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Perona, P.

Publications and source records attributed to Perona, P..

7 recordsLinked to original sources

Rapid spatial cognition in mice, with and without neocortex and hippocampus

Rapid learning, memory, and generalization are often attributed to circuits of the neocortex and hippocampus, but their specific role remains unclear. To examine these cognitive abilities together in individual mice, we observed mice navigating the Manhattan Maze, a reconfigurable 3D labyrinth. Naive wildtype mice improved within two rewards, approached efficient paths within about 20 rewards, retained a 9-turn route overnight, and learned faster in new configurations. Much of the few-shot improvement follows from a rule-based forward bias that emerged even before any reward. In a maze with loops, where that rule is less useful, mice learned within a few rewards to prefer one bottleneck corridor far from the reward location, while choices elsewhere stayed flexible. To begin linking these different components of learning to brain function, we presented the same task to mutant mice that lack the hippocampus and most of the neocortex. They were impaired during initial exploration, where repetitive scanning made them about 3-fold slower to obtain the first few rewards. Past that stage, learning, retention over weeks to months, and generalization to new mazes were largely preserved. Learning this fast is hard to reconcile with trial and error, in which value propagates backwards from the reward. A neuromorphic circuit model instead accounts for the bottleneck choice: it builds a map of the environment without reward, and needs neither cortical nor hippocampal circuit motifs. Therefore, learning is possible without the involvement of neocortex and hippocampus. Structure learned before the first reward, rather than the reward itself, may be what makes few-shot learning possible.

neuroscience↗

Distributed representations of innate behaviors in the hypothalamus do not predict specialized functional centers

Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.

neuroscience↗

A Foundation Model for Cell Segmentation

Cells are a fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, most models are specialist models that work well for specific domains but cannot be applied across domains or scale well with large amounts of data. In this work, we present CellSAM, a universal model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells, yeast, and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. Additionally, we demonstrate how CellSAM can be applied across diverse bioimage analysis workflows. A deployed version of CellSAM is available at https://cellsam.deepcell.org/.

bioinformatics↗

Endotaxis: A Universal Algorithm for Mapping, Goal-Learning, and Navigation

An animal entering a new environment typically faces three challenges: explore the space for resources, memorize their locations, and navigate towards those targets as needed. Experimental work on exploration, mapping, and navigation has mostly focused on simple environments - such as an open arena [68], a pond [42], or a desert [44] - and much has been learned about neural signals in diverse brain areas under these conditions [12, 54]. However, many natural environments are highly structured, such as a system of burrows, or of intersecting paths through the underbrush. Similarly, for many cognitive tasks, a sequence of simple actions can give rise to complex solutions. Here we propose an algorithm that learns the structure of a complex environment, discovers useful targets during exploration, and navigates back to those targets by the shortest path. It makes use of a behavioral module common to all motile animals, namely the ability to follow an odor to its source [4]. We show how the brain can learn to generate internal "virtual odors" that guide the animal to any location of interest. This endotaxis algorithm can be implemented with a simple 3-layer neural circuit using only biologically realistic structures and learning rules. Several neural components of this scheme are found in brains from insects to humans. Nature may have evolved a general mechanism for search and navigation on the ancient backbone of chemotaxis.

neuroscience↗

Mice in a labyrinth: Rapid learning, sudden insight, and efficient exploration

Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal quickly discovers the location of a reward in the maze and executes correct 10-bit choices after only 10 reward experiences - a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.

neuroscience↗

The Mouse Action Recognition System (MARS): a software pipeline for automated analysis of social behaviors in mice

The study of naturalistic social behavior requires quantification of animals interactions. This is generally done through manual annotation--a highly time consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely-behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARSs annotations to human annotations and find that MARSs pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS, to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis, in a package that is user-friendly and easily modifiable.

neuroscience↗

Mouse Academy: high-throughput automated training and trial-by-trial behavioral analysis during learning

Progress in understanding how individual animals learn will require high-throughput standardized methods for behavioral training but also advances in the analysis of the resulting behavioral data. In the course of training with multiple trials, an animal may change its behavior abruptly, and capturing such events calls for a trial-by-trial analysis of the animals strategy. To address this challenge, we developed an integrated platform for automated animal training and analysis of behavioral data. A low-cost and space-efficient apparatus serves to train entire cohorts of mice on a decision-making task under identical conditions. A generalized linear model (GLM) analyzes each animals performance at single-trial resolution. This model infers the momentary decision-making strategy and can predict the animals choice on each trial with an accuracy of ~80%. We also assess the animals detailed trajectories and body poses within the apparatus. Unsupervised analysis of these features revealed unusual trajectories that represent hesitation in the response. This integrated hardware/software platform promises to accelerate the understanding of animal learning.

animal behavior and cognition↗