Biology subjects

Grewe, B. F.

Publications and source records attributed to Grewe, B. F..

12 recordsLinked to original sources

bioRxiv2025

Category learning disentangles representation of trial events in hippocampus CA1

The hippocampus (HC) is known to encode task-relevant variables, including latent ones, capturing and parsing critical information from sequences into episodes. This is thought to be the basis to form a cognitive map of possible abstract states beyond mere perceptual details, akin a state machine, with predictive value, contributing to an internal world model. However, its specific role in categorization tasks in general, where learning a category may depend on independent, unordered (non-sequential) experienced examples, remains unclear. Here, we investigate CA1 population coding during a categorization paradigm in mice in combination with calcium imaging at different stages of category training with interleaved generalization tests. Our results reveal that hippocampal coding changes critically throughout the different stages of categorization training. Specifically, the disentangling of choice and outcome variables emerges as factorized, abstract, representations and fundamentally distinguishes simple discrimination from categorization training. Furthermore, this factorized geometry relates to improved behavioral performance. Our findings suggest that trial encoding on HC adapts in response to the category structure presented by representing critical events into the appropriate computational format to support generalization of category membership.

neuroscience↗

bioRxiv2025

Activation of latent programs for maternal behavior

Maternal behaviors in mammals, which are critical for offspring survival, involve complex interactions between innate neural circuits and behavioral flexibility. This flexibility depends on environmental needs and external stimuli. Recent studies suggest that social transmission plays a crucial role in the acquisition of maternal behaviors in alloparental (non-maternal) subjects, particularly highlighting visual observation for virgin female mice to acquire pup retrieval behavior. However, the extent to which these behaviors rely on social transmission versus the activation of pre-existing, latent behaviors remains unclear. Here, we systematically investigated the necessity of social observation for maternal behavior acquisition, using a social transmission pup retrieval paradigm in pup-naive virgin female mice. Following the same protocol as previous studies, we compared retrieval performance on conditions that either allowed or prevented the visual observation of maternal demonstrations. Contrary to previous findings reporting substantial differences between these conditions, we found that virgin females readily acquired pup retrieval behavior regardless of their visual access to experienced mothers. We observed no significant correlation between maternal and virgin performance after task exposure. Detailed behavioral analysis revealed that the primary difference between mothers and virgins lay on the latency to initiate the behavior, but once started, virgins complete the retrieval as fast as mothers. Together, our findings show that pup exposure is enough to activate latent behaviors, and suggest that pup retrieval might not be transmitted by visual observation.

animal behavior and cognition↗

bioRxiv2025

Inhibition from the oxytocin system tunes prefrontal circuit dynamics to promote social behavior

The prefrontal cortex (PFC) plays a central role in the selection and expression of diverse social behaviors. A balance of excitation and inhibition is necessary for normal social functioning, but it remains unclear how inhibition sculpts pyramidal activity to promote specific social behaviors. To address this question, we developed a novel decision-making task in which female mice chose between interaction with a male (sociosexual stimulus) or an appetitive non-social option (milk solution). To explore the role of a task-relevant inhibitory subpopulation, we targeted neurons in the medial PFC (mPFC) expressing oxytocin receptors (OXTR neurons). Combining optogenetic inhibition of OXTR neurons with population calcium imaging of pyramidal neural activity, we found that OXTR neurons normally promote interaction with a male compared to a non-social alternative. OXTR neurons also regulate pyramidal activity, which enables a specific ensemble to represent the male option during decision-making. Computational modeling reproduced these findings through a pyramidal competition mechanism, in which regulation by OXTR neurons allows a male-representing pyramidal ensemble to effectively compete against the remaining pyramidal population to drive male choice. These results provide a candidate mechanism by which inhibition enables mPFC pyramidal activity to select for specific social behaviors and may help explain why excitation/inhibition balance is so important for social functioning.

neuroscience↗

bioRxiv2024

Hierarchical Target Learning in the Mammalian Neocortex: A Pyramidal Neuron Perspective

1Computational neuroscience currently debates two competing hypotheses to explain hierarchical learning in the neocortex: deep learning inspired approximations of the backpropagation algorithm, where neurons adjust synapses to minimize an error, and target learning algorithms, where neurons learn by reducing the feedback needed to achieve a desired target activity. While both hypotheses have been supported by theoretical studies, there is currently no empirical test that compares them directly. Here we provide such tests in the mouse neocortex by evaluating both hypotheses against experimental data at the single cell level and at the population level. At the single cell level, we conduct in vitro experiments that clarify the relationship between algorithmic learning signals and synaptic plasticity within individual pyramidal neurons. At the population level, we analyze in vivo calcium imaging data in the lateral visual cortex. By combining in vivo and in vitro data we reveal a critical discrepancy between neocortical hierarchical learning and canonical machine learning.

neuroscience↗

bioRxiv2023

Simultaneous single-cell calcium imaging of neuronal population activity and brain-wide BOLD fMRI

Blood Oxygen Level-Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) allows for non-invasive, indirect recordings of neural activity across the whole brain in both humans and animals. However, the relationship between the local neural population activity and the vascular activity is not completely understood. To investigate this relationship, we present a novel MRI compatible single-photon microscope capable of measuring cellular resolution Ca2+ activity of genetically defined neurons during whole-brain BOLD fMRI in awake behaving mice. Using this combined imaging approach, we found a difference in activity patterns between cells which was dependent on their location with respect to the vasculature. Notably, neurons near the vasculature showed pronounced negative activity during contralateral whisker movements at 3 Hz. In a second proof of concept experiment, we demonstrate the potential of recording both local neural activities, like those in the barrel field (SSp-bfd), and BOLD fMRI readings from interlinked brain regions. In sum, the presented technological advancement paves the way for studies examining the interplay between local brain circuits and overarching brain functions. In addition, the new approach enhances our understanding of the vascular BOLD fMRI signal, providing insights into the determinants of local neurovascular functions and the brains organizational framework across various scales.

neuroscience↗

bioRxiv2023

Population-level neural correlates of flexible avoidance learning in medial prefrontal cortex

The medial prefrontal cortex (mPFC) has been proposed to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors. However, how such a link is implemented has remained unclear. To measure prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a novel tone-signaled active avoidance paradigm in mice. We developed a novel analysis approach based on dimensionality reduction and decoding that allowed us to identify and isolate population activity patterns related the tone stimulus, learned avoidance actions and general motion. While tone-related activity was not informative about behavior, avoidance-related activity was predictive of upcoming avoidance actions. Moreover, avoidance-related activity distinguished between two different learned avoidance actions, consistent with a model in which mPFC contributes to the selection between different goal-directed actions. Overall, our results suggest that mPFC circuit dynamics transform sensory inputs into specific behavioral outputs through distributed population-level computations.

neuroscience↗

bioRxiv2023

Learning Cortical Hierarchies with Temporal Hebbian Updates.

A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. These same hierarchical structures routinely emerge in artificial neural networks (ANNs) trained for image/object recognition tasks, suggesting that a similar process might underlie biological neural networks. However, the classical ANN training algorithm, backpropagation, is considered biologically implausible, and thus several alternative biologically plausible methods have been developed. For instance, several cortical-inspired ANNs in which the apical dendrite of a pyramidal neuron encodes top-down prediction signals have been proposed. In this case, akin to theories of predictive coding, a prediction error can be calculated locally inside each neuron for updating its incoming weights. Notwithstanding, from a neuroscience perspective, it is unclear whether neurons could compare their apical vs. somatic spiking activities to compute prediction errors. Here, we propose a solution to this problem by adapting the framework of the apical-somatic prediction error to the temporal domain. In particular, we show that if the apical feedback signal changes the postsynaptic firing rate, we can use differential Hebbian updates, a rate-based version of the classical spiking time-dependent plasticity (STDP) updates. To the best of our knowledge, this is the first time a cortical-like deep ANN has been trained using such time-based learning rules. Overall, our work removes a key requirement of biologically plausible models for deep learning that does not align with plasticity rules observed in biology and proposes a learning mechanism that would explain how the timing of neuronal activity can allow supervised hierarchical learning.

neuroscience↗

bioRxiv2021

Striatum-projecting prefrontal cortex neurons support working memory maintenance

The medial prefrontal cortex (mPFC) and the dorsomedial striatum (dmStr) are linked to working memory (WM) but how striatum-projecting mPFC neurons contribute to WM encoding, maintenance, or retrieval remains unclear. Here, we probed mPFC[->]dmStr pathway function in freely-moving mice during a T-maze alternation test of spatial WM. Fiber photometry of GCaMP6m-labeled mPFC[->]dmStr projection neurons revealed strongest activity during the delay period that requires WM maintenance. Demonstrating causality, optogenetic inhibition of mPFC[->]dmStr neurons only during the delay period impaired performance. Conversely, enhancing mPFC[->]dmStr pathway activity--via pharmacological suppression of HCN1 or by optogenetic activation during the delay-- alleviated WM impairment induced by NMDA receptor blockade. Consistently, cellular-resolution miniscope imaging resolved preferred activation of >50% mPFC[->]dmStr neurons during WM maintenance. This subpopulation was distinct from neurons showing preference for encoding and retrieval. In all periods, including the delay, neuronal sequences were evident. Striatum-projecting mPFC neurons thus critically contribute to spatial WM maintenance.

neuroscience↗

bioRxiv2021

Different encoding of reward location in dorsal and ventral hippocampus

Hippocampal neurons encode a cognitive map for spatial navigation1. When they fire at specific locations in the environment, they are known as place cells2. In the dorsal hippocampus place cells accumulate at current navigational goals, such as learned reward locations3-6. In the intermediate-to-ventral hippocampus (here collectively referred to as ventral hippocampus), neurons fire across larger place fields7-10 and regulate reward- seeking behavior11-16, but little is known about their involvement in reward-directed navigation. Here, we compared the encoding of learned reward locations in the dorsal and ventral hippocampus during spatial navigation. We used calcium imaging with a head- mounted microscope to track the activity of CA1 cells over multiple days during which mice learned different reward locations. In dorsal CA1 (dCA1), the overall number of active place cells increased in anticipation of reward but the recruited cells changed with the reward location. In ventral CA1 (vCA1), the activity of the same cells anticipated the reward locations. Our results support a model in which the dCA1 cognitive map incorporates a changing population of cells to encode reward proximity through increased population activity, while the vCA1 provides a reward-predictive code in the activity of a specific subpopulation of cells. Both of these location-invariant codes persisted over time, and together they provide a dual hippocampal reward-location code, assisting goal- directed navigation17, 18.

neuroscience↗

bioRxiv2021

Highly task-specific and distributed neural connectivity in working memory revealed by single-trial decoding in mice and humans

Working memory (WM), the capacity to briefly and intentionally maintain mental items, is key to successful goal-directed behaviour and impaired in a range of psychiatric disorders. To date, several brain regions, connections, and types of neural activity have been correlatively associated with WM performance. However, no unifying framework to integrate these findings exits, as the degree of their species- and task-specificity remains unclear. Here, we investigate WM correlates in three task paradigms each in mice and humans, with simultaneous multi-site electrophysiological recordings. We developed a machine learning-based approach to decode WM-mediated choices in individual trials across subjects from hundreds of electrophysiological measures of neural connectivity with up to 90% prediction accuracy. Relying on predictive power as indicator of correlates of psychological functions, we unveiled a large number of task phase-specific WM-related connectivity from analysis of predictor weights in an unbiased manner. Only a few common connectivity patterns emerged across tasks. In rodents, these were thalamus-prefrontal cortex delta- and beta-frequency connectivity during memory encoding and maintenance, respectively, and hippocampal-prefrontal delta- and theta-range coupling during retrieval, in rodents. In humans, task-independent WM correlates were exclusively in the gamma-band. Mostly, however, the predictive activity patterns were unexpectedly specific to each task and always widely distributed across brain regions. Our results suggest that individual tasks cannot be used to uncover generic physiological correlates of the psychological construct termed WM and call for a new conceptualization of this cognitive domain in translational psychiatry.

neuroscience↗

bioRxiv2020

Deep learning based behavioral analysis enables high precision rodent tracking and is capable of outperforming commercial solutions

To study brain function, preclinical research relies heavily on animal monitoring and the subsequent analyses of behavior. Commercial platforms have enabled semi high-throughput behavioral analyses by providing accurate tracking of animals, yet they often struggle with the analysis of ethologically relevant behaviors and lack the flexibility to adapt to variable testing environments. In the last couple of years, substantial advances in deep learning and machine vision have given researchers the ability to take behavioral analysis entirely into their own hands. Here, we directly compare the performance of commercially available platforms (Ethovision XT14, Noldus; TSE Multi Conditioning System, TSE Systems) to cross-verified human annotation. To this end, we provide a set of videos - carefully annotated by several human raters - of three widely used behavioral tests (open field, elevated plus maze, forced swim test). Using these data, we show that by combining deep learning-based motion tracking (DeepLabCut) with simple post-analysis, we can track animals in a range of classic behavioral tests at similar or even greater accuracy than commercial behavioral solutions. In addition, we integrate the tracking data from DeepLabCut with post analysis supervised machine learning approaches. This combination allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, thus outperforming commercial solutions. Moreover, the resulting machine learning approach eliminates variation both within and between human annotators. In summary, our approach helps to improve the quality and accuracy of behavioral data, outperforming commercial systems at a fraction of the cost.

animal behavior and cognition↗

bioRxiv2018

Amygdala neuronal ensembles dynamically encode behavioral states

Internal states, including affective or homeostatic states, are important behavioral motivators. The amygdala is a key brain region involved in the regulation of motivated behaviors, yet how distinct internal states are represented in amygdala circuits is unknown. Here, by imaging somatic neural calcium dynamics in freely moving mice, we identify changes in the relative activity levels of two major, non-overlapping populations of principal neurons in the basal nucleus of the amygdala (BA) that predict switches between exploratory and non-exploratory (defensive, anxiety-like) behavioral states across different environments. Moreover, the amygdala widely broadcasts internal state information via several output pathways to larger brain networks, and sensory responses in BA occur independently of behavioral state encoding. Thus, the brain processes external stimuli and internal states in an orthogonal manner, which may facilitate rapid and flexible selection of appropriate, state-dependent behavioral responses.

neuroscience↗