Biology subjects

Abrate, M. P.

Publications and source records attributed to Abrate, M. P..

4 recordsLinked to original sources

bioRxiv2025

From movement to cognitive maps: recurrent neural networks reveal how locomotor development shapes hippocampal spatial coding

AO_SCPLOWBSTRACTC_SCPLOWThe hippocampus contains neurons whose firing correlates with an animals location and orientation in space. Collectively, these neurons are held to support a cognitive map of the environment, enabling the recall of and navigation to specific locations. Although recent studies have characterised the timelines of spatial neuron development, no unifying mechanistic model has yet been proposed. Moreover, the processes driving the emergence of spatial representations in the hippocampus remain unclear (Tan et al., 2017). Here, we combine computational analysis of postnatal locomotor development with a recurrent neural network (RNN) model of hippocampal function to demonstrate how changes in movement statistics - and the resulting sensory experiences - shape the formation of spatial tuning. First, we identify distinct developmental stages in rat locomotion during open-field exploration using published experimental data. Then, we train shallow RNNs to predict upcoming visual stimuli from concurrent visual and vestibular inputs, exposing them to trajectories that reflect progressively maturing locomotor patterns. Our findings reveal that these changing movement statistics drive the sequential emergence of spatially tuned units, mirroring the developmental timeline observed in rats. The models generate testable predictions about how spatial tuning properties mature - predictions we confirm through analysis of hippocampal recordings. Critically, we demonstrate that replicating the specific statistics of developmental locomotion - rather than merely accelerating sensory change - is essential for the emergence of an allocentric spatial representation. These results establish a mechanistic link between embodied sensorimotor experience and the ontogeny of hippocampal spatial neurons, with significant implications for neurodevelopmental research and predictive models of navigational brain circuits.*

neuroscience↗

bioRxiv2024

Disordered Hippocampal Reactivations Predict Spatial Memory Deficits in a Mouse Model of Alzheimer's Disease

Alzheimers disease (AD) is characterised by progressive memory decline associated with hippocampal degeneration. However, the specific physiological mechanisms underlying hippocampal dysfunction in AD remain poorly understood and improved knowledge may aid both diagnosis and help identify new avenues for therapeutic intervention. We investigated how disruptions in hippocampal reactivations relate to place cell stability and spatial memory deficits in an AD mouse model. Using the APP knock-in mouse model NL-G-F, we conducted simultaneous behavioural and electrophysiological recordings in a radial arm maze. NL-G-F mice exhibited significant impairments in memory performance, demonstrated by an increased propensity to revisit arms, compared to wild-type controls. These memory deficits were associated with a reduction in the stability of hippocampal place cells, which occurred over short timescales and was accentuated across rest. While the rate of hippocampal reactivation events during rest was unchanged, the structure of these events was significantly degraded in NL-G-F mice. The decreased structure of reactivations was predictive of decreased stability in place cell firing. These findings suggest that disrupted reactivation sequences may act as a mechanism of memory disorder in AD.

neuroscience↗

bioRxiv2024

Fast and Accurate LSTM Meta-modeling of TNF-induced Tumor Resistance In Vitro

Multi-level, hybrid models and simulations, among other methods, are essential to enable predictions and hypothesis generation in systems biology research. However, the computational complexity of these models poses a bottleneck, limiting the applicability of methodologies relying on large number of simulations, such as the Optimization via Simulation (OvS) of complex biological processes. Meta-models based on approximate surrogate models simplify multi-level simulations, maintaining accuracy while reducing computational costs. Among Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM) networks are well suited to handle sequential data, which often characterizes biological simulations. This paper presents an LSTM-based surrogate modeling approach for multi-level simulations of complex biological processes. Validation relies on the simulation of Tumor Necrosis Factor (TNF) administration to a 3T3 mouse fibroblasts tumor spheroid based on PhysiBoSS 2.0, a hybrid agent-based multi-level modeling framework. Results show that the proposed LSTM meta-model is accurate and fast compared with the simulator. In fact, it infers simulated behavior with an average relative error of 7.5%. Moreover, it is at least five orders of magnitude faster. Even considering the cost of training, this approach provides a faster, more accurate, and reusable surrogate of multi-scale simulations in computationally complex tasks, such as model-based OvS of biological processes.

bioinformatics↗

bioRxiv2020

LiftPose3D, a deep learning-based approach for transforming 2D to 3D pose in laboratory animals

Markerless 3D pose estimation has become an indispensable tool for kinematic studies of laboratory animals. Most current methods recover 3D pose by multi-view triangulation of deep network-based 2D pose estimates. However, triangulation requires multiple, synchronized cameras and elaborate calibration protocols that hinder its widespread adoption in laboratory studies. Here, we describe LiftPose3D, a deep network-based method that overcomes these barriers by reconstructing 3D poses from a single 2D camera view. We illustrate LiftPose3Ds versatility by applying it to multiple experimental systems using flies, mice, rats, and macaque monkeys and in circumstances where 3D triangulation is impractical or impossible. Our framework achieves accurate lifting for stereotyped and non-stereotyped behaviors from different camera angles. Thus, LiftPose3D permits high-quality 3D pose estimation in the absence of complex camera arrays, tedious calibration procedures, and despite occluded body parts in freely behaving animals.

animal behavior and cognition↗