Theoretical models can explain how network structure shapes neural computation, but they typically assume idealized connectivity that is inconsistent with the heterogeneous wiring of biological circuits. We address this issue in the Drosophila head-direction system, a recurrent network with ring-attractor dynamics that enable angular velocity integration. The network’s symmetric wiring motifs are reminiscent of classical models, but with additional heterogeneity that should, in principle, destabilize attractor dynamics. Inspired by novel architectures discovered through machine-learning-based optimization, we develop an algorithm that transforms attractor models with symmetric connectivity into functionally equivalent models with heterogeneous connectivity. By replacing each unit with multiple clones that preserve its output, the algorithm embeds hidden symmetries in heterogeneous connectivity, maintaining ring-attractor dynamics and accurate integration. Analysis of multiple fly connectomes provides evidence for duplicated units whose connectivity reflects hidden symmetries, consistent with our theory. Our framework helps reconcile idealized models of neural computation with heterogeneous biological circuits.

Dopaminergic neurons with distinct projection patterns and physiological properties compose memory subsystems in a brain. However, it is poorly understood whether or how they interact during complex learning. Here, we identify a feedforward circuit formed between dopamine subsystems and show that it is essential for second-order conditioning, an ethologically important form of higher-order associative learning. The Drosophila mushroom body comprises a series of dopaminergic compartments, each of which exhibits distinct memory dynamics. We find that a slow and stable memory compartment can serve as an effective “teacher” by instructing other faster and transient memory compartments via a single key interneuron, which we identify by connectome analysis and neurotransmitter prediction. This excitatory interneuron acquires enhanced response to reward-predicting odor after first-order conditioning and, upon activation, evokes dopamine release in the “student” compartments. These hierarchical connections between dopamine subsystems explain distinct properties of first- and second-order memory long known by behavioral psychologists.

The anterodorsal projection neuron lineage of Drosophila melanogaster produces 40 neuronal types in a stereotypic order. Here we take advantage of this complete lineage sequence to examine the role of known temporal fating factors, including Chinmo and the Hb/Kr/Pdm/Cas transcriptional cascade, within this diverse central brain lineage. Kr mutation affects the temporal fate of the neuroblast (NB) itself, causing a single fate to be skipped, whereas Chinmo null only elicits fate transformation of NB progeny without altering cell counts. Notably, Chinmo operates in two separate windows to prevent fate transformation (into the subsequent Chinmo-indenpendent fate) within each window. By contrast, Hb/Pdm/Cas play no detectable role, indicating that Kr either acts outside of the cascade identified in the ventral nerve cord or that redundancy exists at the level of fating factors. Therefore, hierarchical fating mechanisms operate within the lineage to generate neuronal diversity in an unprecedented fashion.

Understanding how neural signals control muscle activity during behavior is a key challenge in motor neuroscience. To this end, recent advances in intramuscular multielectrode arrays have enabled high-quality multichannel recordings of many motor unit action potentials (MUAPs) in freely moving subjects. However, identifying individual MUAP events within multichannel recordings is a significant challenge for existing spike sorting methods, which are typically optimized for identifying action potentials from neurons in the brain. To overcome this challenge, we developed the Enhanced Motor Unit sorter (EMUsort), an extension of Kilosort4 (KS4) that achieves high-performance MUAP spike sorting. We applied EMUsort to high-resolution intramuscular recordings from rat forelimb during locomotion and monkey forelimb during a reaching task. EMUsort improves upon prior methods by addressing key challenges encountered with MUAP datasets, including: 1) long time delays across electrodes due to propagation along muscle fibers, 2) more complex waveform shapes compared to neuronal action potentials, and 3) a high degree of MUAP overlap due to cumulative motor unit recruitment. We compared EMUsort to existing spike sorting methods quantitatively using simulated datasets that closely emulated the rat and monkey datasets we recorded. EMUsort provided median error rate reductions of 67.5% and 49.9% during periods of high motor unit activation for the rat and monkey datasets, respectively. In sum, EMUsort provides a substantial improvement to MUAP spike sorter accuracy, especially during regions of high MUAP overlap, in an easy-to-use software package.

Understanding how neural signals control muscle activity during behavior is a key challenge in motor neuroscience. To this end, recent advances in intramuscular multielectrode arrays have enabled high-quality multichannel recordings of many motor unit action potentials (MUAPs) in freely moving subjects. However, identifying individual MUAP events within multichannel recordings is a significant challenge for existing spike sorting methods, which are typically optimized for identifying action potentials from neurons in the brain. To overcome this challenge, we developed the Enhanced Motor Unit sorter (EMUsort), an extension of Kilosort4 (KS4) that achieves high-performance MUAP spike sorting. We applied EMUsort to high-resolution intramuscular recordings from rat forelimb during locomotion and monkey forelimb during a reaching task. EMUsort improves upon prior methods by addressing key challenges encountered with MUAP datasets, including: 1) long time delays across electrodes due to propagation along muscle fibers, 2) more complex waveform shapes compared to neuronal action potentials, and 3) a high degree of MUAP overlap due to cumulative motor unit recruitment. We compared EMUsort to existing spike sorting methods quantitatively using simulated datasets that closely emulated the rat and monkey datasets we recorded. EMUsort provided median error rate reductions of 67.5% and 49.9% during periods of high motor unit activation for the rat and monkey datasets, respectively. In sum, EMUsort provides a substantial improvement to MUAP spike sorter accuracy, especially during regions of high MUAP overlap, in an easy-to-use software package.

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of sensory stimuli. We recorded simultaneously from ∼20,000 neurons in mouse visual cortex and found that the neural population had discrimination thresholds of 0.3° in an orientation decoding task. These thresholds are ∼100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of trial-by-trial perceptual learning tasks. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

The challenge of recovering the topology of massive neuronal circuits can potentially be met by high throughput Electron Microscopy (EM) imagery. Segmenting a 3-dimensional stack of EM images into the individual neurons is difficult, due to the low depth-resolution in existing high-throughput EM technology, such as serial section Transmission EM (ssTEM). In this paper we propose methods for detecting the high resolution locations of membranes from low depth-resolution images. We approach this problem using both a method that learns a discriminative, over-complete dictionary and a kernel SVM. We test this approach on tomographic sections produced in simulations from high resolution Focused Ion Beam (FIB) images and on low depth-resolution images acquired with ssTEM and evaluate our results by comparing it to manual labeling of this data.

We present a compressed domain scheme that is able to recognize and localize actions at high speeds. The recognition problem is posed as performing an action video query on a test video sequence. Our method is based on computing motion similarity using compressed domain features which can be extracted with low complexity. We introduce a novel motion correlation measure that takes into account differences in motion directions and magnitudes. Our method is appearance invariant, requires no prior segmentation, alignment or stabilization, and is able to localize actions in both space and time. We evaluated our method on a benchmark action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our proposed method achieved a classification accuracy of 90%, comparing favorably with existing methods in action classification accuracy, and is able to localize a template video of 80 x 64 pixels with 23 frames in a test video of 368 x 184 pixels with 835 frames in just 11 seconds, easily outperforming other methods in localization speed. We also perform a systematic investigation of the effects of various encoding options on our proposed approach. In particular, we present results on the compression-classification trade-off, which would provide valuable insight into jointly designing a system that performs video encoding at the camera front-end and action classification at the processing backend.

Random scattering and aberrations severely limit the imaging depth in optical microscopy. We introduce a rapid, parallel wavefront compensation technique that efficiently compensates even highly complex phase distortions. Using coherence gated backscattered light as a feedback signal, we focus light deep inside highly scattering brain tissue. We demonstrate that the same wavefront optimization technique can also be used to compensate spectral phase distortions in ultrashort laser pulses using nonlinear iterative feedback. We can restore transform limited pulse durations at any selected target location and compensate for dispersion that has occurred in the optical train and within the sample.

We describe a procedure for designing proteins with backbones produced by varying the parameters in the Crick coiled coil-generating equations. Combinatorial design calculations identify low-energy sequences for alternative helix supercoil arrangements, and the helices in the lowest-energy arrangements are connected by loop building. We design an antiparallel monomeric untwisted three-helix bundle with 80-residue helices, an antiparallel monomeric right-handed four-helix bundle, and a pentameric parallel left-handed five-helix bundle. The designed proteins are extremely stable (extrapolated ΔGfold > 60 kilocalories per mole), and their crystal structures are close to those of the design models with nearly identical core packing between the helices. The approach enables the custom design of hyperstable proteins with fine-tuned geometries for a wide range of applications.