Biology subjects

Campagner, D.

Publications and source records attributed to Campagner, D..

8 recordsLinked to original sources

bioRxiv2026

Presynaptic temporal dynamics flexibly set input weights in the mouse escape circuit

Animals facing threat must integrate multiple streams of information -- about danger, environment, and internal state -- into a time-pressured escape decision. In mice, this computation is performed by glutamatergic neurons of the dorsal periaqueductal grey (dPAG), but how their convergent long-range inputs combine to drive flexible decisions is unknown. Here we find that the functional weight of each input is set predominantly by the temporal statistics of its presynaptic activity, rather than by pathway identity or synaptic placement. We first used multi-region single unit recordings during naturalistic behaviour and generalised linear models to estimate the functional connectivity from midbrain, hypothalamic, and cortical inputs onto dPAG neurons. We then combined synapse-resolution circuit tracing, two-photon dendritic stimulation with whole-cell somatic and dendritic recordings, and biophysical modelling to identify the mechanisms setting these weights. We found that dPAG neurons are electrotonically compact, generating broadly uniform somatic responses to inputs across the dendritic tree. As a result, presynaptic firing dynamics -- burstiness within neurons and population synchrony -- are the dominant determinants of input efficacy. This temporal-statistics framework accounts for the measured differences in functional connectivity across input regions and predicts that input weights should change dynamically whenever presynaptic temporal structure shifts -- which we confirm by showing rapid, context-dependent reweighting of cortical input during motivational conflict. We propose that the subcellular specialisations of dPAG neurons allow them to integrate signals from distributed sources into a single decision, with input weights that can be flexibly adjusted on behavioural timescales -- a principle that may extend to other brain hubs that compute survival decisions.

neuroscience↗

bioRxiv2025

Aeon: an open-source platform to study the neural basis of ethological behaviours over naturalistic timescales

Ethological behaviours are a powerful tool for neuroscience since they leverage the robust neural computations shaped by the species evolution to study the neural basis of cognitive functions. However, such behaviours are often transitory and dependent on factors that vary over space, time and number of individuals, making them difficult to capture with standard laboratory tasks. Here we present Aeon, an open-source platform designed for continuous, long-term study of self-guided behaviours in multiple mice and simultaneous recording of brain activity within large, customizable habitats. By integrating specialized modules for navigation, nesting and sleeping, escaping, foraging, and social interaction, Aeon enables the expression of key ethological behaviours while achieving experimental control and multi-dimensional quantifications from sub-millisecond to month-long durations. Its software architecture ensures robust data acquisition via many synchronized data streams and delivers a new standardised, unified data format that yields seamless, integrated analysis pipelines. Using assays such as digging-to-threshold and social foraging, Aeon reveals how mice adapt strategies in a changing environment and in response to conspecifics. Aeon bridges ecological relevance with rigorous experimental control to advance our understanding of how neural circuit activity gives rise to a range of highly conserved and adaptive behaviours.

neuroscience↗

bioRxiv2021

Innate heuristics and fast learning support escape route selection in mice

When faced with imminent danger, animals must rapidly take defensive actions to reach safety. Mice can react to innately threatening stimuli in less than 250 milliseconds [1] and, in simple environments, use spatial memory to quickly escape to shelter [2,3]. Natural habitats, however, often offer multiple routes to safety which animals must rapidly identify and choose from to maximize the chances of survival [4]. This is challenging because while rodents can learn to navigate complex mazes to obtain rewards [5,6], learning the value of different routes through trial-and-error during escape from threat would likely be deadly. Here we have investigated how mice learn to choose between different escape routes to shelter. By using environments with paths to shelter of varying length and geometry we find that mice prefer options that minimize both path distance and path angle relative to the shelter. This choice strategy is already present during the first threat encounter and after only ~10 minutes of exploration in a novel environment, indicating that route selection does not require experience of escaping. Instead, an innate heuristic is used to assign threat survival value to alternative paths after rapidly learning the spatial environment. This route selection process is flexible and allows quick adaptation to arenas with dynamic geometries. Computational modelling of different classes of reinforcement learning agents shows that the observed behavior can be replicated by model-based agents acting in an environment where the shelter location is rewarding during exploration. These results show that mice combine fast spatial learning with innate heuristics to choose escape routes with the highest survival value. They further suggest that integrating priors acquired through evolution with knowledge learned from experience supports adaptation to changing environments while minimizing the need for trial-and-error when the errors are very costly.

neuroscience↗

bioRxiv2021

The retrosplenial cortex combines internal and external cues to encode head velocity during navigation

The extent to which we successfully navigate the environment depends on our ability to continuously track our heading direction and speed. Angular head velocity (AHV) cells, which encode the speed and direction of head turns during navigation, are fundamental to this process, yet the mechanisms that determine their function remain unknown. By performing chronic single-unit recordings in the retrosplenial cortex (RSP) of the mouse and tracking the activity of individual AHV neurons between freely moving and head-restrained conditions, we find that vestibular inputs dominate AHV signalling. In addition, we discover that self-generated optic flow input onto these neurons increases the gain and signal-to-noise ratio of angular velocity coding during free exploration. Psychophysical experiments and neural decoding further reveal that vestibular-visual integration increases the perceptual accuracy of egocentric angular velocity and the fidelity of its representation by RSP ensembles. We propose that while AHV coding is dependent on vestibular input, it also uses vision to maximise navigation accuracy in nocturnal and diurnal environments.

neuroscience↗

bioRxiv2020

Mice learn multi-step routes by memorizing subgoal locations

The behavioral strategies that mammals use to learn multi-step routes in natural settings are unknown. Here we show that mice spontaneously adopt a subgoal memory strategy. We first investigated how mice navigate to shelter in response to threats when the direct path is blocked. Initially, they fled toward the shelter and negotiated obstacles using sensory cues. Within twenty minutes, they adopted a subgoal strategy, initiating escapes by running directly to the obstacles edge. Mice continued to target this subgoal location after the obstacle was removed, indicating use of spatial memory. However, standard models of spatial learning - egocentric-movement repetition and internal-map building - did not explain how subgoal memories formed. Instead, mice used a hybrid approach: memorizing salient locations encountered during spontaneous practice runs. This strategy was also used during geometrically identical reward-seeking behavior. These results suggest that subgoal memorization is a fundamental strategy by which rodents learn efficient multi-step routes in new environments.

neuroscience↗

bioRxiv2020

A cortico-collicular circuit for accurate orientation to shelter during escape

When faced with predatorial threats, escape towards shelter is an adaptive action that offers long-term protection against the attacker. From crustaceans to mammals, animals rely on knowledge of safe locations in the environment to instinctively execute rapid shelter-directed escape actions 1,2. While previous work has identified neural mechanisms of escape initiation3-5, it is not known how the escape circuit incorporates spatial information to execute rapid flights along the most efficient route to shelter. Here we show that mouse retrosplenial cortex (RSP) and superior colliculus (SC) form a circuit that encodes shelter direction vector and is specifically required for accurately orienting to shelter during escape. Shelter direction is encoded in RSP and SC neurons in egocentric coordinates and SC shelter-direction tuning depends on RSP activity. Inactivation of the RSP-SC pathway disrupts orientation to shelter and causes escapes away from the optimal shelter-directed route, but does not lead to generic deficits in orientation or spatial navigation. We find that the RSP and SC are monosynaptically connected and form a feedforward lateral inhibition microcircuit that strongly drives the inhibitory collicular network due to higher RSP input convergence and synaptic integration efficiency in inhibitory SC neurons. This results in broad shelter direction tuning in inhibitory SC neurons and sharply tuned excitatory SC neurons. These findings are recapitulated by a biologically-constrained spiking network model where RSP input to the local SC recurrent ring architecture generates a circular shelter-direction map. We propose that this RSP-SC circuit might be specialized for generating collicular representations of memorized spatial goals that are readily accessible to the motor system during escape, or more broadly, during navigation when the goal must be reached as fast as possible.

neuroscience↗

bioRxiv2019

A system for tracking whisker kinematics and whisker shape in three dimensions

Quantification of behaviour is essential for systems neuroscience. Since the whisker system is a major model system for investigating the neural basis of behaviour, it is important to have methods for measuring whisker movements from behaving animals. Here, we developed a high-speed imaging system that measures whisker movements simultaneously from two vantage points. We developed an algorithm that uses the stereo video data to track multiple whiskers by fitting 3D curves to the basal section of each target whisker. By using temporal information to constrain the fits, the algorithm is able to track multiple whiskers in parallel with low error rate. We used the output of the tracker to produce a 3D description of each tracked whisker, including its 3D orientation and 3D shape, as well as bending-related mechanical force. In conclusion, we present an automatic system to track whiskers in 3D from high-speed video, creating the opportunity for comprehensive 3D analysis of sensorimotor behaviour and its neural basis.\n\nAuthor summaryThe great ethologist Niko Tinbergen described a crucial challenge in biology to measure the \"total movements made by the intact animal\". Advances in high-speed video and machine analysis of such data have made it possible to make profound advances. Here, we target the whisker system. The whisker system is a major experimental model in neurobiology and, since the whiskers are readily imageable, the system is ideally suited to machine vision. Rats and mice explore their environment by sweeping their whiskers to and fro. It is important to measure whisker movements in 3D, since whiskers move in 3D and since the mechanical forces that act on them are 3D. However, the problem of automatically tracking whiskers in 3D from video has generally been regarded as prohibitively difficult. Our innovation here is to extract 3D information about whiskers using a two-camera, high-speed imaging system and to develop computational methods to infer 3D whisker state from the imaging data. Our hope is that this study will facilitate comprehensive, 3D analysis of whisker behaviour and, more generally, contribute new insight into brain mechanisms of perception and behaviour.

neuroscience↗

bioRxiv2018

Prediction of Choice from Competing Mechanosensory and Choice-Memory Cues During Active Tactile Decision Making

Perceptual decision making is an active process where animals move their sense organs to extract task-relevant information. To investigate how the brain translates sensory input into decisions during active sensation, we developed a mouse active touch task where the mechanosensory input can be precisely measured and that challenges animals to use multiple mechanosensory cues. Mice were trained to localise a pole using a single whisker and to report their decision by selecting one of three choices. Using high-speed imaging and machine vision we estimated whisker-object mechanical forces at millisecond resolution. Mice solved the task by a sensory-motor strategy where both the strength and direction of whisker bending were informative cues to pole location. We found competing influences of immediate sensory input and choice memory on mouse choice. On correct trials, choice could be predicted from the direction and strength of whisker bending, but not from previous choice. In contrast, on error trials, choice could be predicted from previous choice but not from whisker bending. This study shows that animal choices during active tactile decision making can be predicted from mechanosenory and choice-memory signals; and provides a new task, well-suited for future study of the neural basis of active perceptual decisions.\n\nSIGNIFICANCE STATEMENTDue to the difficulty of measuring the sensory input to moving sense organs, active perceptual decision making remains poorly understood. The whisker system provides a way forward since it is now possible to measure the mechanical forces due to whisker-object contact during behaviour. Here we train mice in a novel behavioural task that challenges them to use rich mechanosensory cues, but can be performed using one whisker and enables task-relevant mechanical forces to be precisely estimated. This approach enables rigorous study of how sensory cues translate into action during active, perceptual decision making. Our findings provide new insight into active touch and how sensory/internal signals interact to determine behavioural choices.

animal behavior and cognition↗