: 99%

Circuit Analysis of the Drosophila Brain using Connectivity-based Neuronal Classification Reveals Organization of Key Communication Pathways

Mehta

Goldin

Ascoli

2022

Preprint

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We present a functionally relevant, quantitative characterization of the neural circuitry of Drosophila melanogaster at the mesoscopic level of neuron types as classified exclusively based on network connectivity. Starting from a large neuron-to-neuron brain-wide connectome of the fruit fly, we use stochastic block modeling and spectral graph clustering to group neurons together into a common 'cell class' if they connect to neurons of other classes according to the same probability distributions. Thus, nodes of the identified circuit represent cell classes, while the directed edges represent the connection probabilities between neurons in those respective classes. We then characterize the connectivity-based cell classes with standard neuronal biomarkers, including neurotransmitters, developmental birthtimes, morphological features, spatial embedding, and functional anatomy. Mutual information indicates that connectivity-based classification reveals aspects of neurons that are not adequately captured by traditional classification schemes. Next, using graph-theoretic and random walk analyses to identify neuron classes as hubs, sources, or destinations, we detect patterns of directional connectivity and information flow that potentially underpin specific functional interactions in the Drosophila brain. We uncover a core of highly interconnected dopaminergic cell classes functioning as the backbone information highway for multisensory integration. We also recognize glutamatergic-dominant connections between the motor and optic regions, possibly facilitating circadian rhythmic activity. Additional predicted pathways pertain to spatial orientation, fight-or-flight response, and olfactory learning. Mapping the developmental birthtimes of neurons also pinpoints the temporal change of the circuit blueprint over the fly lifespan, revealing distinct critical growth periods of individual pathways. Our analysis provides experimentally testable hypotheses critically deconstructing complex brain function from organized connectomic architecture. We present a functionally relevant, quantitative characterization of the neural circuitry of Drosophila melanogaster at the mesoscopic level of neuron types as classified exclusively based on network connectivity. Starting from a large neuron-to-neuron brain-wide connectome of the fruit fly, we use stochastic block modeling and spectral graph clustering to group neurons together into a common 'cell class' if they connect to neurons of other classes according to the same probability distributions. Thus, nodes of the identified circuit represent cell classes, while the directed edges represent the connection probabilities between neurons in those respective classes. We then characterize the connectivity-based cell classes with standard neuronal biomarkers, including neurotransmitters, developmental birthtimes, morphological features, spatial embedding, and functional anatomy. Mutual information indicates that connectivity-based classification reveals aspects of neurons that are not adequately captured by traditional classification schemes. Next, using graph-theoretic and random walk analyses to identify neuron classes as hubs, sources, or destinations, we detect patterns of directional connectivity and information flow that potentially underpin specific functional interactions in the Drosophila brain. We uncover a core of highly interconnected dopaminergic cell classes functioning as the backbone information highway for multisensory integration. We also recognize glutamatergic-dominant connections between the motor and optic regions, possibly facilitating circadian rhythmic activity. Additional predicted pathways pertain to spatial orientation, fight-or-flight response, and olfactory learning. Mapping the developmental birthtimes of neurons also pinpoints the temporal change of the circuit blueprint over the fly lifespan, revealing distinct critical growth periods of individual pathways. Our analysis provides experimentally testable hypotheses critically deconstructing complex brain function from organized connectomic architecture.

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“…To theoretically explore the effect of heterogeneous connectivity on network dynamics, we assume that the heavytailed distributions of synaptic weights are power laws, the same assumption made in [41] to theoretically study the effect of heterogeneous connectivity on neural dynamics near the edge of chaos. Recent experimental studies have found the existence of power-law synaptic strengths in drosophila whole brain data [42], but note that these are not mammalian brain data. In our heterogeneous network model ( Fig.…”

Section: Heterogeneous Network Modelmentioning

confidence: 96%

Fractional diffusion theory of balanced heterogeneous neural networks

Wardak

Gong

2020

Preprint

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Interactions of large numbers of spiking neurons give rise to complex neural dynamics with fluctuations occurring at multiple scales. Understanding the dynamical mechanisms underlying such complex neural dynamics is a long-standing topic of interest in neuroscience, statistical physics and nonlinear dynamics. Conventionally, fluctuating neural dynamics are formulated as balanced, uncorrelated excitatory and inhibitory inputs with Gaussian properties. However, heterogeneous, non-Gaussian properties have been widely observed in both neural connections and neural dynamics. Here, based on balanced neural networks with heterogeneous, non-Gaussian features, our analysis reveals that in the limit of large network size, synaptic inputs possess power-law fluctuations, leading to a remarkable relation of complex neural dynamics to the fractional diffusion formalisms of non-equilibrium physical systems. By uniquely accounting for the leapovers caused by the fluctuations of spiking activity, we further develop a fractional Fokker-Planck equation with absorbing boundary conditions. This body of formalisms represents a novel fractional diffusion theory of heterogeneous neural networks and results in an exact description of the network activity states. This theory is further implemented in a biologically plausible, balanced neural network and identifies a novel type of network state with rich, nonlinear response properties, providing a unified account of a variety of experimental findings on neural dynamics at the individual neuron and the network levels, including fluctuations of membrane potentials and population firing rates. We illustrate that this novel state endows neural networks with a fundamental computational advantage; that is, the neural response is maximised as a function of structural connectivity. Our theory and its network implementations provide a framework for investigating complex neural dynamics emerging from large networks of spiking neurons and their functional roles in neural processing.

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Diverse Community Structures in the Neuronal-Level Connectome of the Drosophila Brain

Cited by 15 publications

References 67 publications

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Circuit Analysis of the Drosophila Brain using Connectivity-based Neuronal Classification Reveals Organization of Key Communication Pathways

Fractional diffusion theory of balanced heterogeneous neural networks

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