Biology subjects

Sawtell, N. B.

Publications and source records attributed to Sawtell, N. B..

10 recordsLinked to original sources

bioRxiv2026

A mechanism for attenuating responses to anticipated sounds in the dorsal cochlear nucleus

The dorsal cochlear nucleus (DCN) is a cerebellum-like structure in the mammalian auditory brainstem that combines auditory nerve input with diverse auditory and non-auditory signals conveyed by granule cells. Granule cells form excitatory synapses onto inhibitory interneurons known as cartwheel cells, and in vitro studies have demonstrated an anti-Hebbian form of plasticity at these synapses. However, the function of cartwheel cells and their plastic granule cell input has remained unknown. Using in vivo electrophysiological recordings, optogenetics, and computational modeling, we provide evidence that intrinsic electrophysiological properties of cartwheel cells invert the expected effects of anti-Hebbian plasticity, generating a positive feedback loop that enhances cartwheel cell inhibition of DCN output neurons at the onset of anticipated sounds. This combined cellular and synaptic mechanism may implement a novel form of predictive processing that is robust to variability in the timing of anticipated sensory input.

neuroscience↗

bioRxiv2025

Connectome analysis of a cerebellum-like circuit for sensory prediction

Stable and accurate perception involves comparing incoming sensory input with internally- generated predictions 1-3. A mechanistic understanding of this process has been elusive due to the size and complexity of the relevant brain regions in mammals. Here we leverage connectomics to comprehensively map the cell types and synaptic connections underlying a well-characterized and ecologically relevant form of predictive sensory processing in the cerebellum-like electrosensory lobe (ELL) of weakly electric fish 4,5. Connectome analysis reveals highly-structured feedforward and recurrent synaptic connectivity mediating the cancellation of predictable electrosensory input. A computational model constrained by prior electrophysiological recordings shows how this connectivity supports the formation of predictions at multiple sites within the network and how the ELL solves a continual learning problem by maintaining fast and accurate predictions despite noise and changes in environmental context. Overall, these findings provide a blueprint for using connectomics to elucidate learning in vertebrate nervous systems.

neuroscience↗

bioRxiv2025

Direct cerebellar control over motor production in a species with extreme cerebellar enlargement

The cerebellum is thought to fine-tune movement without being required for its production. However, this textbook view derives mainly from studies of mammalian species with highly developed cerebral cortices. Here we examined cerebellar function in the elephant-nose fish, a member of a family of African weakly electric fish (Mormyridae) in which the cerebellum is massively enlarged. The elephant-nose fish is named for a flexible facial appendage that is used to probe surfaces and extract prey from substrate. Results from microstimulation, electrophysiological recordings, and lesions support a direct role for the C1 region of the mormyrid cerebellum in controlling movement of this appendage. These findings suggest that the cerebellum is capable of performing functions typically ascribed to the cerebral cortex, emphasizing the importance of evolutionary history on the functional specialization of brain regions.

neuroscience↗

bioRxiv2024

An end-to-end model of active electrosensation

1Weakly electric fish localize and identify objects by sensing distortions in a self-generated electric field. Fish can determine the resistance and capacitance of an object, for example, even though the field distortions being sensed are small and highly-dependent on object distance and size. Here we construct a model of the responses of the fishs electroreceptors on the basis of experimental data, and we develop a model of the electric fields generated by the fish and the distortions due to objects of different resistances and capacitances. This provides us with an accurate and efficient method for generating large artificial data sets simulating fish interacting with a wide variety of objects. Using these sets, we train an artificial neural network (ANN), representing brain areas downstream of electroreceptors, to extract the 3D location, size, and electrical properties of objects. The model performs best if the ANN operates in two stages: first estimating object distance and size and then using this information to extract electrical properties. This suggests a specific form of modularity in the electrosensory system that can be tested experimentally and highlights the potential of end-to-end modeling for studies of sensory processing.

neuroscience↗

bioRxiv2023

Collective Sensing in Electric Fish

A number of organisms, including dolphins, bats, and electric fish, possess sophisticated active sensory systems that use self-generated signals (e.g. acoustic or electrical emissions) to probe the environment1,2. Studies of active sensing in social groups have typically focused on strategies for minimizing interference from conspecific emissions2-4. However, it is well-known from engineering that multiple spatially distributed emitters and receivers can greatly enhance environmental sensing (e.g. multistatic radar and sonar)5-8. Here we provide evidence from modeling, neural recordings, and behavioral experiments that the African weakly electric fish Gnathonemus petersii utilizes the electrical pulses of conspecifics to extend electrolocation range, discriminate objects, and increase information transmission. These results suggest a novel, collective mode of active sensing in which individual perception is enhanced by the energy emissions of nearby group members.

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bioRxiv2023

Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings

To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device samples neuronal activity at ultra-high spatial density ([~]10 times higher than previous probes) with low noise levels, while trading off recording span. NP Ultra is effectively an implantable voltage-sensing camera that captures a planar image of a neurons electrical field. We use a spike sorting algorithm optimized for these probes to demonstrate that the yield of visually-responsive neurons in recordings from mouse visual cortex improves up to [~]3-fold. We show that NP Ultra can record from small neuronal structures including axons and dendrites. Recordings across multiple brain regions and four species revealed a subset of extracellular action potentials with unexpectedly small spatial spread and axon-like features. We share a large-scale dataset of these brain-wide recordings in mice as a resource for studies of neuronal biophysics. Finally, using ground-truth identification of three major inhibitory cortical cell types, we found that these cell types were discriminable with approximately 75% success, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, detection of subcellular compartments, and cell type classification to enable more powerful dissection of neural circuit activity during behavior.

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bioRxiv2023

Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools

Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the supervised approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce "Lightning Pose," an efficient pose estimation package with three algorithmic contributions. First, in addition to training on a few labeled video frames, we use many unlabeled videos and penalize the network whenever its predictions violate motion continuity, multiple-view geometry, and posture plausibility (semi-supervised learning). Second, we introduce a network architecture that resolves occlusions by predicting pose on any given frame using surrounding unlabeled frames. Third, we refine the pose predictions post-hoc by combining ensembling and Kalman smoothing. Together, these components render pose trajectories more accurate and scientifically usable. We release a cloud application that allows users to label data, train networks, and predict new videos directly from the browser.

neuroscience↗

bioRxiv2022

A Biophysical Basis for Learning and Transmitting Sensory Predictions

Homeostatic (anti-Hebbian) forms of synaptic are effective at eliminating "prediction errors" that signal the differences between predicted and actual sensory input. However, such mechanisms appear to preclude the possibility of transmitting the resulting predictions to downstream circuits, severely limiting their utility. Using modeling and recordings from the electrosensory lobe of mormyrid fish, we reveal interactions between axonal and dendritic spikes that support both the learning and transmission of predictions. We find that sensory input modulates the rate of dendritic spikes by adjusting the amplitude of backpropagating axonal action potentials. Homeostatic plasticity counteracts these effects through changes in the underlying membrane potential, allowing the dendritic spike rate to be restored to equilibrium while simultaneously transmitting predictions through modulation of the axonal spike rate. These results reveal how two types of spikes dramatically enhance the computational power of single neurons in support of an ethologically relevant multi-layer computation.

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bioRxiv2022

An Internal Model of Sensorimotor Context in Freely Swimming Electric Fish

Nervous systems are hypothesized to learn and store internal models that predict the sensory consequences of motor actions. However, little is known about the neural mechanisms for generating accurate predictions under real-world conditions in which the sensory consequences of action depend on environmental context. Using novel methods for underwater neural recording in freely swimming electric fish, we demonstrate that complex movement-related input to the active electrosensory system is effectively cancelled, despite being highly-dependent on the nearby environment. Computational modeling and closed-loop electrophysiological experiments indicate that the cerebellum-like circuitry of the electrosensory lobe generates context-specific predictions of self-generated input by combining motor signals with electrosensory feedback. These results provide mechanistic insight into sophisticated internal models supporting natural behavior in freely moving animals. One-Sentence SummaryUnderwater recordings in electric fish reveal neural mechanisms for predicting the sensory consequences of behavior under natural conditions in freely moving animals.

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bioRxiv2021

Neural Readout of a Latency Code in the Active Electrosensory System

The latency of spikes relative to a stimulus conveys sensory information across modalities. However, in most cases it remains unclear whether and how such latency codes are utilized by postsynaptic neurons. In the active electrosensory system of mormyrid fish, a latency code for stimulus amplitude in electroreceptor afferent nerve fibers (EAs) is hypothesized to be read out by a central reference provided by motor corollary discharge (CD). Here we demonstrate that CD enhances sensory responses in postsynaptic granular cells of the electrosensory lobe, but is not required for reading out EA input. Instead, diverse latency and spike count tuning across the EA population gives rise to graded information about stimulus amplitude that can be read out by standard integration of converging excitatory synaptic inputs. Inhibitory control over the temporal window of integration renders two granular cell subclasses differentially sensitive to information derived from relative spike latency versus spike count.

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