A behavioral polymorphism caused by a single gene inside a supergene, bioRxiv, 2020-01-15
AbstractBehavioral evolution relies on genetic changes, yet few social behaviors can be traced to specific genetic sequences in vertebrates. Here, we show experimental evidence that differentiation of a single gene has contributed to divergent behavioral phenotypes in the white-throated sparrow, a common North American songbird. In this species, one of two alleles of ESR1, encoding estrogen receptor α (ERα), has been captured inside a differentiating supergene that segregates with an aggressive phenotype, such that ESR1 expression predicts aggression. Here, we show that the aggressive phenotype associated with the supergene is prevented by ESR1 knockdown in a single brain region. Next, we show that in a free-living population, aggression is predicted by allelic imbalance favoring the supergene allele. Cis-regulatory variation between the two alleles affects transcription factor binding sites, DNA methylation, and rates of transcription. This work provides a rare illustration of how genotypic divergence has led to behavioral phenotypic divergence in a vertebrate.
biorxiv neuroscience 0-100-users 2020Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy, bioRxiv, 2020-01-12
SUMMARYMany animals use coordinated limb movements to interact with and navigate through the environment. To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to map synaptic connectivity within a neuronal network that controls limb movements. We present a synapse-resolution EM dataset containing the ventral nerve cord (VNC) of an adult female Drosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we reconstructed 507 motor neurons, including all those that control the legs and wings. We show that a specific class of leg sensory neurons directly synapse onto the largest-caliber motor neuron axons on both sides of the body, representing a unique feedback pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM data acquisition more accessible and affordable to the scientific community.
biorxiv neuroscience 100-200-users 2020Individual differences among deep neural network models, bioRxiv, 2020-01-09
AbstractDeep neural networks (DNNs) excel at visual recognition tasks and are increasingly used as a modelling framework for neural computations in the primate brain. However, each DNN instance, just like each individual brain, has a unique connectivity and representational profile. Here, we investigate individual differences among DNN instances that arise from varying only the random initialization of the network weights. Using representational similarity analysis, we demonstrate that this minimal change in initial conditions prior to training leads to substantial differences in intermediate and higher-level network representations, despite achieving indistinguishable network-level classification performance. We locate the origins of the effects in an under-constrained alignment of category exemplars, rather than a misalignment of category centroids. Furthermore, while network regularization can increase the consistency of learned representations, considerable differences remain. These results suggest that computational neuroscientists working with DNNs should base their inferences on multiple networks instances instead of single off-the-shelf networks.
biorxiv neuroscience 100-200-users 2020Binary and analog variation of synapses between cortical pyramidal neurons, bioRxiv, 2020-01-01
AbstractLearning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (L23 pyramidal cells), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects. We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes (Arellano et al., 2007) by a log-normal distribution (Loewenstein, Kuras and Rumpel, 2011; de Vivo et al., 2017; Santuy et al., 2018). A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here we show that synapse size, when restricted to synapses between L23 pyramidal cells, is well-modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size (Sorra and Harris, 1993; Koester and Johnston, 2005; Bartol et al., 2015; Kasthuri et al., 2015; Dvorkin and Ziv, 2016; Bloss et al., 2018; Motta et al., 2019). We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences. We discuss the implications for the stability-plasticity dilemma.
biorxiv neuroscience 100-200-users 2020Dopamine modulates the size of striatal projection neuron ensembles, bioRxiv, 2019-12-06
SUMMARYDopamine (DA) is a critical modulator of brain circuits that control voluntary movements, but our understanding of its influence on the activity of target neurons in vivo remains limited. Here, we use two-photon Ca2+ imaging to simultaneously monitor the activity of direct and indirect-pathway spiny projection neurons (SPNs) in the striatum of behaving mice during acute and prolonged manipulations of DA signaling. We find that, contrary to prevailing models, DA does not modulate activity rates in either pathway strongly or differentially. Instead, DA exerts a prominent influence on the overall number of direct and indirect pathway SPNs recruited during behavior. Chronic loss of midbrain DA neurons in a model of Parkinson’s disease selectively impacts direct pathway ensembles and profoundly alters how they respond to DA elevation. Our results indicate that DA regulates striatal output by dynamically reconfiguring its sparse ensemble code and provide novel insights into the pathophysiology of Parkinson’s disease.
biorxiv neuroscience 100-200-users 2019Experience dependent contextual codes in the hippocampus, bioRxiv, 2019-12-04
AbstractThe hippocampus is a medial temporal lobe brain structure that contains circuitry and neural representations capable of supporting declarative memory. Hippocampal place cells fire in one or few restricted spatial locations in a given environment. Between environmental contexts, place cell firing fields remap (turning onoff or moving to a new spatial location), providing a unique population-wide neural code for context specificity. However, the manner by which features associated with a given context combine to drive place cell remapping remains a matter of debate. Here we show that remapping of neural representations in region CA1 of the hippocampus is strongly driven by prior beliefs about the frequency of certain contexts, and that remapping is equivalent to an optimal estimate of the identity of the current context under that prior. This prior-driven remapping is learned early in training and remains robust to changes in behavioral task-demands. Furthermore, a simple associative learning mechanism is sufficient to reproduce these results. Our findings demonstrate that place cell remapping is a generalization of representing an animal’s location. Rather than simply representing location in physical space, the hippocampus represents an optimal estimate of location in a multi-dimensional stimulus space.
biorxiv neuroscience 100-200-users 2019