Biology subjects

Midler, B.

Publications and source records attributed to Midler, B..

5 recordsLinked to original sources

bioRxiv2026

Evolution imposes an inductive bias that alters and accelerates learning dynamics

The learning dynamics of biological brains and artificial neural networks are of interest to both neuroscience and machine learning. A key difference between them is that neural networks are often trained from a randomly initialized state whereas each brain is the product of generations of evolutionary optimization, yielding innate structures that enable few-shot learning and inbuilt reflexes. Artificial neural networks, by contrast, require non-ethological quantities of training data to attain comparable performance. To investigate the effect of evolutionary optimization on the learning dynamics of neural networks, we combined algorithms simulating natural selection and online learning to produce a method for evolutionarily conditioning artificial neural networks, and applied it to both reinforcement and supervised learning contexts. We found the evolutionary conditioning algorithm, by itself, performs comparably to an unoptimized baseline. However, evolutionarily conditioned networks show signs of unique and latent learning dynamics, and can be rapidly fine-tuned to optimal performance. These results suggest evolution constitutes an inductive bias that tunes neural systems to enable rapid learning.

neuroscience↗

bioRxiv2026

Lightning Pose 3D: an uncertainty-aware framework for data-efficient multi-view animal pose estimation

Multi-view pose estimation is essential for quantifying animal behavior in scientific research, yet current methods struggle to achieve accurate tracking with limited labeled data and suffer from poor uncertainty estimates. We address these challenges with a flexible framework that can operate with or without camera calibration, combining novel training and post-processing techniques with an uncertainty-aware pseudo-labeling distillation procedure. Our multi-view model processes all camera views jointly using a pretrained vision transformer backbone, and a simulated occlusion technique encourages the model to learn robust cross-view correspondences without requiring camera parameters. When camera parameters are available, 3D data augmentations and a triangulation-based loss further encourage geometric consistency. We extend the Ensemble Kalman Smoother (EKS) post-processor to the nonlinear case, leveraging camera geometry, and introduce a variance inflation technique that detects cross-view inconsistencies and corrects overconfident predictions. We validate our approach on five datasets spanning three species (fly, mouse, bird), including a multi-animal dataset with two visually distinct individuals; the proposed pipeline consistently outperforms existing methods across datasets. We demonstrate how these improvements translate to downstream scientific analyses using data from the International Brain Laboratory, showing improved unsupervised behavioral clustering and neural decoding of paw kinematics with just 200 labeled frames. To facilitate adoption, we developed a browser-based, cloud-compatible user interface that supports the full life cycle of multi-view pose estimation, from labeling and model training to post-processing with EKS and diagnostic visualizations.

neuroscience↗

bioRxiv2025

Striatal pathways oppositely shift cortical activity along the decision axis

The cortex and basal ganglia are organized into multiple parallel loops that serve motor, limbic, and cognitive functions. The classic model of cortico-basal ganglia interactions posits that within each loop, the direct pathway of the basal ganglia activates the cortex and the indirect pathway inhibits it1-3. While this model has found support in the motor domain4,5, whether opponent control by the two pathways extends to the cognitive domain remains unknown. Here, we record from anterior cingulate cortex (ACC) and dorsomedial striatum (DMS) while inhibiting direct or indirect pathway neurons in DMS, as mice perform an accumulation-of-evidence task6-10. Inconsistent with the classic model, the manipulations do not produce opponent changes in overall ACC activity. Instead, the pathways exert opponent influence over a subpopulation of ACC neurons that encode accumulated sensory evidence, the task-relevant decision variable. The direction of the modulation depends on a neurons tuning to ipsilateral versus contralateral evidence, such that the two pathways generate opponent shifts in coding specifically along the decision axis. Thus, our results uncover unexpected specificity in the effects of basal ganglia pathways on the cortex, with the two pathways of the DMS exerting opponent control not on overall activity but on coding of the relevant task variable. This functional specificity may extend to other basal ganglia loops to support different aspects of adaptive behavior, with the pathways serving a general role in selecting and shifting cortical representations to subserve circuit-specific functions.

neuroscience↗

bioRxiv2023

Retrieval induced forgetting in a non-monotonic hippocampal model

Retrieval induced forgetting (RIF) occurs when the retrieval of one item negatively impacts the recall probability of related items stored in memory (Anderson et al., 1994). Recently, Ritvo et al. (2023) demonstrated RIF emerges in a neural network model equipped with non-monotonic plasticity. Their finding supports the non-monotonic plasticity hypothesis (NMPH; Ritvo et al., 2019): the theory that connection changes in the brain follow a "U" shaped function of post-synaptic stimulation. Here, we apply a unique implementation of non-monotonic plasticity to a neural network model of an idealized hippocampus (HPC) and evaluate it with an adaptation of a classic RIF task. The model evidences the behavioral and representational characteristics of RIF, replicating Ritvo et al. (2023). As a monotonic baseline model failed these tests, we provide evidence of non-monotonic plasticitys sufficiency for RIF. In addition to demonstrating the NMPH is robust to multiple implementations and evaluative paradigms, we conduct additional analysis to provide a mechanistic explanation for how non-monotonic plasticity brings about RIF. Lastly, we evaluate the model with an expansion of RIF: reverse RIF. The model fails this final test, raising questions for future research on the necessary parameters of non-monotonic plasticity and whether it must pair with complementary processes in the brain.

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↗