Biology subjects

Recanatesi, S.

Publications and source records attributed to Recanatesi, S..

12 recordsLinked to original sources

bioRxiv2026

Reward changes the use and structure of predictive spatial maps

Reward-related changes in hippocampal place-cell activity may reflect either different use of an existing spatial map or plasticity that reshapes the map. We separated these contributions in a two-pathway model: a recurrent network learned to predict sensory input and developed a spatial representation, while a reinforcement-learning controller combined this representation with an explicit goal signal to guide behavior. Replaying identical trajectories before and after learning isolated representational plasticity. Goal-directed behavior strongly concentrated place fields near reward, whereas changes to the representation were smaller and more variable. A reward-proximity teaching signal made these changes larger and more consistent, and the change was strongly attenuated when recurrent plasticity was restricted. After reward relocation, goal-related directional tuning adapted more fully than place-field organization, while withdrawing reward and proximity teaching reversed the learned field concentration. Thus, reward affects predictive spatial maps through separable mechanisms: goal information changes how an existing map is used, whereas reward-related teaching signals reshape the map itself. Our results provide a concrete framework for future experiments to disentangle how reward information, plasticity and behavior jointly shape hippocampal activity.

neuroscience↗

bioRxiv2026

Task Engagement Gates Interareal Communication Geometry in the Mouse Thalamocortical-Midbrain Visual Circuit

Visual processing unfolds across hierarchically organized brain circuits. Existing theories largely explain changes in population geometry through local shifts in gain, firing-rate statistics, or recurrent dynamics, yet do not account for how interareal coordination interacts with local population geometry to constrain downstream population states. We used task engagement, compared to a passive condition, to probe this coordination in Neuropixels recordings spanning the mouse visual thalamocortical-midbrain circuit. Engagement reduced network activity, response participation, and dimensionality across the hierarchy. To account for this circuit-level organization, we developed a theoretical framework in which afferent population geometry interacts with local recurrent dynamics to constrain the accessible dynamics of downstream populations. Across the thalamocortical stages, population-wide afferent statistics predicted downstream activity and dimensionality. At the cortex--midbrain interface, engagement instead reorganized interareal communication geometry. Together, these results identify interareal input geometry as a key constraint on neural population dynamics and uncover a general principle by which behavioral engagement constrains neural state spaces across distributed visual circuits.

neuroscience↗

bioRxiv2024

A non-Hebbian code for episodic memory

Hebbian plasticity has long dominated neurobiological models of memory formation. Yet plasticity rules operating on one-shot episodic memory timescales rarely depend on both pre- and postsynaptic spiking, challenging Hebbian theory in this crucial regime. To address this, we present an episodic memory model governed by a simple non-Hebbian rule depending only on presynaptic activity. We show that this rule, capitalizing on high-dimensional neural activity with restricted transitions, naturally stores episodes as paths through complex state spaces like those underlying a world model. The resulting memory traces, which we term path vectors, are highly expressive and decodable with an odor-tracking algorithm. We show that path vectors are robust alternatives to Hebbian traces when created via spiking and support diverse one-shot sequential and associative recall tasks, and policy learning. Thus, non-Hebbian plasticity is sufficient for flexible memory and learning, and well-suited to encode episodes and policies as paths through a world model.

neuroscience↗

bioRxiv2022

Path vectors: a neural code for sequential memory

While recalling lists of unrelated items is highly challenging we can recall much longer sequences structured as an episode or story. It is unknown why these patterns have such a striking influence on memory. We introduce a model where the experience of an episode or story is represented as a path through a pre-existing network of cognitive states. We demonstrate that by summing the neural representations of the visited states, this path can be transformed into a simple neural code: a path vector. We show how, by leveraging sparse connectivity and high dimensionality, path vectors provide robust codes for a large set of sequences and can be decoded mechanistically for memory retrieval. Fitting our model to data reveals how human free and serial recall may emerge from adapting coding mechanisms tuned for sequences aligned with existing network paths. We thus posit that sequences such as episodes or stories map more directly to existing cognitive network paths than arbitrary lists, with the latter eliciting paths that tend to interfere and impair recall. Our model suggests that mnemonic strategies like imposing narrative structure on a list act to improve recall by reducing such path interference. This work illuminates a simple bioplausible means for flexibly recruiting existing cognitive structures to encode new memories.

neuroscience↗

bioRxiv2020

A scale-dependent measure of system dimensionality

A fundamental problem in science is uncovering the effective number of dynamical degrees of freedom in a complex system, a quantity that depends on the spatio-temporal scale at which the system is observed. Here, we propose a scale-dependent generalization of a classic enumeration of latent variables, the Participation Ratio. We show how this measure relates to conventional quantities such as the Correlation dimension and Principal Component Analysis, and demonstrate its properties in dynamical systems such as the Lorentz attractor. We apply the method to neural population recordings in multiple brain areas and brain states, and demonstrate fundamental differences in the effective dimensionality of neural activity in behaviorally engaged states versus spontaneous activity. Our method applies broadly to multi-variate data across fields of science.

neuroscience↗

bioRxiv2020

Strong coupling and local control of dimensionality across brain areas

The brain contains an astronomical number of neurons, but it is their collective activity that underlies brain function. The number of degrees of freedom that this collective activity explores - its dimensionality - is therefore a fundamental signature of neural dynamics and computation (1-7). However, it is not known what controls this dimensionality in the biological brain - and in particular whether and how recurrent synaptic networks play a role (8-10). Through analysis of high-density Neuropixels recordings (11), we argue that areas across the mouse cortex operate in a sensitive regime that gives these synaptic networks a very strong role in controlling dimensionality. We show that this control is expressed across time, as cortical activity transitions among states with different dimensionalities. Moreover, we show that the control is mediated through highly tractable features of synaptic networks. We then analyze these key features via a massive synaptic physiology dataset (12). Quantifying these features in terms of cell-type specific network motifs, we find that the synaptic patterns that impact dimensionality are prevalent in both mouse and human brains. Thus local circuitry scales up systematically to help control the degrees of freedom that brain networks may explore and exploit.

neuroscience↗

bioRxiv2020

Single circuit in V1 capable of switching contexts during movement using VIP population as a switch

As animals adapt to their environments, their brains are tasked with processing stimuli in different sensory contexts. Whether these computations are context dependent or independent, they are all implemented in the same neural tissue. A crucial question is what neural architectures can respond flexibly to a range of stimulus conditions and switch between them. This is a particular case of flexible architecture that permits multiple related computations within a single circuit. Here, we address this question in the specific case of the visual system circuitry, focusing on context integration, defined as the integration of feedforward and surround information across visual space. We show that a biologically inspired microcircuit with multiple inhibitory cell types can switch between visual processing of the static context and the moving context. In our model, the VIP population acts as the switch and modulates the visual circuit through a disinhibitory motif. Moreover, the VIP population is efficient, requiring only a relatively small number of neurons to switch contexts. This circuit eliminates noise in videos by using appropriate lateral connections for contextual spatio-temporal surround modulation, having superior denoising performance compared to circuits where only one context is learned. Our findings shed light on a minimally complex architecture that is capable of switching between two naturalistic contexts using few switching units. Author SummaryThe brain processes information at all times and much of that information is context-dependent. The visual system presents an important example: processing is ongoing, but the context changes dramatically when an animal is still vs. running. How is context-dependent information processing achieved? We take inspiration from recent neurophysiology studies on the role of distinct cell types in primary visual cortex (V1).We find that relatively few "switching units" -- akin to the VIP neuron type in V1 in that they turn on and off in the running vs. still context and have connections to and from the main population -- is sufficient to drive context dependent image processing. We demonstrate this in a model of feature integration, and in a test of image denoising. The underlying circuit architecture illustrates a concrete computational role for the multiple cell types under increasing study across the brain, and may inspire more flexible neurally inspired computing architectures.

neuroscience↗

bioRxiv2020

Autoencoder networks extract latent variables and encode these variables in their connectomes

Spectacular advances in imaging and data processing techniques are revealing a wealth of information about brain connectomes. This raises an exciting scientific opportunity: to infer the underlying circuit function from the structure of its connectivity. A potential roadblock, however, is that - even with well constrained neural dynamics - there are in principle many different connectomes that could support a given computation. Here, we define a tractable setting in which the problem of inferring circuit function from circuit connectivity can be analyzed in detail: the function of input compression and reconstruction, in an autoencoder network with a single hidden layer. Here, in general there is substantial ambiguity in the weights that can produce the same circuit function, because largely arbitrary changes to "input" weights can be undone by applying the inverse modifications to the "output" weights. However, we use mathematical arguments and simulations to show that adding simple, biologically motivated regularization of connectivity resolves this ambiguity in an interesting way: weights are constrained such that the latent variable structure underlying the inputs can be extracted from the weights by using nonlinear dimensionality reduction methods.

neuroscience↗

bioRxiv2020

Metastable attractors explain the variable timing of stable behavioral action sequences

Natural animal behavior displays rich lexical and temporal dynamics, even in a stable environment. The timing of self-initiated actions shows large variability even when they are executed in reliable, well-learned sequences. To elucidate the neural mechanism underlying this mix of reliability and stochasticity, we trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), known to reflect trial-by-trial action timing fluctuations. Using hidden Markov models, we established a dictionary between ensemble activity patterns and actions. We then showed that metastable attractors, with a reliable sequential structure yet high transition timing variability, could be produced by coupling a high-dimensional recurrent network and a low-dimensional feedforward one. Transitions between attractors in our model were generated by correlated variability arising from the feedback loop between the two networks. This mechanism predicted aligned, low-dimensional noise correlations that were empirically verified in M2 ensembles. Our work establishes a novel framework for investigating the circuit origins of self-initiated behavior based on correlated variability.

neuroscience↗

bioRxiv2019

Dynamic compression and expansion in a classifying recurrent network

Recordings of neural circuits in the brain reveal extraordinary dynamical richness and high variability. At the same time, dimensionality reduction techniques generally uncover low-dimensional structures underlying these dynamics when tasks are performed. In general, it is still an open question what determines the dimensionality of activity in neural circuits, and what the functional role of this dimensionality in task learning is. In this work we probe these issues using a recurrent artificial neural network (RNN) model trained by stochastic gradient descent to discriminate inputs. The RNN family of models has recently shown promise in revealing principles behind brain function. Through simulations and mathematical analysis, we show how the dimensionality of RNN activity depends on the task parameters and evolves over time and over stages of learning. We find that common solutions produced by the network naturally compress dimensionality, while variability-inducing chaos can expand it. We show how chaotic networks balance these two factors to solve the discrimination task with high accuracy and good generalization properties. These findings shed light on mechanisms by which artificial neural networks solve tasks while forming compact representations that may generalize well.

neuroscience↗

bioRxiv2019

Emergence of hierarchical organization in memory for random material

Structured information is easier to remember and recall than random one. In real life, information exhibits multi-level hierarchical organization, such as clauses, sentences, episodes and narratives in language. Here we show that multi-level grouping emerges even when participants perform memory recall experiments with random sets of words. To quantitatively probe brain mechanisms involved in memory structuring, we consider an experimental protocol where participants perform final free recall (FFR) of several random lists of words each of which was first presented and recalled individually. We observe a hierarchy of grouping organizations of FFR, most notably many participants sequentially recalled relatively long chunks of words from each list before recalling words from another list. More-over, participants who exhibited strongest organization during FFR achieved highest levels of performance. Based on these results, we develop a hierarchical model of memory recall that is broadly compatible with our findings. Our study shows how highly controlled memory experiments with random and meaningless material, when combined with simple models, can be used to quantitatively probe the way meaningful information can efficiently be organized and processed in the brain, so to be easily retrieved. Significance StatementInformation that people communicate to each other is highly structured. For example, a story contains meaningful elements of various degrees of complexity (clauses, sentences, episodes etc). Recalling a story, we are chiefly concerned with these meaningful elements and not its exact wording. Here we show that people introduce structure even when recalling random lists of words, by grouping the words into chunks of various sizes. Doing so improves their performance. The so formed chunks closely correspond in size to story elements described above. This suggests that our memory is trained to create a structure that resembles the one it typically deals with in real life, and that using random material like word lists can be used to quantitatively probe these memory mechanisms.

neuroscience↗

bioRxiv2018

Signatures and mechanisms of low-dimensional neural predictive manifolds

Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the tasks low-dimensional latent structure in the network activity - i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.

neuroscience↗