Optogenetic Dissection of Neural Circuit Function in Behaving Animals

AbstractDown Syndrome Cell Adhesion Molecules (dscam and dscaml1) are essential regulators of neural circuit assembly, but their roles in vertebrate neural circuit function are still mostly unexplored. We investigated the role of dscaml1 in the zebrafish oculomotor system, where behavior, circuit function, and neuronal activity can be precisely quantified. Loss of zebrafish dscaml1 resulted in deficits in retinal patterning and light adaptation, consistent with its known roles in mammals. Oculomotor analyses showed that mutants have abnormal gaze stabilization, impaired fixation, disconjugation, and faster fatigue. Notably, the saccade and fatigue phenotypes in dscaml1 mutants are reminiscent of human ocular motor apraxia, for which no animal model exists. Two-photon calcium imaging showed that loss of dscaml1 leads to impairment in the saccadic premotor pathway but not the pretectum-vestibular premotor pathway, indicating a subcircuit requirement for dscaml1. Together, we show that dscaml1 has both broad and specific roles in oculomotor circuit function, providing a new animal model to investigate the development of premotor pathways and their associated human ocular disorders.

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Quantitative synapse analysis for cell-type specific connectomics

10.1101/386912 ◽

2018 ◽

Author(s):

Dika A. Kuljis ◽

Khaled Zemoura ◽

Cheryl A. Telmer ◽

Jiseok Lee ◽

Eunsol Park ◽

...

Keyword(s):

Large Scale ◽

Neural Circuit ◽

Apical Dendrite ◽

Automated Detection ◽

Cell Type ◽

Disease States ◽

Specific Localization ◽

Cell Type Specific ◽

Dendritic Branches ◽

Circuit Function

AbstractAnatomical methods for determining cell-type specific connectivity are essential to inspire and constrain our understanding of neural circuit function. We developed new genetically-encoded reagents for fluorescence-synapse labeling and connectivity analysis in brain tissue, using a fluorogen-activating protein (FAP)-or YFP-coupled, postsynaptically-localized neuroligin-1 targeting sequence (FAP/YFPpost). Sparse viral expression of FAP/YFPpost with the cell-filling, red fluorophore dTomato (dTom) enabled high-throughput, compartment-specific localization of synapses across diverse neuron types in mouse somatosensory cortex. High-resolution confocal image stacks of virally-transduced neurons were used for 3D reconstructions of postsynaptic cells and automated detection of synaptic puncta. We took advantage of the bright, far-red emission of FAPpost puncta for multichannel fluorescence alignment of dendrites, synapses, and presynaptic neurites to assess subtype-specific inhibitory connectivity onto L2 neocortical pyramidal (Pyr) neurons. Quantitative and compartment-specific comparisons show that PV inputs are the dominant source of inhibition at both the soma and across all dendritic branches examined and were particularly concentrated at the primary apical dendrite, a previously unrecognized compartment of L2 Pyr neurons. Our fluorescence-based synapse labeling reagents will facilitate large-scale and cell-type specific quantitation of changes in synaptic connectivity across development, learning, and disease states.

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Rapid and sustained homeostatic control of presynaptic exocytosis at a central synapse

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1909675116 ◽

2019 ◽

Vol 116 (47) ◽

pp. 23783-23789 ◽

Cited By ~ 6

Author(s):

Igor Delvendahl ◽

Katarzyna Kita ◽

Martin Müller

Keyword(s):

Time Scales ◽

Neural Circuit ◽

Mossy Fiber ◽

Pool Size ◽

Homeostatic Plasticity ◽

Experimental Conditions ◽

Central Synapse ◽

Circuit Function ◽

Vesicle Pool ◽

Activity Dependent

Animal behavior is remarkably robust despite constant changes in neural activity. Homeostatic plasticity stabilizes central nervous system (CNS) function on time scales of hours to days. If and how CNS function is stabilized on more rapid time scales remains unknown. Here, we discovered that mossy fiber synapses in the mouse cerebellum homeostatically control synaptic efficacy within minutes after pharmacological glutamate receptor impairment. This rapid form of homeostatic plasticity is expressed presynaptically. We show that modulations of readily releasable vesicle pool size and release probability normalize synaptic strength in a hierarchical fashion upon acute pharmacological and prolonged genetic receptor perturbation. Presynaptic membrane capacitance measurements directly demonstrate regulation of vesicle pool size upon receptor impairment. Moreover, presynaptic voltage-clamp analysis revealed increased Ca2+-current density under specific experimental conditions. Thus, homeostatic modulation of presynaptic exocytosis through specific mechanisms stabilizes synaptic transmission in a CNS circuit on time scales ranging from minutes to months. Rapid presynaptic homeostatic plasticity may ensure stable neural circuit function in light of rapid activity-dependent plasticity.

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The dialectic of Hebb and homeostasis

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2016.0258 ◽

2017 ◽

Vol 372 (1715) ◽

pp. 20160258 ◽

Cited By ~ 79

Author(s):

Gina G. Turrigiano

Keyword(s):

Recent Progress ◽

Neural Circuits ◽

Neural Circuit ◽

Homeostatic Plasticity ◽

Homeostatic Control ◽

Spatial And Temporal Scales ◽

Network Function ◽

Hebbian Plasticity ◽

Circuit Function ◽

Hebbian Mechanisms

It has become widely accepted that homeostatic and Hebbian plasticity mechanisms work hand in glove to refine neural circuit function. Nonetheless, our understanding of how these fundamentally distinct forms of plasticity compliment (and under some circumstances interfere with) each other remains rudimentary. Here, I describe some of the recent progress of the field, as well as some of the deep puzzles that remain. These include unravelling the spatial and temporal scales of different homeostatic and Hebbian mechanisms, determining which aspects of network function are under homeostatic control, and understanding when and how homeostatic and Hebbian mechanisms must be segregated within neural circuits to prevent interference. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

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Adult neurogenesis and modulation of neural circuit function

Current Opinion in Neurobiology ◽

10.1016/j.conb.2011.02.006 ◽

2011 ◽

Vol 21 (2) ◽

pp. 360-364 ◽

Cited By ~ 24

Author(s):

Kaoru Inokuchi

Keyword(s):

Adult Neurogenesis ◽

Neural Circuit ◽

Circuit Function

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Neural architecture: from cells to circuits

Journal of Neurophysiology ◽

10.1152/jn.00044.2018 ◽

2018 ◽

Vol 120 (2) ◽

pp. 854-866 ◽

Cited By ~ 2

Author(s):

Sarah E. V. Richards ◽

Stephen D. Van Hooser

Keyword(s):

Nervous System ◽

Cerebral Cortex ◽

Structure Function ◽

Neural Circuits ◽

Neural Circuit ◽

Neuronal Morphology ◽

Neural Architecture ◽

Synaptic Interactions ◽

Branching Patterns ◽

Circuit Function

Circuit operations are determined jointly by the properties of the circuit elements and the properties of the connections among these elements. In the nervous system, neurons exhibit diverse morphologies and branching patterns, allowing rich compartmentalization within individual cells and complex synaptic interactions among groups of cells. In this review, we summarize work detailing how neuronal morphology impacts neural circuit function. In particular, we consider example neurons in the retina, cerebral cortex, and the stomatogastric ganglion of crustaceans. We also explore molecular coregulators of morphology and circuit function to begin bridging the gap between molecular and systems approaches. By identifying motifs in different systems, we move closer to understanding the structure-function relationships that are present in neural circuits.

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Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body

10.1101/2021.02.03.429444 ◽

2021 ◽

Author(s):

Nada Y. Abdelrahman ◽

Eleni Vasilaki ◽

Andrew C. Lin

Keyword(s):

Mushroom Body ◽

Neural Circuit ◽

Memory Performance ◽

Activity Levels ◽

Compensatory Mechanisms ◽

Network Parameters ◽

Kenyon Cells ◽

Average Activity ◽

Circuit Function ◽

Activity Dependent

AbstractNeural circuits use homeostatic compensation to achieve consistent behaviour despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual, and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly’s olfactory memory center. In a computational model, we show that memory performance is degraded when the mushroom body’s principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability, because the resulting inter-KC variability in average activity levels makes odor representations less separable. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model’s compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.Significance statementHow does variability between neurons affect neural circuit function? How might neurons behave similarly despite having different underlying features? We addressed these questions in neurons called Kenyon cells, which store olfactory memories in flies. Kenyon cells differ among themselves in key features that affect how active they are, and in a model of the fly’s memory circuit, adding this inter-neuronal variability made the model fly worse at learning the values of multiple odors. However, memory performance was rescued if compensation between the variable underlying features allowed Kenyon cells to be equally active on average, and we found the hypothesized compensatory variability in real Kenyon cells’ anatomy. This work reveals the existence and computational benefits of compensatory variability in neural networks.

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Predicting how and when hidden neurons skew measured synaptic interactions

10.1101/105510 ◽

2017 ◽

Cited By ~ 2

Author(s):

Braden A. W. Brinkman ◽

Fred Rieke ◽

Eric Shea-Brown ◽

Michael A. Buice

Keyword(s):

Neural Coding ◽

Neural Circuit ◽

Quantitative Relationship ◽

Network Size ◽

Effective Interactions ◽

Synaptic Interactions ◽

Neural Computations ◽

Hidden Neurons ◽

Circuit Function ◽

The Impact

AbstractA major obstacle to understanding neural coding and computation is the fact that experimental recordings typically sample only a small fraction of the neurons in a circuit. Measured neural properties are skewed by interactions between recorded neurons and the “hidden” portion of the network. To properly interpret neural data and determine how biological structure gives rise to neural circuit function, we thus need a better understanding of the relationships between measured effective neural properties and the true underlying physiological properties. Here, we focus on how the effective spatiotemporal dynamics of the synaptic interactions between neurons are reshaped by coupling to unobserved neurons. We find that the effective interactions from a pre-synaptic neuronr′to a post-synaptic neuronrcan be decomposed into a sum of the true interaction fromr′torplus corrections from every directed path fromr′torthrough unobserved neurons. Importantly, the resulting formula reveals when the hidden units have—or do not have—major effects on reshaping the interactions among observed neurons. As a particular example of interest, we derive a formula for the impact of hidden units in random networks with “strong” coupling—connection weights that scale with, whereNis the network size, precisely the scaling observed in recent experiments. With this quantitative relationship between measured and true interactions, we can study how network properties shape effective interactions, which properties are relevant for neural computations, and how to manipulate effective interactions.

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Parvalbumin interneuron dysfunction in a thalamus - prefrontal cortex circuit in Disc1 deficiency mice

10.1101/054759 ◽

2016 ◽

Author(s):

Kristen Delevich ◽

Hanna Jaaro-Peled ◽

Mario Penzo ◽

Akira Sawa ◽

Bo Li

Keyword(s):

Prefrontal Cortex ◽

Mouse Models ◽

Pyramidal Neurons ◽

Neural Circuit ◽

Gaba Release ◽

Parvalbumin Interneurons ◽

Parvalbumin Interneuron ◽

Inhibitory Circuit ◽

Circuit Function ◽

Deficient Mice

AbstractTwo of the most consistent findings across disrupted-in-schizophrenia-1 (DISC1) mouse models are impaired working memory and reduced number or function of parvalbumin interneurons within the prefrontal cortex. While these findings suggest parvalbumin interneuron dysfunction in DISC1-related pathophysiology, to date, cortical inhibitory circuit function has not been investigated in depth in DISC1 deficiency mouse models. Here we assessed the function of a feedforward circuit between the mediodorsal thalamus (MD) and the medial prefrontal cortex (mPFC) in mice harboring a deletion in one allele of the Disc1 gene. We found that the inhibitory drive onto layer 3 pyramidal neurons in the mPFC was significantly reduced in the Disc1 deficient mice. This reduced inhibition was accompanied by decreased GABA release from local parvalbumin, but not somatostatin, inhibitory interneurons, and by impaired feedforward inhibition in the MD-mPFC circuit. Our results reveal a cellular mechanism by which deficiency in DISC1 causes neural circuit dysfunction frequently implicated in psychiatric disorders.

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