A single-cell level and connectome-derived computational model of the <i>Drosophila</i> brain

Huang, Yu-Chi; Wang, Cheng-Te; Su, Ta-Shun; Kao, Kuo-Wei; Lin, Yen-Jen; Chiang, Ann‐Shyn; Lo, Chung-Chuan

doi:10.1101/391474

Cited by 6 publications

(18 citation statements)

References 67 publications

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“…First of all, it has been widely reported that inhibitory neurons increase the variability of a network [26, 27, 28, 29], and supporting evidence from connectome studies revealed that recurrent connections are abundant [30, 31]. In particular, mutual inhibition is abundant and formed locally [32, 26], possibly performing local computations [33, 22]. Secondly, evident shows that there are functional differences between feedback and mutual inhibition.…”

Section: Introductionmentioning

confidence: 89%

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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: Introductionmentioning

confidence: 89%

“…All simulations in this study are ran on flysim [35]. Each individual neuron was modeled as a leaky integrate- and-fire neuron: where C m is the capacitance, V the membrane potential, τ is the time constant, E L the reversal potential, and the current from the j th presynaptic neuron, and r represents the receptor.…”

Section: Methodsmentioning

confidence: 99%

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Liu

White

2020

Preprint

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“…The second stakeholder group consists of computational/theoretical neuroscientists. Several computational models have been proposed to study whole brain function [25], the early visual system [26] and visual motion detection [27,28]. Electrophysiology data has been used to identify the computation underlying photoreceptor function [29].…”

Section: Figurementioning

confidence: 99%

“…The connectomic and anatomical data currently in the FFBO platform includes all available open fly brain data from the (i) FlyCircuit [20], spanning some 20,000 neurons and 1,260,000 inferred synaptic connections [25], and (ii) the Janelia seven column Medulla EM reconstruction that includes some 500 neurons and 67,000 synapses [15,42], and (iii) the Janelia larval EM reconstruction that currently includes some 500 neurons and 138,000 synapses [43,44]. The physiological data currently in the database consists of 1.6 hours of electrophysiology recordings from photoreceptors [29,45], olfactory sensory neurons [46] and antennal lobe projection neurons [47].…”

Section: The Fruit Fly Brain Observatorymentioning

confidence: 99%

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The Fruit Fly Brain Observatory: From Structure to Function

Nh¹,

C²,

Tomkins

et al. 2019

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The fruit fly is a key model organism for studying the activity of interconnected brain circuits. A large scattered global research community of neurobiologists and neurogeneticists, computational and theoretical neuroscientists, and computer scientists and engineers has been developing a vast trove of experimental and modeling data that has yet to be distilled into new knowledge and understanding of the functional logic of the brain. Developing open shared models, modelling tools and data repositories that can be accessed from anywhere in the world is the necessary engine for accelerating our understanding of how the brain works.To that end we developed the Fruit Fly Brain Observatory (FFBO), the next generation open-source platform to support open, collaborative Drosophila neuroscience research. FFBO provides a (i) hub for storing and integrating fruit fly brain research data from multiple data sources worldwide, (ii) unified repository of tools and methods to build, emulate and compare fruit fly brain models in health and disease, and (iii) an open framework for fruit fly brain data processing and model execution. FFBO provides access to application tools for visualizing, configuring, simulating and analyzing computational models of brain circuits of the (i) cell type map, (ii) connectome, (iii) synaptome, and (iv) activity map using intuitive queries in plain English. Tools are provided to extract the function inherent in these structural maps. All applications can be accessed with any modern browser.

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A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain

Lazar

Türkcan

Zhou

2021

Preprint

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The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain at this scale posits significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for defining functional circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of massive local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe.

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A single-cell level and connectome-derived computational model of the Drosophila brain

Cited by 6 publications

References 67 publications

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

The Fruit Fly Brain Observatory: From Structure to Function

A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain

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