Biology subjects

Pezzotta, A.

Publications and source records attributed to Pezzotta, A..

6 recordsLinked to original sources

bioRxiv2026

From sequences to schemas: low-rank recurrent dynamics underlie abstract relational representations

A hallmark of intelligent behavior is the ability to extract abstract relational structure from temporal sequences -- recognizing, for instance, that aab, ccd, and eef all follow the same underlying pattern, regardless of the specific elements involved. This capacity, observed across species and sensory modalities, is thought to underlie the formation of cognitive schemas: compressed internal models that support rapid generalization to novel experiences. Yet the neural circuit mechanisms by which such abstract, identity-independent representations emerge from sequential experience remain largely unknown. Here, we investigate this question using Recurrent Neural Networks (RNNs) as mechanistic models of neural circuits, trained to classify sequences based on their latent algebraic patterns (e.g., aab, aad [->] AAB; aba, aca [->] ABA) without supervision on intermediate transitions. We demonstrate that RNNs spontaneously learn low-dimensional representations that mirror the hierarchical generative structure of the sequences, and that this abstraction is mechanistically supported by the emergence of low-rank recurrent connectivity. The leading singular component of the recurrent weight matrix integrates relational transition information -- whether consecutive tokens are the same or different -- across time, driving the formation of a structured, tree-like geometry in the population state space. Through singular vector ablation, we establish a causal role for this component: removing it selectively erases memory for earlier transitions while leaving local, single-step sensitivity intact. Finally, while RNNs trained on next-token prediction do not spontaneously acquire these abstract representations, transferring the low-rank scaffold learned from classification significantly accelerates learning and improves generalization -- an effect specific to the abstract structure of the scaffold rather than generic statistical pretraining. These findings offer a computational account of how task demands shape recurrent connectivity to support temporal abstraction, with direct implications for understanding schema formation in biological brains.

neuroscience↗

bioRxiv2025

Predictable Engineering of Signal-DependentCis-Regulatory Elements

Cis-regulatory elements (CREs) control how genes respond to external signals, but the principles governing their structure and function remain poorly understood. While differential transcription factor binding is known to regulate gene expression, how CREs integrate the amount and combination of inputs to secure precise spatiotemporal profiles of gene expression remains unclear. Here, we developed a high-throughput combinatorial screening strategy, that we term NeMECiS, to investigate signal- dependent synthetic CREs (synCREs) in differentiating mammalian stem cells. By concatenating fragments of functional CREs from genes that respond to Sonic Hedgehog in the developing vertebrate neural tube, we found that CRE activity follows hierarchical design rules. While individual 200-base-pair fragments showed minimal activity, their combinations generated thousands of functional signal-responsive synCREs, many exceeding the activity of natural sequences. Statistical modelling revealed CRE function can be decomposed into specific quantitative contributions in which sequence fragments combine through a multiplicative rule, tuned by their relative positioning and spacing. These findings provide a predictive framework for CRE redesign, which we used to engineer synthetic CREs that alter the pattern of motor neuron differentiation in neural tissue. These findings establish quantitative principles for engineering synthetic regulatory elements with programmable signal responses to rewire genetic circuits and control stem cell differentiation, providing a basis for understanding developmental gene regulation and designing therapeutic gene expression systems.

developmental biology↗

bioRxiv2024

Tumor-associated macrophages enhance tumor innervation and spinal cord repair

AO_SCPLOWBSTRACTC_SCPLOWTumor-associated macrophages (TAM) enhance cancer progression by promoting angiogenesis, extracellular matrix (ECM) remodeling, and immune suppression. Nerve infiltration is a hallmark of various cancers and is known to directly contribute to tumor growth. However, the role of TAM in promoting intratumoral nerve growth remains poorly understood. In this study, we demonstrate that TAM expressed a distinct "neural growth" gene signature. TAM actively enhance neural growth within tumors and directly promote neurites outgrowth. We identify secreted phosphoprotein 1 (Spp1) as a key mediator of TAM-driven neural growth activity, which triggers neuronal mTORC2 signaling. Leveraging this new neural growth function, which added to the TAM wound healing properties, we explored TAM potential to repair central nervous system. Adoptive transfer of in vitro-generated TAM in a severe complete-compressive-contusive spinal cord injury (scSCI) model, not only repaired the damaged neural parenchyma by improving tissue oxygenation, ECM remodeling, and dampening chronic inflammation, but also resulted in neural regrowth and partial functional motor recovery. Proteomic analysis and subsequent functional validation confirmed that TAM-induced spinal cord regeneration is mediated through the activation of neural mTORC2 signaling pathways. Collectively, our data unveil a previously unrecognized role of TAM in tumor innervation, neural growth, and neural tissue repair.

neuroscience↗

bioRxiv2022

Optimal control of gene regulatory networks for morphogen-driven tissue patterning

The organised generation of functionally distinct cell types in developing tissues depends on establishing spatial patterns of gene expression. In many cases, this is directed by spatially graded chemical signals - known as morphogens. In the influential "French Flag Model", morphogen concentration is proposed to instruct cells to acquire their specific fate. However, this mechanism has been questioned. It is unclear how it produces timely and organised cell-fate decisions, despite the presence of changing morphogen levels, molecular noise and individual variability. Moreover, feedback is present at various levels in developing tissues introducing dynamics to the process that break the link between morphogen concentration, signaling activity and position. Here we develop an alternative approach using optimal control theory to tackle the problem of morphogen-driven patterning. In this framework, intracellular signalling is derived as the control strategy that guides cells to the correct fate while minimizing a combination of signalling levels and the time taken. Applying this approach demonstrates its utility and recovers key properties of the patterning strategies that are found in experimental data. Together, the analysis offers insight into the design principles that produce timely, precise and reproducible morphogen patterning and it provides an alternative framework to the French Flag paradigm for investigating and explaining the control of tissue patterning.

developmental biology↗

bioRxiv2022

From recency to the central tendency bias in working memory: a unifying attractor network model

The central tendency bias, or contraction bias, is a phenomenon where the judgment of the magnitude of items held in working memory appears to be biased towards the average of past observations. It is assumed to be an optimal strategy by the brain, and commonly thought of as an expression of the brains ability to learn the statistical structure of sensory input. On the other hand, recency biases such as serial dependence are also commonly observed, and are thought to reflect the content of working memory. Recent results from an auditory delayed comparison task in rats, suggest that both biases may be more related than previously thought: when the posterior parietal cortex (PPC) was silenced, both short-term and contraction biases were reduced. By proposing a model of the circuit that may be involved in generating the behavior, we show that a volatile working memory content susceptible to shifting to the past sensory experience - producing short-term sensory history biases - naturally leads to contraction bias. The errors, occurring at the level of individual trials, are sampled from the full distribution of the stimuli, and are not due to a gradual shift of the memory towards the sensory distributions mean. Our results are consistent with a broad set of behavioral findings and provide predictions of performance across different stimulus distributions and timings, delay intervals, as well as neuronal dynamics in putative working memory areas. Finally, we validate our model by performing a set of human psychophysics experiments of an auditory parametric working memory task.

neuroscience↗

bioRxiv2020

Free recall scaling laws and short-term memory effects in a latching attractor network

Despite the complexity of human memory, paradigms like free recall have revealed robust qualitative and quantitative characteristics, such as power laws governing recall capacity. Although abstract random matrix models could explain such laws, the possibility of their implementation in large networks of interacting neurons has so far remained unexplored. We study an attractor network model of long-term memory endowed with firing rate adaptation and global inhibition. Under appropriate conditions, the transitioning behaviour of the network from memory to memory is constrained by limit cycles that prevent the network from recalling all memories, with scaling similar to what has been found in experiments. When the model is supplemented with a heteroassociative learning rule, complementing the standard autoassociative learning rule, as well as short-term synaptic facilitation, our model reproduces other key findings in the free recall literature, namely serial position effects, contiguity and forward asymmetry effects, as well as the semantic effects found to guide memory recall. The model is consistent with a broad series of manipulations aimed at gaining a better understanding of the variables that affect recall, such as the role of rehearsal, presentation rates and (continuous/end-of-list) distractor conditions. We predict that recall capacity may be increased with the addition of small amounts of noise, for example in the form of weak random stimuli during recall. Moreover, we predict that although the statistics of the encoded memories has a strong effect on the recall capacity, the power laws governing recall capacity may still be expected to hold.

neuroscience↗