Biology subjects

Schaffer, E. S.

Publications and source records attributed to Schaffer, E. S..

6 recordsLinked to original sources

bioRxiv2026

Preserved geometry during representational drift enables stable perception and memory

In many brain regions, the stimulus tuning of neurons is stable on a timescale of hours but not on a timescale of weeks, a phenomenon often called representational drift. This would seem to imply that these brain regions cannot be used for stable recognition of sensory stimuli or the retrieval of associative memories learned several weeks prior. However, decoding approaches have demonstrated that in some cases, stable decoding of drifting representations is possible. In principle, adaptive decoding provides a plausible resolution to the paradox of how the brain operates with drifting representations, but we lack a deep understanding of what the requirements are for stable decoding to be possible. Here, we offer a general mathematical framework that explains when and why stable decoding from a drifting representation can be achieved. First, we demonstrate that both feedforward and recurrent networks preserve the geometry of their inputs when the network is sufficiently large, meaning that representational drift must also preserve geometry in these networks. Second, we demonstrate that drifting representations that have stable geometry are decodable with adaptive decoders. Therefore, not only the existence of preserved geometry in the presence of representational drift but also the ability to decode from drifting representations simply requires the population of neurons exhibiting representational drift to be large. This theoretical framework not only suggests that preserved geometry should be a general feature of drifting representations, it also explains the conditions under which empirical efforts to measure stable geometry will be successful.

neuroscience↗

bioRxiv2026

Identifying Phelan-McDermid-Like Electrophysiological Subtypes in Autism Using EEG and Machine Learning

BackgroundPhelan McDermid syndrome (PMS), caused by SHANK3 haploinsufficiency, is a genetic form of autism spectrum disorder (ASD) that provides a genetically defined model for studying ASD-related circuit dysfunction. SHANK3 mutations disrupt synaptic organization and cortical synchrony, leading to attenuated gamma-band auditory steady-state responses (ASSRs). We investigated whether PMS-related electrophysiological signatures could be identified using machine learning and whether similar patterns are present in a subset of individuals with idiopathic ASD (iASD). MethodsEEG recorded during a 40-Hz ASSR paradigm was collected from 123 participants (42 TD aged 2-30, 56 iASD aged 3-31, 25 PMS aged 2-26). We extracted time-series, ERSP, FOOOF-derived spectral, and intertrial phase coherence (ITPC) features. XGBoost models with leave-one-out cross-validation classified PMS versus TD; the best age/sex-adjusted ITPC model was then applied to iASD participants to derive a Synchrony Atypicality Index (SAI). Unsupervised clustering of high-dimensional ITPC features was also performed. ResultsITPC-based models showed the strongest discrimination between TD and PMS participants (AUROC = 0.83). When applied to iASD participants, 35.7% exhibited elevated SAI, indicating a PMS-like gamma-band phase-locking profile. Classification of iASD versus PMS performed poorly in the full sample but improved markedly after excluding high-SAI iASD individuals, consistent with substantial heterogeneity within iASD. Unsupervised clustering of ITPC features identified PMS-enriched clusters that also captured high-SAI iASD participants. Results were consistent after controlling for age in sensitivity analyses. ConclusionsReduced 40-Hz ITPC is a mechanistically interpretable electrophysiological signature of PMS and identifies a biologically meaningful PMS-like subgroup within iASD, supporting biomarker-guided stratification.

neuroscience↗

bioRxiv2025

Asymmetric Social Representations in the Prefrontal Cortex for Cooperative Behavior

Cooperation is a hallmark of social species, enabling individuals to achieve goals that are unattainable alone. Across species, cooperative behaviors are often organized by distinct social roles such as leaders and followers, yet the neural mechanisms supporting such role-based coordination remain elusive. Here we introduce a new paradigm for studying cooperation in mice, where pairs of animals engage in a joint spatial foraging task that naturally gives rise to stable leader-follower roles predictive of learning speed. Disruption of medial prefrontal cortex (mPFC) activity, particularly in followers, impairs cooperation and induces reciprocal shifts in how animals weigh self- and partner-related cues for decision-making. Calcium imaging reveals that mPFC encodes both leadership dynamics and an egocentric social value map of the partners position, each in an asymmetric, role-specific manner. Combining this behavior with a novel multi-agent inverse reinforcement learning framework, we identify latent value functions that guide cooperative decisions and are decodable from mPFC activity. These findings uncover fundamental neural computations that support cooperation, revealing how social roles shape decision-making in real time. Our work opens new avenues for investigating the cellular and circuit basis of social cognition and collective behavior.

neuroscience↗

bioRxiv2021

Flygenvectors: The spatial and temporal structure of neural activity across the fly brain

What are the spatial and temporal scales of brainwide neuronal activity, and how do activities at different scales interact? We used SCAPE microscopy to image a large fraction of the central brain of adult Drosophila melanogaster with high spatiotemporal resolution while flies engaged in a variety of behaviors, including running, grooming and flailing. This revealed neural representations of behavior on multiple spatial and temporal scales. The activity of most neurons across the brain correlated (or, in some cases, anticorrelated) with running and flailing over timescales that ranged from seconds to almost a minute. Grooming elicited a much weaker global response. Although these behaviors accounted for a large fraction of neural activity, residual activity not directly correlated with behavior was high dimensional. Many dimensions of the residual activity reflect the activity of small clusters of spatially organized neurons that may correspond to genetically defined cell types. These clusters participate in the global dynamics, indicating that neural activity reflects a combination of local and broadly distributed components. This suggests that microcircuits with highly specified functions are provided with knowledge of the larger context in which they operate, conferring a useful balance of specificity and flexibility.

neuroscience↗

bioRxiv2021

Semi-supervised sequence modeling for improved behavioral segmentation

A popular approach to quantifying animal behavior from video data is through discrete behavioral segmentation, wherein video frames are labeled as containing one or more behavior classes such as walking or grooming. Sequence models learn to map behavioral features extracted from video frames to discrete behaviors, and both supervised and unsupervised methods are common. However, each approach has its drawbacks: supervised models require a time-consuming annotation step where humans must hand label the desired behaviors; unsupervised models may fail to accurately segment particular behaviors of interest. We introduce a semi-supervised approach that addresses these challenges by constructing a sequence model loss function with (1) a standard supervised loss that classifies a sparse set of hand labels; (2) a weakly supervised loss that classifies a set of easy-to-compute heuristic labels; and (3) a self-supervised loss that predicts the evolution of the behavioral features. With this approach, we show that a large number of unlabeled frames can improve supervised segmentation in the regime of sparse hand labels and also show that a small number of hand labeled frames can increase the precision of unsupervised segmentation.

animal behavior and cognition↗

bioRxiv2020

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

Noninvasive behavioral tracking of animals is crucial for many scientific investigations. Recent transfer learning approaches for behavioral tracking have considerably advanced the state of the art. Typically these methods treat each video frame and each object to be tracked independently. In this work, we improve on these methods (particularly in the regime of few training labels) by leveraging the rich spatiotemporal structures pervasive in behavioral video -- specifically, the spatial statistics imposed by physical constraints (e.g., paw to elbow distance), and the temporal statistics imposed by smoothness from frame to frame. We propose a probabilistic graphical model built on top of deep neural networks, Deep Graph Pose (DGP), to leverage these useful spatial and temporal constraints, and develop an efficient structured variational approach to perform inference in this model. The resulting semi-supervised model exploits both labeled and unlabeled frames to achieve significantly more accurate and robust tracking while requiring users to label fewer training frames. In turn, these tracking improvements enhance performance on downstream applications, including robust unsupervised segmentation of behavioral "syllables," and estimation of interpretable "disentangled" low-dimensional representations of the full behavioral video. Open source code is available at https://github.com/paninski-lab/deepgraphpose.

animal behavior and cognition↗