Biology subjects

Wilson, R. I.

Publications and source records attributed to Wilson, R. I..

9 recordsLinked to original sources

bioRxiv2026

A network model of path control

We present a network model that enables a navigating agent to shape the spatial and temporal structure of its paths through its environment. The model is inspired by the insect fan-shaped body. In it, a recurrent neural network receives an input that encodes an agent's velocity vector. Meanwhile, network activity determines the setpoint of a control loop that influences this velocity vector through motor outputs. The network thus guides future movement using memory of past movement. We illustrate how the network's dynamics determine the trajectory features it extracts to produce the setpoint. We then relate the joint dynamics of network activity and the agent's velocity vector to the structure of the resulting paths. This network can produce paths suitable for different forms of exploration. It can also stabilize movements initiated by lower-level systems. Our analyses illustrate how recurrent neural dynamics and memory can shape behavior when embedded in closed loops.

neuroscience↗

bioRxiv2025

Specialized parallel pathways for adaptive control of visual object pursuit

To pursue a moving visual object, the brain must generate motor commands that continuously steer the object to the center of the visual field via feedback. The gain of this control loop is flexible, yet the biological mechanisms underlying such adaptive control are not well-understood. Here we show that adaptive control in the Drosophila pursuit system involves two parallel pathways. One detects objects in the periphery and steers them toward the center of the visual field. The other detects objects near the center of the visual field and steers them to the visual midline. This latter pathway is flexible: gain increases when the object is moving away from the midline and when the pursuer is running fast. This latter pathway is also preferentially recruited when the fly is aroused, and suppressing it decreases pursuit performance. Our findings demonstrate how adaptive control can emerge from parallel pathways with specialized properties.

neuroscience↗

bioRxiv2023

Fine-grained descending control of steering in walking Drosophila

Locomotion involves rhythmic limb movement patterns that originate in circuits outside the brain. Purposeful locomotion requires descending commands from the brain, but we do not understand how these commands are structured. Here we investigate this issue, focusing on the control of steering in walking Drosophila. First, we describe different limb "gestures" associated with different steering maneuvers. Next, we identify a set of descending neurons whose activity predicts steering. Focusing on two descending cell types downstream from distinct brain networks, we show that they evoke specific limb gestures: one lengthens strides on the outside of a turn, while the other attenuates strides on the inside of a turn. Notably, a single descending neuron can have opposite effects during different locomotor rhythm phases, and we identify networks positioned to implement this phase-specific gating. Together, our results show how purposeful locomotion emerges from brain cells that drive specific, coordinated modulations of low-level patterns.

neuroscience↗

bioRxiv2022

Transforming a head direction signal into a goal-oriented steering command

To navigate, we must continuously estimate the direction we are headed in, and we must use this information to guide our path toward our goal1. Direction estimation is accomplished by ring attractor networks in the head direction system2,3. However, we do not understand how the sense of direction is used to guide action. Drosophila connectome analyses4,5 recently revealed two cell types (PFL2 and PFL3) that connect the head direction system to the locomotor system. Here we show how both cell types combine an allocentric head direction signal with an internal goal signal to produce an egocentric motor drive. We recorded their activity as flies navigated in a virtual reality environment toward a goal stored in memory. Strikingly, PFL2 and PFL3 populations are both modulated by deviation from the goal direction, but with opposite signs. The amplitude of PFL2 activity is highest when the fly is oriented away from its goal; activating these cells destabilizes the current orientation and drives turning. By contrast, total PFL3 activity is highest around the goal; these cells generate directional turning to correct small deviations from the goal. Our data support a model where the goal is stored as a sinusoidal pattern whose phase represents direction, and whose amplitude represents salience. Variations in goal amplitude can explain transitions between goal-oriented navigation and exploration. Together, these results show how the sense of direction is used for feedback control of locomotion.

neuroscience↗

bioRxiv2021

A Bayesian perspective on the ring attractor for heading-direction tracking in the Drosophila central complex

Working memories are thought to be held in attractor networks in the brain. These attractors should keep track of the uncertainty associated with each memory, so as to weigh it properly against conflicting new evidence. However, conventional attractors do not represent uncertainty. Here we show how uncertainty could be incorporated into an attractor, specifically a ring attractor that encodes head direction. First, we introduce the first rigorous normative framework (the circular Kalman filter) for benchmarking the performance of a ring attractor under conditions of uncertainty. Next we show that the recurrent connections within a conventional ring attractor can be re-tuned to match this benchmark. This allows the amplitude of network activity to grow in response to confirmatory evidence, while shrinking in response to poor-quality or strongly conflicting evidence. This "Bayesian ring attractor" performs near-optimal angular path integration and evidence accumulation. Indeed, we show that a Bayesian ring attractor is consistently more accurate than a conventional ring attractor. Moreover, near-optimal performance can be achieved without exact tuning of the network connections. Finally, we use large-scale connectome data to show that the network can achieve near-optimal performance even after we incorporate biological constraints. Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions with direct relevance to the head direction system, as well as any neural system that tracks direction, orientation, or periodic rhythms. Significance StatementData from human subjects as well as animals shows that working memories are associated with a sense of uncertainty. Indeed, a sense of uncertainty is what allows an observer to properly weigh new evidence against their current memory. However, we do not understand how the brain tracks uncertainty. Here we describe a simple and biologically plausible network model that can track the uncertainty associated with a working memory. The representation of uncertainty in this model improves the accuracy of its working memory, as compared to conventional models, because it assigns the proper weight to new conflicting evidence. Our model provides a new interpretation for observed fluctuations in brain activity, and it makes testable new predictions.

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bioRxiv2020

Transforming representations of movement from body- to world-centric space

When an animal moves through the world, its brain receives a stream of information about the bodys translational movement. These incoming movement signals, relayed from sensory organs or as copies of motor commands, are referenced relative to the body. Ultimately, such body-centric movement signals must be transformed into world-centric coordinates for navigation1. Here we show that this computation occurs in the fan-shaped body in the Drosophila brain. We identify two cell types in the fan-shaped body, PFNd and PFNv2,3, that conjunctively encode translational velocity signals and heading signals in walking flies. Specifically, PFNd and PFNv neurons form a Cartesian representation of body-centric translational velocity - acquired from premotor brain regions4,5 - that is layered onto a world-centric heading representation inherited from upstream compass neurons6-8. Then, we demonstrate that the next network layer, comprising h{Delta}B neurons, is wired so as to transform the representation of translational velocity from body-centric to world-centric coordinates. We show that this transformation is predicted by a computational model derived directly from electron microscopy connectomic data9. The model illustrates the key role of a specific network motif, whereby the PFN neurons that synapse onto the same h{Delta}B neuron have heading-tuning differences that offset the differences in their preferred body-centric directions of movement. By integrating a world-centric representation of travel velocity over time, it should be possible for the brain to form a working memory of the path traveled through the environment10-12.

neuroscience↗

bioRxiv2020

Neural control of steering in walking Drosophila

Orienting behaviors provide a continuous stream of information about an organisms sensory experiences and plans. Thus, to study the links between sensation and action, it is useful to identify the neurons in the brain that control orienting behaviors. Here we describe descending neurons in the Drosophila brain that predict and influence orientation (heading) during walking. We show that these cells have specialized functions: whereas one cell type predicts sustained low-gain steering, the other predicts transient high-gain steering. These latter cells integrate internally-directed steering signals from the head direction system with stimulus-directed steering signals from multimodal sensory pathways. The inputs to these cells are organized to produce "see-saw" steering commands, so that increasing output from one brain hemisphere is accompanied by decreasing output from the other hemisphere. Together, our results show that internal and external drives are integrated to produce descending motor commands with different timescales, for flexible and precise control of an organisms orientation in space.

neuroscience↗

bioRxiv2019

SPARC: a method to genetically manipulate precise proportions of cells.

Many experimental approaches rely on controlling gene expression in select subsets of cells within an individual animal. However, reproducibly targeting transgene expression to specific fractions of a genetically-defined cell-type is challenging. We developed Sparse Predictive Activity through Recombinase Competition (SPARC), a generalizable toolkit that can express any effector in precise proportions of post-mitotic cells in Drosophila. Using this approach, we demonstrate targeted expression of many effectors and apply these tools to calcium imaging of individual neurons and optogenetic manipulation of sparse cell populations in vivo.

neuroscience↗

bioRxiv2017

Wiring variations that enable and constrain neural computation in a sensory microcircuit

Neural network function can be shaped by varying the strength of synaptic connections. One way to achieve this is to vary connection structure. To investigate how structural variation among synaptic connections might affect neural computation, we examined primary afferent connections in the Drosophila olfactory system. We used large-scale serial section electron microscopy to reconstruct all the olfactory receptor neuron (ORN) axons that target a left-right pair of glomeruli, as well as all the projection neurons (PNs) postsynaptic to these ORNs. We found three variations in ORN[->]PN connectivity. First, we found a systematic co-variation in synapse number and PN dendrite size, suggesting total synaptic conductance is tuned to postsynaptic excitability. Second, we discovered that PNs receive more synapses from ipsilateral than contralateral ORNs, providing a structural basis for odor lateralization behavior. Finally, we found evidence of imprecision in ORN[->]PN connections and show how this can diminish network performance.

neuroscience↗