Biology subjects

Cowan, N. J.

Publications and source records attributed to Cowan, N. J..

13 recordsLinked to original sources

bioRxiv2025

Synchronized path-integration recalibration but distinct landmark-control dynamics in head direction and CA1 place cells

Accurate spatial navigation relies on path integration, a process of tracking ones location by integrating self-motion cues. Path integration uses a gain factor relating self-motion signals to displacement on the cognitive map. This gain is plastic, recalibrating rapidly to match perceived displacements relative to external cues. To elucidate the mechanism of recalibration, we simultaneously recorded from place cells, which instantiate the cognitive map, and head direction (HD) cells, thought to orient the map. Persistent conflict between self-motion and visual feedback induced functionally identical recalibration of path-integration gain in the two neural populations during forward locomotion; however, during locomotor immobility accompanied by head-scanning, HD cells did not exhibit recalibration. Moreover, the two populations manifested differential field-shifting dynamics relative to landmarks during recalibration. These results uncover a tightly coordinated yet behavior-dependent recalibration process across the navigation circuit that achieves a robust yet flexible coupling of the internal sense of position and direction.

neuroscience↗

bioRxiv2025

Multisensory integration for active mechanosensation in Drosophila flight

To support robust behaviors in highly variable environments, animals rely on active sampling of their sensory surroundings. Here, we use tethered, flying Drosophila melanogaster and a multisensory behavioral apparatus simulating forward flight to determine how visual and mechanosensory information are integrated and control active movements of an important multimodal sensory organ, the antennae. We found that flies perform active antennal movements in response to varying airflow, and that the direction of these movements changes depending on the visual environment. Next, we found that antennal movements are amplified in the presence of visual motion, but only when the fly was flying. Through mechanical and optogenetic manipulation of mechanosensory input, we found that mechanosensory feedback is vital to antennal positioning at flight onset. Additionally, we observed unexpected changes in wingbeat frequency when the antenna was mechanically stabilized, suggesting that multiple antennal mechanosensors contribute to flight regulation. Finally, we show that integration of mechanosensory and visual cues for controlling antennal motion follows in a "winner-takes-all" paradigm dependent on the stimulus frequency, mirroring visuo-mechanosensory guided behaviors in other species. Together, these results reveal novel behavioral gating of sensory information and expand our understanding of the efferent control of active sensing.

neuroscience↗

bioRxiv2025

Feedback and feedforward control are differentially delayed in cerebellar ataxia

1Damage to the cerebellum can cause ataxia, a condition associated with impaired movement coordination. Typically, coordinated movement relies on a combination of anticipatory mechanisms (specifically, feedforward control) and corrective mechanisms (embodied by feedback control). Here, we show that in 3D reaching in VR, ataxia preserves the visuomotor feedforward and feedback control structure compared to the control group. However, the ataxia group exhibits a small increase in feedback delay (~ 20 ms) and a substantial increase in feedforward delay (~ 70 ms) together with a reduced feedback gain (~25 % lower). Our results suggest that the feedforward and feedback pathways remain largely intact in ataxia, but that time delay deficits and temoral discoordination amongst these control pathways may contribute to the disorder. We also find that providing a preview--analogous to driving on a clear night and seeing the road ahead vs. driving in the fog-- improves tracking performance in the ataxia group, although the control group was significantly better able to exploit this preview information. Overall, our results indicate that the feedforward control and preview utilization are relatively well-preserved in individuals with cerebellar ataxia, and that preview could potentially be leveraged to enhance the feedforward performance of those with ataxia.

neuroscience↗

bioRxiv2024

Illumination mediates a switch in both active sensing and control in weakly electric fish

To execute sensory-guided behavior, the nervous system must manage uncertainty within multiple streams of information. There are two highly nonlinear mechanisms for achieving this: 1) sensory reweighting, an internal neural computation which places more emphasis on sensory information that exhibits the least uncertainty, e.g. in a Bayesian framework, and 2) active sensing, an overt behavior that seeks to improve the quality of sensory information before it enters the nervous system. Here we show that animals solve both of these nonlinear problems concurrently. We studied how the weakly electric glass knifefish Eigenmannia virescens alters its movement dynamics under parametric manipulations of illumination. We hypothesized a concomitant switch in both overt active sensing and internal multisensory reweighting. To test this, we varied illumination levels from 0.1 to 210 lx as fish tracked a moving refuge. We discovered that in a neighborhood of a critical threshold (on the order of 1 to 10 lx), small increases in illumination led to dramatic changes in both active sensing and multisensory control, specifically in 1) steep reductions in fish head and tail movements and 2) decreased refuge tracking phase lag. Outside of this threshold, large changes in illumination only caused small changes in active sensing and control. A control-theoretic model that dynamically modulates the weights of vision and electrosense due to illumination changes corroborates our findings. These findings underscore the complex, multipartite, nonlinear nature of locomotor control and the remarkable ability of the nervous system to execute multiple parallel strategies for managing sensory uncertainty.

animal behavior and cognition↗

bioRxiv2024

Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration

Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)--a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variables correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bumps location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the networks representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.

neuroscience↗

bioRxiv2023

Can a Fish Learn to Ride a Bicycle? Sensorimotor Adaptation to Destabilizing Dynamics in Weakly Electric Fish

1Humans and other animals can readily learn to compensate for destabilizing dynamics, such as balancing an object or riding a bicycle. How does the nervous system learn to compensate for such destabilizing dynamics, and what are the benefits of the newly learned control policies? To investigate these questions, we examined how the weakly electric glass knifefish, Eigenmannia virescens, retunes its control system in the face of novel, destabilizing dynamics. Using a real-time feedback system, we measured swimming movements as seven individual fish tracked a moving refuge, and we fed the swimming movements back through novel dynamics to alter the refuge motion, creating an artificially destabilizing reafferent loop. We discovered that fish learned to retune their sensorimotor controllers as the artificially destabilizing feedback was gradually introduced. Furthermore, when the artificial feedback was extinguished, fish exhibited a clear aftereffect, retaining their learned sensorimotor controllers for several minutes before washing out. This retuning of the control system under destabilizing dynamics: (i) improved tracking performance compared to the predicted performance had fish not re-tuned their baseline controller, (ii) reduced sensitivity of the sensorimotor system to low-frequency disturbances, such as would arise from turbulence or motor noise, and (iii) improved phase margin, a measure of stability robustness, despite the artificial feedback driving the putative baseline control system towards instability. Our study sheds light on how the nervous system adapts to changing closed-loop dynamics, and how those changes impact performance and stability; the presence of aftereffects suggest a plasticity-based mechanism reminiscent of cerebellar learning.

animal behavior and cognition↗

bioRxiv2023

Organisms employ a mode-switching strategy for solving the explore-vs-exploit problem

The inescapable link between sensing and movement generates a conflict between producing costly movements for gathering information ("explore") versus using previously acquired information to achieve a goal ("exploit"). Determining the optimal balance between explore and exploit is a computationally intractable problem, necessitating the use of heuristics. We looked to nature to measure and model the solutions used by organisms. Here we show that the electric fish Eigenmannia virescens uses a salience-dependent mode-switching strategy to solve the explore-exploit conflict during a refuge tracking task. The fish produced distinctive non-Gaussian (i.e., non-normal) distributions of movement velocities characterized by sharp peaks for slower, task-oriented "exploit" movements and broad shoulders for faster, "explore" movements. The measures of non-normality increased in relation to increased sensory salience. Data from ten phylogenetically diverse organisms, from amoebae to humans, revealed the same distinctive distribution of movement velocities that were also modulated in relation to sensory salience. We propose a state-uncertainty based mode-switching heuristic that (1) reproduces the distinctive velocity distribution, (2) rationalizes modulation by sensory salience, and (3) outperforms the classic persistent excitation approach while using less energy. This mode-switching heuristic provides insights to purposeful exploratory behaviors in organisms as well as a framework for more efficient state estimation and control of robots.

animal behavior and cognition↗

bioRxiv2022

Closed-loop control and recalibration of place cells by optic flow

Understanding the interplay between sensory input, endogenous neural dynamics, and behavioral output is key toward understanding the principles of neural computation. Hippocampal place cells are an ideal system to investigate this closed-loop interaction, as they are influenced by both self-motion (idiothetic) signals and by external sensory landmarks as an animal navigates its environment1-9. To continuously update a position signal on an internal "cognitive map", the hippocampal system integrates self-motion signals over time10,11. In the absence of stable, external landmarks, however, these spatial correlates of neuronal activity can quickly accumulate error and cause the internal representation of position or direction to drift relative to the external environment1,5. We have previously demonstrated that, in addition to their known roles in preventing and/or correcting path-integration error, external landmarks can be used as a putative teaching signal to recalibrate the gain of the path integration system6. However, it remains unclear whether idiothetic cues, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if instead an unambiguous allocentric frame of reference, anchored by polarizing landmark information, is essential for path integration recalibration. Here, we use principles of control theory12,13 to demonstrate systematic control of place fields by pure optic flow information in freely moving animals by using a neurally closed-loop virtual reality system that adjusts optic flow speed as a function of real-time decoding of the hippocampal spatial map. Using this "cognitive clamp", we show that we can not only bring the updating of the map under control of the optic flow cues but we can also elicit recalibration of path integration. This finding demonstrates that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.

neuroscience↗

bioRxiv2022

Control becomes habitual early on when learning a novel motor skill

When people perform the same task repeatedly, their behavior becomes habitual, or inflexible to changes in the goals or structure of a task. While habits have been hypothesized to be a key aspect of motor skill acquisition, there has been little empirical work investigating the relationship between skills and habits. To better understand this relationship, we examined whether and when peoples behavior would become habitual as they learned a challenging new motor skill. After up to ten days of practice, we altered the structure of the task to assess whether participants would flexibly adjust their behavior or habitually persist in performing the task the way they originally learned. We found that participants behavior became habitual early in practice--after only two days--at which point they were still relatively unskilled. These data demonstrate that motor skills become habitual after relatively little training, but can nevertheless further improve with practice.

neuroscience↗

bioRxiv2020

De novo learning versus adaptation of continuous control in a manual tracking task

How do people learn to perform tasks that require continuous adjustments of motor output, like riding a bicycle? People rely heavily on cognitive strategies when learning discrete movement tasks, but such time-consuming strategies are infeasible in continuous control tasks that demand rapid responses to ongoing sensory feedback. To understand how people can learn to perform such tasks without the benefit of cognitive strategies, we imposed a rotation/mirror reversal of visual feedback while participants performed a continuous tracking task. We analyzed behavior using a system identification approach which revealed two qualitatively different components of learning: adaptation of a baseline controller and formation of a new, task-specific continuous controller. These components exhibited different signatures in the frequency domain and were differentially engaged under the rotation/mirror reversal. Our results demonstrate that people can rapidly build a new continuous controller de novo and can simultaneously deploy this process with adaptation of an existing controller.

neuroscience↗

bioRxiv2019

Spooky interaction at a distance in cave and surface dwelling electric fishes

Glass knifefish (Eigenmannia) are a group of weakly electric fishes found throughout the Amazon basin. We made recordings of the electric fields of two populations of freely behaving Eigenmannia in their natural habitats: a troglobitic population of blind cavefish (Eigenmannia vicentespelaea) and a nearby epigean (surface) population (Eigenmannia trilineata). These recordings were made using a grid of electrodes to determine the movements of individual fish in relation to their electrosensory behaviors. The strengths of electric discharges in cavefish were larger than in surface fish, which may be a correlate of increased reliance on electrosensory perception and larger size. Both movement and social signals were found to affect the electrosensory signaling of individual Eigenmannia. Surface fish were recorded while feeding at night and did not show evidence of territoriality. In contrast, cavefish appeared to maintain territories. Surprisingly, we routinely found both surface and cavefish with sustained differences in electric field frequencies that were below 10 Hz despite being within close proximity of less than one meter. A half century of analysis of electrosocial interactions in laboratory tanks suggest that these small differences in electric field frequencies should have triggered the jamming avoidance response. Fish also showed significant interactions between their electric field frequencies and relative movements at large distances, over 1.5 meters, and at high differences in frequencies, often greater than 50 Hz. These interactions are likely envelope responses in which fish alter their EOD frequency in relation to changes in the depth of modulation of electrosocial signals.

neuroscience↗

bioRxiv2018

Closed-Loop Control of Active Sensing Movements Regulates Sensory Slip.

Active sensing involves the production of motor signals for the purpose of acquiring sensory information [1-3]. The most common form of active sensing, found across animal taxa and behaviors, involves the generation of movements--e.g. whisking [4-6], touching [7,8], sniffing [9,10], and eye movements [11]. Active-sensing movements profoundly affect the information carried by sensory feedback pathways [12-15] and are modulated by both top-down goals (e.g. measuring weight vs. texture [1,16]) and bottom-up stimuli (e.g. lights on/off [12]) but it remains unclear if and how these movements are controlled in relation to the ongoing feedback they generate. To investigate the control of movements for active sensing, we created an experimental apparatus for freely swimming weakly electric fish, Eigenmannia virescens, that modulates the gain of reafferent feedback by adjusting the position of a refuge based on real time videographic measurements of fish position. We discovered that fish robustly regulate sensory slip via closed-loop control of active-sensing movements. Specifically, as fish performed the task of maintaining position inside the refuge [17-22], they dramatically up- or down-regulated fore-aft active-sensing movements in relation to a 4-fold change of experimentally modulated reafferent gain. These changes in swimming movements served to maintain a constant magnitude of sensory slip. The magnitude of sensory slip depended on the presence or absence of visual cues. These results indicate that fish use two controllers: one that controls the acquisition of information by regulating feedback from active sensing movements, and another that maintains position in the refuge, a control structure that may be ubiquitous in animals [23,24].

animal behavior and cognition↗

bioRxiv2018

Recalibration of path integration in hippocampal place cells

Hippocampal place cells are spatially tuned neurons that serve as elements of a \"cognitive map\" in the mammalian brain1. To detect the animals location, place cells are thought to rely upon two interacting mechanisms: sensing the animals position relative to familiar landmarks2,3 and measuring the distance and direction that the animal has travelled from previously occupied locations4-7. The latter mechanism, known as path integration, requires a finely tuned gain factor that relates the animals self-movement to the updating of position on the internal cognitive map, with external landmarks necessary to correct positional error that eventually accumulates8,9. Path-integration-based models of hippocampal place cells and entorhinal grid cells treat the path integration gain as a constant9-14, but behavioral evidence in humans suggests that the gain is modifiable15. Here we show physiological evidence from hippocampal place cells that the path integration gain is indeed a highly plastic variable that can be altered by persistent conflict between self-motion cues and feedback from external landmarks. In a novel, augmented reality system, visual landmarks were moved in proportion to the animals movement on a circular track, creating continuous conflict with path integration. Sustained exposure to this cue conflict resulted in predictable and prolonged recalibration of the path integration gain, as estimated from the place cells after the landmarks were extinguished. We propose that this rapid plasticity keeps the positional update in register with the animals movement in the external world over behavioral timescales (mean 50 laps over 35 minutes). These results also demonstrate that visual landmarks not only provide a signal to correct cumulative error in the path integration system, as has been previously shown4,8,16-19, but also rapidly fine-tune the integration computation itself.

neuroscience↗