Animal brains are complex organs composed of thousands of interconnected neurons. Characterizing the network properties of these brains is a requisite step towards understanding mechanisms of computation and information flow. With the completion of the Flywire project, we now have access to the connectome of a complete adult Drosophila brain, containing 130,000 neurons and millions of connections. Here, we present a statistical summary and data products of the Flywire connectome, delving into its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We uncovered a population of highly connected neurons known as the “rich club” and identified subsets of neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions. The freely available data and neuron populations presented here will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Visual, auditory, somatosensory, and olfactory stimuli generate temporally precise patterns of action potentials (spikes). It is unclear, however, how the precision of spike generation relates to the pattern and variability of synaptic input elicited by physiological stimuli. We determined how synaptic conductances evoked by light stimuli that activate the rod bipolar pathway control spike generation in three identified types of mouse retinal ganglion cells (RGCs). The relative amplitude, timing, and impact of excitatory and inhibitory input differed dramatically between On and Off RGCs. Spikes evoked by repeated somatic injection of identical light-evoked synaptic conductances were more temporally precise than those evoked by light. However, the precision of spikes evoked by conductances that varied from trial to trial was similar to that of light-evoked spikes. Thus, the rod bipolar pathway modulates different RGCs via unique combinations of synaptic input, and RGC temporal variability reflects variability in the input this circuit provides.

We give a covering number bound for deep learning networks that is independent of the size of the network. The key for the simple analysis is that for linear classifiers, rotating the data doesn't affect the covering number. Thus, we can ignore the rotation part of each layer's linear transformation, and get the covering number bound by concentrating on the scaling part.

Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication-it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components-a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research.

Genetically encoded calcium indicators (GECIs), together with modern microscopy, allow repeated activity measurement, in real time and with cellular resolution, of defined cellular populations. Recent efforts in protein engineering have yielded several high-quality GECIs that facilitate new applications in neuroscience. Here, we summarize recent progress in GECI design, optimization, and characterization, and provide guidelines for selecting the appropriate GECI for a given biological application. We focus on the unique challenges associated with imaging in behaving animals.

Classical studies have related the spiking of selected neocortical neurons to behavior, but little is known about activity sampled from the entire neural population. We recorded from neurons selected independent of spiking, using cell-attached recordings and two-photon calcium imaging, in the barrel cortex of mice performing an object localization task. Spike rates varied across neurons, from silence to >60 Hz. Responses were diverse, with some neurons showing large increases in spike rate when whiskers contacted the object. Nearly half the neurons discriminated object location; a small fraction of neurons discriminated perfectly. More active neurons were more discriminative. Layer (L) 4 and L5 contained the highest fractions of discriminating neurons (\~{}63% and 79%, respectively), but a few L2/3 neurons were also highly discriminating. Approximately 13,000 spikes per activated barrel column were available to mice for decision making. Coding of object location in the barrel cortex is therefore highly redundant.

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits. Expected final online publication date for the , Volume 45 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The brain plans and executes volitional movements. The underlying patterns of neural population activity have been explored in the context of movements of the eyes, limbs, tongue, and head in nonhuman primates and rodents. How do networks of neurons produce the slow neural dynamics that prepare specific movements and the fast dynamics that ultimately initiate these movements? Recent work exploits rapid and calibrated perturbations of neural activity to test specific dynamical systems models that are capable of producing the observed neural activity. These joint experimental and computational studies show that cortical dynamics during motor planning reflect fixed points of neural activity (attractors). Subcortical control signals reshape and move attractors over multiple timescales, causing commitment to specific actions and rapid transitions to movement execution. Experiments in rodents are beginning to reveal how these algorithms are implemented at the level of brain-wide neural circuits.

Identified neuron classes in vertebrate cortical [1-4] and subcortical [5-8] areas and invertebrate peripheral [9-11] and central [12-14] brain neuropils encode specific visual features of a panorama. How downstream neurons integrate these features to control vital behaviors, like escape, is unclear [15]. In Drosophila, the timing of a single spike in the giant fiber (GF) descending neuron [16-18] determines whether a fly uses a short or long takeoff when escaping a looming predator [13]. We previously proposed that GF spike timing results from summation of two visual features whose detection is highly conserved across animals [19]: an object's subtended angular size and its angular velocity [5-8, 11, 20, 21]. We attributed velocity encoding to input from lobula columnar type 4 (LC4) visual projection neurons, but the size-encoding source remained unknown. Here, we show that lobula plate/lobula columnar, type 2 (LPLC2) visual projection neurons anatomically specialized to detect looming [22] provide the entire GF size component. We find LPLC2 neurons to be necessary for GF-mediated escape and show that LPLC2 and LC4 synapse directly onto the GF via reconstruction in a fly brain electron microscopy (EM) volume [23]. LPLC2 silencing eliminates the size component of the GF looming response in patch-clamp recordings, leaving only the velocity component. A model summing a linear function of angular velocity (provided by LC4) and a Gaussian function of angular size (provided by LPLC2) replicates GF looming response dynamics and predicts the peak response time. We thus present an identified circuit in which information from looming feature-detecting neurons is combined by a common post-synaptic target to determine behavioral output.

Evolution has tuned the nervous system of most animals to produce stereotyped behavioural responses to ethologically relevant stimuli. For example, female Drosophila avoid laying eggs in the presence of geosmin, an odorant produced by toxic moulds. Using this system, we now identify third order olfactory neurons that are essential for an innate aversive behaviour. Connectomics data place these neurons in the context of a complete synaptic circuit from sensory input to descending output. We find multiple levels of valence-specific convergence, including a novel form of axo-axonic input onto second order neurons conveying another danger signal, the pheromone of parasitoid wasps. However we also observe a massive divergence as geosmin-responsive second order olfactory neurons connect with a diverse array of ∼75 cell types. Our data suggest a transition from a labelled line organisation in the periphery to one in which olfactory information is mapped onto many different higher order populations with distinct behavioural significance.