Biology subjects

Branco, T.

Publications and source records attributed to Branco, T..

14 recordsLinked to original sources

bioRxiv2026

A cellular midbrain mechanism for executing fast and reliable escape

Escape from threat is one of the most ancient and conserved sensorimotor transformations and must satisfy two competing demands. It must be fast and reliable, because failing to escape genuine threats risks death, yet also selective, because escaping indiscriminately costs energy and missed resources 1,2. Speed and reliability favour a system with a low threshold that responds to the slightest indication of danger, whereas selectivity demands a high threshold that filters out innocuous stimuli -- yet both must be achieved simultaneously. While previous work has identified mechanisms for implementing selectivity 3-6, it is not known how the mammalian brain achieves speed and reliability once genuine threats have been identified. Here we use whole-cell recordings from dorsal periaqueductal gray (dPAG) neurons in behaving mice to show that the escape circuit resolves this tension via high intrinsic excitability at the single cell level. We find that while the membrane potential of dPAG neurons does not reach action potential threshold during exploratory behaviour, they require only a small amount of current to fire. Threat stimuli cause a sparse increase in synaptic input rate that, because of the high excitability, produces a sustained depolarizing voltage step that drives spiking and escape behaviour. Individual dPAG neurons respond similarly on escape and non-escape trials --what determines escape is the fraction of the dPAG population that is recruited, a finding we confirm with single-unit recordings in freely moving mice. The membrane voltage step response is also invariant to threat type, in contrast to superior colliculus neurons where membrane potential dynamics reflect stimulus identity. These findings reveal a single neuron mechanism for fast and reliable escape, in which the high intrinsic excitability of dPAG neurons transforms sparse, threat-evoked synaptic input into a rapid and stereotyped voltage signal, converting diverse threat representations into a uniform escape command.

neuroscience↗

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↗

bioRxiv2023

Tonically active GABAergic neurons in the dorsal periaqueductal gray control the initiation and execution of instinctive escape

To avoid predation, animals perform defensive actions that are both instinctive and adaptable to the environment. In mice, the decision to escape from imminent threats is implemented by a feed-forward circuit in the midbrain, where excitatory VGluT2+ neurons in the dorsal periaqueductal gray (dPAG) compute escape initiation and escape vigour from threat evidence. Here we show that GABAergic VGAT+ neurons in the dPAG dynamically control this process by modulating the excitability of excitatory escape neurons. Using in vitro patchclamp and in vivo neural activity recordings in freely behaving mice we found that VGAT+ dPAG neurons fire action potentials tonically in the absence of synaptic inputs and are a major source of synaptic inhibition to VGluT2+ dPAG neurons. Activity in these spontaneously firing VGAT+ cells transiently decreases at escape onset and increases during escape, peaking at escape termination. Optogenetically increasing or decreasing VGAT+ dPAG activity bidirectionally changes the probability of escape when the stimulation is delivered at the time of threat onset, and the duration of escape when delivered after escape initiation. We conclude that the activity of tonically firing VGAT+ dPAG neurons sets a threshold for escape initiation and controls the execution of the flight locomotor action.

neuroscience↗

bioRxiv2021

Mice identify subgoal locations through an action-driven mapping process

Mammals instinctively explore and form mental maps of their spatial environments. Models of cognitive mapping in neuroscience mostly depict map-learning as a process of random or biased diffusion. In practice, however, animals explore spaces using structured, purposeful, sensory-guided actions. Here we test the hypothesis that executing specific exploratory actions is a key strategy for building a cognitive map. Previous work has shown that in arenas with obstacles and a shelter, mice spontaneously learn efficient multi-step escape routes by memorizing allocentric subgoal locations. We thus used threat-evoked escape to probe the relationship between ethological exploratory behavior and allocentric spatial memory. Using closed-loop neural manipulations to interrupt running movements during exploration, we found that blocking runs targeting an obstacle edge abolished subgoal learning. In contrast, blocking other movements while sparing edge-directed runs had no effect on memorizing subgoals. Finally, spatial analyses suggest that the decision to use a subgoal during escape takes into account the mouses starting position relative to the layout of the environment. We conclude that mice use an action-driven learning process to identify subgoals and that these subgoals are then integrated into a map-based planning process. We suggest a conceptual framework for spatial learning that is compatible with the successor representation from reinforcement learning and sensorimotor enactivism from cognitive science.

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

Differential geometry methods for constructing manifold-targeted recurrent neural networks.

Neural computations can be framed as dynamical processes, whereby the structure of the dynamics within a neural network are a direct reflection of the computations that the network performs. A key step in generating mechanistic interpretations within this computation through dynamics framework is to establish the link between network connectivity, dynamics and computation. This link is only partly understood. Recent work has focused on producing algorithms for engineering artificial recurrent neural networks (RNN) with dynamics targeted to a specific goal manifold. Some of these algorithms only require a set of vectors tangent to the target manifold to be computed, and thus provide a general method that can be applied to a diverse set of problems. Nevertheless, computing such vectors for an arbitrary manifold in a high dimensional state space remains highly challenging, which in practice limits the applicability of this approach. Here we demonstrate how topology and differential geometry can be leveraged to simplify this task, by first computing tangent vectors on a low-dimensional topological manifold and then embedding these in state space. The simplicity of this procedure greatly facilitates the creation of manifold-targeted RNNs, as well as the process of designing task-solving on-manifold dynamics. This new method should enable the application of network engineering-based approaches to a wide set of problems in neuroscience and machine learning. Furthermore, our description of how fundamental concepts from differential geometry can be mapped onto different aspects of neural dynamics is a further demonstration of how the language of differential geometry can enrich the conceptual framework for describing neural dynamics and computation.

neuroscience↗

bioRxiv2021

Tools for accurate post hoc determination of marker location within whole-brain microscopy images

To interpret in vivo experiments designed to understand brain function, high-resolution whole-brain microscopy provides a means for post hoc determination of the location of implanted devices and recorded cells in three dimensional brain space that is a critical step for data interrogation. Here we have developed Python-based tools (brainreg and brainreg-segment) to accurately map, in a common coordinate space, the position of dye-labelled probe tracks and two-photon imaged cell populations expressing fluorescent protein. The precise location of probes and cells were validated using physiological recordings and human raters that indicate accuracy levels to less than 70{micro}m. These flexible, open-source methodologies are expected to further evolve with need and to deliver the anatomical precision that is necessary for understanding the functional architecture of the brain.

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↗

bioRxiv2020

Brainrender. A python based software for visualisation of neuroanatomical and morphological data.

The recent development of high-resolution three-dimensional (3D) digital brain atlases and high-throughput brain wide imaging techniques has fueled the generation of large datasets that can be registered to a common reference frame. This registration facilitates integrating data from different sources and resolutions to assemble rich multidimensional datasets. Generating insights from these new types of datasets depends critically on the ability to easily visualize and explore the data in an interactive manner. This is, however, a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. To address this challenge, we have developed brainrender: a generic, open-source Python package for simultaneous and interactive visualization of multidimensional datasets registered to brain atlases. Brainrender has been designed to facilitate the creation of complex custom renderings and can be used programmatically or through a graphical user interface. It can easily render different data types in the same visualization, including user-generated data, and enables seamless use of different brain atlases using the same code base. In addition, brainrender generates high-quality visualizations that can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

neuroscience↗

bioRxiv2018

A synaptic threshold mechanism for computing escape decisions

Escaping from imminent danger is an instinctive behaviour fundamental for survival that requires classifying sensory stimuli as harmless or threatening. The absence of threat allows animals to forage for essential resources, but as the level of threat and potential for harm increases, they have to decide whether or not to seek safety1. Despite previous work on instinctive defensive behaviours in rodents2-13, little is known about how the brain computes the threat level for initiating escape. Here we show that the probability and vigour of escape in mice scale with the intensity of innate threats, and are well described by a theoretical model that computes the distance between threat level and an escape threshold. Calcium imaging and optogenetics in the midbrain of freely behaving mice show that the activity of excitatory VGluT2+ neurons in the deep layers of the medial superior colliculus (mSC) represents the threat stimulus intensity and is predictive of escape, whereas dorsal periaqueductal gray (dPAG) VGluT2+ neurons encode exclusively the escape choice and control escape vigour. We demonstrate a feed-forward monosynaptic excitatory connection from mSC to dPAG neurons that is weak and unreliable, yet necessary for escape behaviour, and which we suggest provides a synaptic threshold for dPAG activation and the initiation of escape. This threshold can be overcome by high mSC network activity because of short-term synaptic facilitation and recurrent excitation within the mSC, which amplifies and sustains synaptic drive to the dPAG. Thus, dPAG VGluT2+ neurons compute escape decisions and vigour using a synaptic mechanism to threshold threat information received from the mSC, and provide a biophysical model of how the brain performs a critical behavioural computation.

neuroscience↗

bioRxiv2017

Global, multiplexed dendritic computations under in vivo-like conditions

Dendrites integrate inputs in highly non-linear ways, but it is unclear how these non-linearities contribute to the overall input-output transformation of single neurons. Here, we developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex spatio-temporal synaptic input patterns. We used the hLN to predict the membrane potential of a detailed biophysical model of a L2/3 pyramidal cell receiving in vivo-like synaptic input and reproducing in vivo dendritic recordings. We found that more than 90% of the somatic response could be captured by linear integration followed a single global non-linearity. Multiplexing inputs into parallel processing channels could improve prediction accuracy by as much as additional layers of local non-linearities. These results provide a data-driven characterisation of a key building block of cortical circuit computations: dendritic integration and the input-output transformation of single neurons during in vivo-like conditions.

neuroscience↗

bioRxiv2017

Rapid spatial learning controls instinctive defensive behavior in mice

Instinctive defensive behaviors are essential for animal survival. Across the animal kingdom there are sensory stimuli that innately represent threat and trigger stereotyped behaviors such as escape or freezing [1-4]. While innate behaviors are considered to be hard-wired stimulus-responses [5], they act within dynamic environments, and factors such as the properties of the threat [6-9] and its perceived intensity [1, 10, 11], access to food sources [12-14] or expectations from past experience [15, 16], have been shown to influence defensive behaviors, suggesting that their expression can be modulated. However, despite recent work [2, 4, 17-21], little is known about how flexible mouse innate defensive behaviors are, and how quickly they can be modified by experience. To address this, we have investigated the dependence of escape behavior on learned knowledge about the spatial environment, and how the behavior is updated when the environment changes acutely. Using behavioral assays with innately threatening visual and auditory stimuli, we show that the primary goal of escape in mice is to reach a previously memorized shelter location. Memory of the escape target can be formed in a single shelter visit lasting less than 20 seconds, and changes in the spatial environment lead to a rapid update of the defensive action, including changing the defensive strategy from escape to freezing. Our results show that while there are innate links between specific sensory features and defensive behavior, instinctive defensive actions are surprisingly flexible and can be rapidly updated by experience to adapt to changing spatial environments.

neuroscience↗