Biology subjects

Fusi, S.

Publications and source records attributed to Fusi, S..

At least 19 recordsLinked to original sources

bioRxiv2026

High-dimensional hippocampus representation facilitates learning of low-dimensional abstract representation and supports generalization

Semantic knowledge encompasses facts, concepts, and their relationships. Recent work has proposed that some forms of semantic knowledge could be encoded in low-dimensional neural representations, such as the abstract, disentangled representational geometries observed in many cognitive brain areas. Experimental work has shown that hippocampal (HPC) lesions impair the formation of flexible and generalizable semantic knowledge. However, HPC representations are often high-dimensional and sparse. How can these representations contribute to the formation of low-dimensional abstract representation across these cognitive cortical areas? Here, we reconcile these observations using multiregional neural network models. Modeling HPC as a sparse, decorrelating autoencoder, we show that its high-dimensional representations promote the robust and efficient formation of low-dimensional abstract representations in downstream regions such as prefrontal cortex. This effect is most pronounced when the geometry of sensory inputs is misaligned with the geometry of desired outputs, requiring a more complex input-output transformation. Mathematical analysis shows that hippocampal expansion recoding suppresses misleading low-dimensional input structure and reshapes the loss landscape of downstream networks, allowing abstract semantic geometry to emerge reliably. The resulting abstract representations provide an inductive bias for sample-efficient learning of novel semantic-to-action mappings. Finally, we show that consolidating hippocampus-dependent representations into a hippocampus-independent pathway improves out-of-distribution generalization based on input structure. Together, these results suggest a circuit mechanism by which high-dimensional hippocampal representations can support, rather than oppose, the emergence of low-dimensional abstract representations.

neuroscience↗

bioRxiv2026

A new neural code for position: generalizable spatial maps in mouse medial frontal cortex

Navigation requires the brain to encode spatial relationships that support extrapolation from explored to unexplored regions of space. We identified a novel neural code for space in the mouse medial frontal cortex (mFC) that is well suited for this form of spatial generalization. Using calcium imaging of excitatory neurons in mFC, hippocampus (dorsal CA1), and lateroventral orbitofrontal cortex (lvOFC) as mice explored a square arena with landmarks, we found that mFC activity supports extrapolation. Linear regression of the animal's x or y coordinates trained in one region of the arena generalized to a completely different region. This property was observed in mFC but not in dCA1 or lvOFC. A model in which neurons encode the squared distance from the animal to multiple landmarks explains several properties of the geometry of the mFC representation, including extrapolation. The neural map that we observed in the frontal cortex may provide a basis for vector computation in navigation and complex route planning.

neuroscience↗

bioRxiv2026

The geometry of context-dependent biased decisions during learning

Adaptive behavior requires inferring latent context and rapidly adjusting decisions in response to changing environmental contingencies. We investigated how reward context is learned, represented, and updated during decision making. We recorded large populations of neurons in lateral prefrontal cortex while macaque monkeys learned a direction-discrimination task in which reward contingencies alternated unpredictably between favoring leftward and rightward choices. Once trained, monkeys inferred context switches from a single unexpected outcome, immediately adjusting both choice bias and reaction times--hallmarks of model-based inference. Early in learning, however, adaptation unfolded gradually across multiple trials. Neural population analyses revealed that reward context was encoded through systematic shifts in the geometry of neural representations. Accumulated sensory evidence (decision variable) and choice were organized along curvilinear decision manifolds, which were displaced across contexts primarily along the decision-variable axis. This geometry naturally implemented context-dependent biases: a fixed linear readout generated different choice tendencies across contexts without remapping. Longitudinal recordings further showed that, with learning, these representational transitions between manifolds became faster, mirroring the emergence of one-trial behavioral generalization. Recurrent neural networks trained on the same task reproduced both the behavioral signatures and the context-dependent geometric shifts. Together, these findings identify a mechanism by which prefrontal circuits support hierarchical inference: reward context is encoded as structured shifts in representational geometry, enabling rapid generalization and flexible control of decision policies.

neuroscience↗

bioRxiv2026

Neural representations supporting generalization under continual learning

Abstraction and generalization are essential for flexible decision-making in novel situations. Recent work in humans and monkeys has shown how abstract variables are encoded by the representational geometry of neural population activity. However, these observations--which are typically made after learning has converged--demonstrate the product of abstraction, but not the process by which abstract knowledge is learned: how are the inputs from concrete experiences transformed into abstract knowledge, and how do neural circuits perform these operations and relay this knowledge? To address these questions, we developed a factorized model of temporal abstraction that builds on the successor representation. The model disentangles the contributions of different levels of abstract learning--from stimulus-stimulus associations to a generalizable task schema--in the form of a factorized prediction error that relates the change in relational knowledge to a predicted change in representational geometry on each trial. We fit the model to the behavior of human participants performing a context-dependent decision task during fMRI. The model captured the learning dynamics at multiple timescales, including the increasing contribution of generalization as participants transferred abstracted relational knowledge between novel task instances. In fMRI, BOLD activity in hippocampus--where, in past work, abstract knowledge was represented after learning--was increasingly attributed to the acquisition of abstract knowledge based on generalization. A similar temporal pattern was observed in entorhinal cortex, a putative source of low-dimensional structural information, and orbitofrontal cortex (OFC), which may depend on relational knowledge to represent state relationships as a cognitive map that guides choices. Indeed, individual variation in the generalization signal in OFC correlated with behavioral performance on key trials that required relational knowledge. Our findings show how the brain regions previously shown to represent abstract knowledge after learning also support the process of abstraction as it evolves from learning concrete associations to a generalizable schema. Our approach offers a computational framework for disentangling the operations driving abstract learning and probing their neural correlates in the dynamics of representational geometry.

neuroscience↗

bioRxiv2025

Impact of Task Similarity and Training Regimes on Cognitive Transfer and Interference

Learning depends not only on the content of what we learn, but also on how we learn and on how experiences are structured over time. To investigate how task similarity and training regime interact during learning, we trained participants on spatial and conceptual learning tasks that shared either similar or distinct underlying structures, using either interleaved or blocked regimes. Interleaving the two tasks hindered performance when their structures were similar, compared to when they were different. In contrast, blocked training produced the opposite effect: it improved performance and facilitated transfer across similar tasks. This effect, however, emerged only when participants first learned the conceptual task, followed by the spatial task, suggesting an asymmetric interaction between task order and structural similarity. We also replicated our results using a neural network model, providing converging evidence for the computational principles governing the interplay between training regime and structural similarity in multi-task learning.

neuroscience↗

bioRxiv2025

Distinct representations of an anxiogenic environment in different cell types of the ventral hippocampus

In addition to its role in episodic memory and spatial navigation, the hippocampus has also been found to influence mood-related disorders such as anxiety and depression. These seemingly distinct roles are consistent with a functional dissociation between the two anatomical poles of the hippocampus: whereas the dorsal portion of the hippocampus in rodents is necessary for spatial tasks, the ventral portion controls affective behaviors. We have recently found that neurons in the ventral, but not dorsal, CA1 area of mice encode anxiety-related information (i.e. are "anxiety cells") in diverse defensive and exploratory behaviors. Still it is unclear how general threat-related information is computed within the hippocampal circuit. In this work, we have examined how distinct hippocampal subregions and cell types encode anxiety-related information by imaging calcium activity in large populations of genetically-defined neurons in the ventral hippocampus while mice explore the elevated plus maze (EPM), a conflict-based anxiety test. We compared the neural encoding of task-related features within the ventral CA1 (vCA1) and ventral dentate gyrus (vDG) regions in order to examine the emergence of anxiety-related activity through the hippocampal circuit. We found that granule cells (vGCs) of the vDG represented similar valence information to neurons in vCA1 in the form of arm-type specific encoding in the EPM, which suggests that encoding of anxiety-related features is already present at this first stage of hippocampal processing. When compared with ventral granule cells (vGCs), ventral mossy cells (vMCs) underlying the DG had stronger spatial encoding and less valence encoding, suggesting that they may be more functionally connected with the highly spatially sensitive dorsal hippocampus. Together these findings will help to understand the encoding of anxiety-related information in the hippocampus and how it relates to neural circuit defects in mood-related disorders.

neuroscience↗

bioRxiv2025

The effects of task similarity during representation learning in brains and neural networks

The complexity of our environment poses significant challenges for adaptive behavior. Recognizing shared structures across tasks can theoretically improve learning through generalization. However, how such shared representations emerge and influence performance remains poorly understood. Contrary to expectations, our findings revealed that individuals trained on tasks with similar low-dimensional structures performed worse than those trained on dissimilar tasks. Magnetoencephalography revealed correlated neural representations in the samestructure group and anticorrelated ones in the different-structure group. Crucially, practice reduced this performance gap and shifted the neural representations of the tasks in the samestructure group towards anticorrelation, like those in the different-structure group. A neural network model trained on similar tasks replicated these findings: tasks with similar structures require more iterations to orthogonalize their representations. These results highlight a complex interplay between task similarity, neural dynamics, and behavior, challenging traditional assumptions about learning and generalization.

neuroscience↗

bioRxiv2025

Optimal sparsity in autoencoder memory models of the hippocampus

Storing complex correlated memories is significantly more efficient when memories are recoded to obtain compressed representations. Previous work has shown that compression can be implemented in a simple neural circuit, which can be described as a sparse autoencoder. The activity of the encoding units in these models recapitulates the activity of hippocampal neurons recorded in multiple experiments. However, these investigations have assumed that the level of sparsity is fixed and that inputs have the same statistics and, hence, that they are uniformly compressible. In contrast, biological agents encounter environments with vastly different memory demands and compressibility. Here, we investigate whether the compressibility of inputs determines optimal sparsity in sparse autoencoders. We find 1) that as the compressibility of inputs increases, the optimal coding level decreases, 2) that the desired coding level diverges from the observed coding level as a function of both memory demand and input compressibility, and 3) that optimal memory capacity is achieved when sparsity is weakly enforced. In addition, we characterize how sparsity and the strength of sparsity enforcement jointly control optimal performance. These results provide predictions for how sparsity in the hippocampus should change in response to environmental statistics and theoretical grounds for why sparsity is dynamically tuned in the brain.

neuroscience↗

bioRxiv2024

Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment

Sensory adaptation dynamically changes neural responses as a function of previous stimuli, profoundly impacting perception. The response changes induced by adaptation have been characterized in detail in individual neurons and at the population level after averaging across trials. However, it is not clear how adaptation modifies the aspects of the representations that relate more directly to the ability to perceive stimuli, such as their geometry and the noise structure in individual trials. To address this question, we recorded from a population of neurons in the mouse visual cortex and presented one stimulus (an oriented grating) more frequently than the others. We then analyzed these data in terms of representational geometry and studied the ability of a linear decoder to discriminate between similar visual stimuli based on the single-trial population responses. Surprisingly, the discriminability of stimuli near the adaptor increased, even though the responses of individual neurons to these stimuli decreased. This improvement arose mainly from how adaptation reshaped the geometry of the mean population responses, with changes in the noise structure playing a minor role. Locomotion modulated the population responses along a direction largely independent of adaptation, allowing the adaptation-induced increase in discriminability to persist during locomotion. Finally, artificial neural networks trained to reconstruct the visual stimulus under metabolic constraints reproduced the increase in discriminability and the decrease in responses. The paradoxical effects of adaptation are thus consistent with the efficient coding framework, allowing the brain to improve the representation of frequent stimuli while limiting the associated metabolic cost.

neuroscience↗

bioRxiv2024

Impaired spatial coding and neuronal hyperactivity in the medial entorhinal cortex of APP NL-G-F mice

The progressive accumulation of amyloid beta (A{beta}) pathology in the brain has been associated with aberrant neuronal network activity and poor cognitive performance in preclinical mouse models of Alzheimers disease (AD). Presently, our understanding of the mechanisms driving pathology-associated neuronal dysfunction and impaired information processing in the brain remains incomplete. Here, we assessed the impact of advanced A{beta} pathology on spatial information processing in the medial entorhinal cortex (MEC) of 18-month App NL-G-F/NL- G-F knock-in (APP KI) mice as they explored contextually novel and familiar open field arenas in a two-day, four-session recording paradigm. We tracked single unit firing activity across all sessions and found that spatial information scores were decreased in MEC neurons from APP KI mice versus those in age-matched C57BL/6J controls. MEC single unit spatial representations were also impacted in APP KI mice. Border cell firing preferences were unstable across sessions and spatial periodicity in putative grid cells was disrupted. In contrast, MEC border cells and grid cells in Control mice were intact and stable across sessions. We then quantified the stability of MEC spatial maps across sessions by utilizing a metric based on the Earth Movers Distance (EMD). We found evidence for increased instability in spatially-tuned APP KI MEC neurons versus Controls when mice were re-exposed to familiar environments and exposed to a novel environment. Additionally, spatial decoding analysis of MEC single units revealed deficits in position and speed coding in APP KI mice in all session comparisons. Finally, MEC single unit analysis revealed a mild hyperactive phenotype in APP KI mice that appeared to be driven by narrow-spiking units (putative interneurons). These findings tie A{beta}-associated dysregulation in neuronal firing to disruptions in spatial information processing that may underlie certain cognitive deficits associated with AD.

neuroscience↗

bioRxiv2024

Rarely categorical, always high-dimensional: how the neural code changes along the cortical hierarchy

A long-standing debate in neuroscience concerns whether individual neurons are organized into functionally distinct populations that encode information differently ("categorical" representations [1-3]) and the implications for neural computation. Here, we systematically analyzed how cortical neurons encode cognitive, sensory, and movement variables across 43 cortical regions during a complex task (14,000+ units from the International Brain Laboratory public Brainwide Map data set [4]) and studied how these properties change across the sensory-cognitive cortical hierarchy [5]. We found that the structure of the neural code was scale-dependent: on a whole-cortex scale, neural selectivity was categorical and organized across regions in a way that reflected their anatomical connectivity. However, within individual regions, categorical representations were rare and limited to primary sensory areas, and neuronal responses were instead very diverse. With theoretical arguments and empirical evidence, we demonstrate that the diversity of neural responses enables high-dimensional representations and, hence, high separability, allowing linear readouts to separate experimental conditions in many arbitrary ways. Indeed, when accounting for information that is actually encoded in each area, all cortical regions exhibit maximal separability. Our results indicate that cortical circuits prioritize diversity over categorical structure, supporting a computational regime geared toward high-dimensional, highly-separable neural representations.

neuroscience↗

bioRxiv2024

Prefrontal representations of retrospective spatial working memory in a rodent radial maze task

Working memory is the cognitive capacity for temporarily holding information in mind for processing or use. It has been theorized to depend upon executive and mnemonic subcomponents, although the contextual mapping of these subcomponents is not complete. Perturbations of prefrontal cortex (PFC) delay activity disrupt spatial working memory performance in rodent tasks. However, recordings of unperturbed PFC delay activity do not consistently contain mnemonic representations of spatial information in these tasks, calling into question the role that mnemonic PFC representations play in freely-moving spatial working memory. We hypothesized that increasing task complexity might increase the likelihood of mnemonic PFC representation emergence. We therefore used an automated eight-arm radial maze to implement a novel match-to-sample rodent spatial working memory task with seven options on each trial, and recorded calcium activity in PFC neurons during task performance. We found that the delay-phase activity of PFC neurons indeed contained mnemonic representations of spatial information at the population level. These representations were retrospective rather than prospective, and--surprisingly--were more evident on error trials. Together with previous results, these observations suggest that in freely-moving spatial working memory tasks, PFC mnemonic representations emerge to empower deviation from a routine behavioral strategy. Significance StatementPrefrontal cortex (PFC) activity is necessary for optimal performance of freely-moving spatial working memory tasks in rodents. Despite this, PFC representations of retrospective actions or stimuli--one quintessential working memory hallmark--are only variably observed during task delays, complicating our understanding of the PFCs role in spatial working memory. Here, we examine cellular-resolution PFC activity in a high-optionality match-to-sample radial maze task and find retrospective delay representations. Strikingly, these delay representations are more evident in error trials. This suggests that in the freely-moving context, explicit PFC representations of retrospective information support deviations from an entrained behavioral strategy, rather than equally supporting all spatial working memory-based behavior.

animal behavior and cognition↗

bioRxiv2024

Modular representations emerge in neural networks trained to perform context-dependent tasks

Experimental and theoretical work has argued both for and against the existence of specialized sub-populations of neurons (modules) within single brain regions. By studying artificial neural networks, we show that this local modularity emerges to support complex (for instance, context-dependent) behavior only when the input to the network is low-dimensional. No anatomical constraints are required. We also show when modular specialization emerges implicitly at the population level, where orthogonal subspaces are viewed as distinct modules. Modularity yields abstract representations, allows for rapid learning and generalization on novel output domains as well as related tasks. Non-modular representations facilitate the rapid learning of unrelated tasks. Our findings reconcile conflicting experimental results and make predictions for future experiments.

neuroscience↗

bioRxiv2024

Purkinje cells in Crus I and II encode the visual stimulus and the impending choice as monkeys learn a reinforcement based visuomotor association task

Visuomotor association involves linking an arbitrary visual cue to a well-learned movement. Transient inactivation of Crus I/II impairs primates ability to learn new associations and delays motor responses without affecting the kinematics of the movement. The simple spikes of Purkinje cells in the Crus regions signal cognitive errors as monkeys learn to associate specific fractal stimuli with movements of the left or right hand. Here we show that as learning progresses, the simple spike activity of individual neurons becomes more selective for stimulus-response associations, with selectivity developing closer to the appearance of visual stimuli. Initially, most neurons respond to both associations, irrespective of the identity of the stimulus and the associated movement, but as learning advances, more neurons distinguish between specific stimulus-hand associations. Using a linear decoder, it was found that in early learning stages, the visual stimulus can be decoded only when the choice can also be decoded. As learning improves, the visual stimulus is decoded earlier than the choice. A simple model can replicate the observed simple spike signals and the monkeys behavior in both the early and late learning stages.

neuroscience↗

bioRxiv2023

Abstract representations emerge in human hippocampal neurons during inference behavior

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior2,3. Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.

neuroscience↗

bioRxiv2023

Neural signatures of stress susceptibility and resilience in the amygdala-hippocampal network

The neural dynamics that underlie divergent anhedonic responses to stress remain unclear. Here, we identified neuronal dynamics in an amygdala-hippocampal circuit that distinguish stress resilience and susceptibility. In a reward-choice task, basolateral amygdala (BLA) activity in resilient mice showed enhanced discrimination of upcoming reward choices. In contrast, a rumination-like signature emerged in the BLA of susceptible mice; a linear decoder could classify the intention to switch or stay on a previously chosen reward. Spontaneous activity in the BLA of susceptible mice was higher dimensional than controls, reflecting the exploration of a larger number of distinct neural states. Manipulation of vCA1-BLA inputs rescued dysfunctional neural dynamics and anhedonia in susceptible mice, suggesting that targeting this pathway can enhance BLA circuit function and ameliorate of depression-related behaviors. One-Sentence SummaryIdentification and rescue of dysfunctional vCA1-BLA population dynamics and behavior in stress-susceptible mice.

neuroscience↗

bioRxiv2023

Ventral CA1 Population Codes for Anxiety

The ventral hippocampus is a critical node in the distributed brain network that controls anxiety. Using miniature microscopy and calcium imaging, we recorded ventral CA1 (vCA1) neurons in freely moving mice as they explored variants of classic behavioral assays for anxiety. Unsupervised behavioral segmentation revealed clusters of behavioral motifs that corresponded to exploratory and vigilance-like states. We discovered multiple vCA1 population codes that represented the anxiogenic features of the environment, such as bright light and openness, as well as the moment-to-moment anxiety state of the animals. These population codes possessed distinct generalization properties: neural representations of anxiogenic features were different for open field and elevated plus/zero maze tasks, while neural representations of moment-to-moment anxiety state were similar across both experimental contexts. Our results suggest that anxiety is not tied to the aversive compartments of these mazes but is rather defined by a behavioral state and its corresponding population code that generalizes across environments.

neuroscience↗

bioRxiv2023

The representational geometry of emotional states in basolateral amygdala

Sensory stimuli associated with aversive outcomes cause multiple behavioral responses related to an animals evolving emotional state, but neural mechanisms underlying these processes remain unclear. Here aversive stimuli were presented to mice, eliciting two responses reflecting fear and flight to safety: tremble and ingress into a virtual burrow. Inactivation of basolateral amygdala (BLA) eliminated differential responses to aversive and neutral stimuli without eliminating responses themselves, suggesting BLA signals valence, not motor commands. However, two-photon imaging revealed that neurons typically exhibited mixed selectivity for stimulus identity, valence, tremble and/or ingress. Despite heterogeneous selectivity, BLA representational geometry was lower-dimensional when encoding valence, tremble and safety, enabling generalization of emotions across conditions. Further, tremble and valence coding directions were orthogonal, allowing linear readouts to specialize. Thus BLA representational geometry confers two computational properties that identify specialized neural circuits encoding variables describing emotional states: generalization across conditions, and readouts lacking interference from other readouts.

neuroscience↗