Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In , recent synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest an increasingly intricate motion model compared with the ubiquitous Hassenstein-Reichardt model, while our knowledge of OFF-pathway (T5) has been incomplete. Here we present a conclusive and comprehensive connectome that for the first time integrates detailed connectivity information for inputs to both T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. While the two pathways are likely evolutionarily linked and indeed exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

Since their inception, computational models have become increasingly complex and useful counterparts to laboratory experiments within the field of neuroscience. Today several software programs exist to solve the underlying mathematical system of equations, but such programs typically solve these equations in all parts of a cell (or network of cells) simultaneously, regardless of whether or not all of the cell is active. This approach can be inefficient if only part of the cell is active and many simulations must be performed. We have previously developed a numerical method that provides a framework for spatial adaptivity by making the computations local to individual branches rather than entire cells (Rempe and Chopp, SIAM Journal on Scientific Computing, 28: 2139-2161, 2006). Once the computation is reduced to the level of branches instead of cells, spatial adaptivity is straightforward: the active regions of the cell are detected and computational effort is focused there, while saving computations in other regions of the cell that are at or near rest. Here we apply the adaptive method to four realistic neuronal simulation scenarios and demonstrate its improved efficiency over non-adaptive methods. We find that the computational cost of the method scales with the amount of activity present in the simulation, rather than the physical size of the system being simulated. For certain problems spatial adaptivity reduces the computation time by up to 80%.

The renal proximal tubule plays a critical role in water and solute reabsorption. Recently we generated a high resolution 3D, quantifiable volume microscopic identification of the ultrastructure of kidney Proximal Tubule (PT) cells using enhanced Focused Ion Beam Scanning Electron Microscopy (eFIB-SEM) and machine learning-based segmentation approaches. This analysis revealed that, in a volume of 70x60x177 µm3 of mouse kidney tissue, the mean volume of PT cells is 1980.25 µm3 ± 491.28 μm3. In an analysis of 25 PT cells, mitochondria (MITO) and endoplasmic reticulum (ER) accounted for an average of 26.4% and 6.3% of cell volume, respectively. Importantly, 91% of the total ER volume appeared to be comprised of a single contiguous ER structure as determined by tracing the ER surface. Using semi-thin sections (0.5 µm) of mouse kidney and antibodies directed against ER proteins we assessed the functional compartmentalization of the ER in PT cells by immunofluorescence microscopy. We find that ER proteins that participate in maintaining ER structure and lipid exchange, such as CLIMP-63 and VAP-A, localize to regions of the ER that are in close apposition to the basolateral plasma membrane (BL PM) of the PT cell. This distribution is confirmed by co-staining with an antibody directed against the Na, K-ATPase, a marker of the BL PM. In contrast, regions of the ER that are involved in calcium ion storage, as detected by staining for the SERCA calcium ATPase, are distributed broadly through the cytoplasm in the area of the cell that is rich in MITO. Staining for mitofilin, a MITO outer membrane protein, confirmed the abundance and distribution of the MITO in all of the PT cells. PDI, a protein that regulates proper folding and maturation of newly synthesized proteins in the lumen of the ER, resides primarily in portions of the ER that surround the nucleus and extend into the apical regions of the cell. PDI is mostly absent from the BL portions of the PT cells. Interestingly, calreticulin, which participates both in ER calcium storage and newly synthesized protein folding and quality control processes, is heavily concentrated in the subapical region of the cell. Using the machine learning algorithm to segment the lumen of the seemingly continuous ER structure demonstrates that, within the limit of resolution of this technique, continuity of ER lumens is limited to discrete patches. The defined distributions of these ER markers demonstrates that the extensive ER network in proximal tubule cells is divided into subdomains with distinct functional capacities and properties. NIH-RC2 DK120534. RDP and EML conributed equally. OAW and MJC contributed equally. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Inside the cell, proteins essential for signaling, morphogenesis, and migration navigate complex pathways, typically via vesicular trafficking or microtubule-driven mechanisms 1-3. However, the process by which soluble cytoskeletal monomers maneuver through the cytoplasm’s ever-changing environment to reach their destinations without using these pathways remains unknown. 4-6 Here, we show that actin cytoskeletal treadmilling leads to the formation of a semi-permeable actin-myosin barrier, creating a specialized compartment separated from the rest of the cell body that directs proteins toward the cell edge by advection, diffusion facilitated by fluid flow. Contraction at this barrier generates a molecularly non-specific fluid flow that transports actin, actin-binding proteins, adhesion proteins, and even inert proteins forward. The local curvature of the barrier specifically targets these proteins toward protruding edges of the leading edge, sites of new filament growth, effectively coordinating protein distribution with cellular dynamics. Outside this compartment, diffusion remains the primary mode of protein transport, contrasting sharply with the directed advection within. This discovery reveals a novel protein transport mechanism that redefines the front of the cell as a pseudo-organelle, actively orchestrating protein mobilization for cellular front activities such as protrusion and adhesion. By elucidating a new model of protein dynamics at the cellular front, this work contributes a critical piece to the puzzle of how cells adapt their internal structures for targeted and rapid response to extracellular cues. The findings challenge the current understanding of intracellular transport, suggesting that cells possess highly specialized and previously unrecognized organizational strategies for managing protein distribution efficiently, providing a new framework for understanding the cellular architecture’s role in rapid response and adaptation to environmental changes.

Although information storage in the central nervous system is thought to be primarily mediated by various forms of synaptic plasticity, other mechanisms, such as modifications in membrane excitability, are available. Local dendritic spikes are nonlinear voltage events that are initiated within dendritic branches by spatially clustered and temporally synchronous synaptic input. That local spikes selectively respond only to appropriately correlated input allows them to function as input feature detectors and potentially as powerful information storage mechanisms. However, it is currently unknown whether any effective form of local dendritic spike plasticity exists. Here we show that the coupling between local dendritic spikes and the soma of rat hippocampal CA1 pyramidal neurons can be modified in a branch-specific manner through an N-methyl-d-aspartate receptor (NMDAR)-dependent regulation of dendritic Kv4.2 potassium channels. These data suggest that compartmentalized changes in branch excitability could store multiple complex features of synaptic input, such as their spatio-temporal correlation. We propose that this ’branch strength potentiation’ represents a previously unknown form of information storage that is distinct from that produced by changes in synaptic efficacy both at the mechanistic level and in the type of information stored.

Even a simple sensory stimulus can elicit distinct innate behaviors and sequences. During sensorimotor decisions, competitive interactions among neurons that promote distinct behaviors must ensure the selection and maintenance of one behavior, while suppressing others. The circuit implementation of these competitive interactions is still an open question. By combining comprehensive electron microscopy reconstruction of inhibitory interneuron networks, modeling, electrophysiology, and behavioral studies, we determined the circuit mechanisms that contribute to the Drosophila larval sensorimotor decision to startle, explore, or perform a sequence of the two in response to a mechanosensory stimulus. Together, these studies reveal that, early in sensory processing, (1) reciprocally connected feedforward inhibitory interneurons implement behavioral choice, (2) local feedback disinhibition provides positive feedback that consolidates and maintains the chosen behavior, and (3) lateral disinhibition promotes sequence transitions. The combination of these interconnected circuit motifs can implement both behavior selection and the serial organization of behaviors into a sequence.

Nervous systems contain sensory neurons, local neurons, projection neurons, and motor neurons. To understand how these building blocks form whole circuits, we must distil these broad classes into neuronal cell types and describe their network connectivity. Using an electron micrograph dataset for an entire Drosophila melanogaster brain, we reconstruct the first complete inventory of olfactory projections connecting the antennal lobe, the insect analog of the mammalian olfactory bulb, to higher-order brain regions in an adult animal brain. We then connect this inventory to extant data in the literature, providing synaptic-resolution "holotypes" both for heavily investigated and previously unknown cell types. Projection neurons are approximately twice as numerous as reported by light level studies; cell types are stereotyped, but not identical, in cell and synapse numbers between brain hemispheres. The lateral horn, the insect analog of the mammalian cortical amygdala, is the main target for this olfactory information and has been shown to guide innate behavior. Here, we find new connectivity motifs, including axo-axonic connectivity between projection neurons, feedback, and lateral inhibition of these axons by a large population of neurons, and the convergence of different inputs, including non-olfactory inputs and memory-related feedback onto third-order olfactory neurons. These features are less prominent in the mushroom body calyx, the insect analog of the mammalian piriform cortex and a center for associative memory. Our work provides a complete neuroanatomical platform for future studies of the adult Drosophila olfactory system.

Aberrations and random scattering severely limit optical imaging in deep tissue. Adaptive optics can in principle drastically extend the penetration depth and improve the image quality. However, for random scattering media a large number of spatial modes need to be measured and controlled to restore a diffraction limited focus. Here, we present a parallel wavefront optimization method using backscattered light as a feedback. Spatial confinement of the feedback signal is realized with a confocal pinhole and coherence gating. We show in simulations and experiments that this approach enables focusing deep into tissue over up to six mean scattering path lengths. Experimentally the technique was tested on tissue phantoms and fixed brain slices.

Electrical coupling in circuits can produce non-intuitive circuit dynamics, as seen in both experimental work from the crustacean stomatogastric ganglion and in computational models inspired by the connectivity in this preparation. Ambiguities in interpreting the results of electrophysiological recordings can arise if sets of pre- or postsynaptic neurons are electrically coupled, or if the electrical coupling exhibits some specificity (e.g. rectifying, or voltage-dependent). Even in small circuits, electrical coupling can produce parallel pathways that can allow information to travel by monosynaptic and/or polysynaptic pathways. Consequently, similar changes in circuit dynamics can arise from entirely different underlying mechanisms. When neurons are coupled both chemically and electrically, modifying the relative strengths of the two interactions provides a mechanism for flexibility in circuit outputs. This, together with neuromodulation of gap junctions and coupled neurons is important both in developing and adult circuits. This article is protected by copyright. All rights reserved.

Animals efficiently learn to navigate their environment. In the laboratory, naive mice explore their environment via highly structured trajectories and can learn to localize new spatial targets in as few as a handful of trials. It is unclear how such efficient learning is possible, since existing computational models of spatial navigation require far more experience to achieve comparable performance and do not attempt to explain the evolving structure of animal behavior during learning. To inform a new algorithm for rapid learning of navigational goals, we took inspiration from the reliable structure of behavior as mice learned to intercept hidden spatial targets. We designed agents that generate behavioral trajectories by controlling the speed and angular velocity of smooth path segments between anchor points. To rapidly learn good anchors, we use Bayesian inference on the history of rewarded and unrewarded trajectories to infer the probability that an anchor will be successful, and active sampling to trim hypothesized anchors. Agents learn within tens of trials to generate compact trajectories that intercept a target, capturing the evolution of behavioral structure and matching the upper limits of learning efficiency observed in mice. We further show that this algorithm can explain how mice avoid obstacles and rapidly adapt to target switches. Finally, we show that this framework naturally encompasses both egocentric and allocentric strategies for navigation.