Biology subjects

Martiniani, S.

Publications and source records attributed to Martiniani, S..

5 recordsLinked to original sources

bioRxiv2025

Hierarchical Neural Circuit Theory of Normalization and Inter-areal Communication

The primate brain exhibits a hierarchical, modular architecture with conserved microcircuits executing canonical computations across reciprocally connected cortical areas. We present a hierarchical neural circuit theory with feedback connections that dynamically implements divisive normalization across its hierarchy. In a two-stage instantiation (V1 {leftrightarrow} V2), increasing feedback from V2 to V1 amplifies responses in both areas. We analytically derive power spectra (V1) and coherence spectra (V1-V2) as functions of frequency (f), and compare them with experimental observations: peaks in both spectra shift to higher frequencies with increased stimulus contrast, and power decays as 1/f4 at high frequencies. The closed-form spectra are validated against direct stochastic simulation of the full nonlinear circuit across contrasts. The theory further predicts distinctive spectral signatures of feedback and input gain modulation. Crucially, the theory offers a unified view of inter-areal communication, defined as the ability to linearly predict neural activity in one brain area from neural activity in another, with emergent features consistent with empirical observations of both communication subspaces and inter-areal coherence. It admits a low-dimensional communication subspace, where inter-areal communication is lower-dimensional than within-area communication. It further predicts that: i) increasing feedback strength enhances inter-areal communication and diminishes within-area communication, without altering the subspace dimensionality; ii) communication is strongest, and the subspace dimensionality lowest, at the frequencies at which V1-V2 coherence peaks; iii) normalization reduces the subspace dimensionality. Finally, a three-area (V1 {leftrightarrow} V4 and V1 {leftrightarrow} V5) instantiation of the theory demonstrates that differential feedback from higher to lower cortical areas dictates their dynamic functional connectivity. Altogether, our theory provides a robust and analytically tractable framework for generating experimentally-testable predictions about normalization, inter-areal communication, and functional connectivity.

neuroscience↗

bioRxiv2025

Stabilization of recurrent neural networks through divisive normalization

Stability is a fundamental requirement for both biological and engineered neural circuits, yet it is surprisingly difficult to guarantee in the presence of recurrent interactions. Standard linear dynamical models of recurrent networks are sensitive to the precise values of the synaptic weights, since stability requires all eigenvalues of the recurrent matrix to lie within the unit circle. Here we demonstrate, both theoretically and numerically, that an arbitrary recurrent neural network can remain stable even when its spectral radius exceeds 1, provided it incorporates divisive normalization, a dynamical neural operation that suppresses the responses of individual neurons. Sufficiently strong recurrent weights lead to instability, but the approach to the unstable phase is preceded by a regime of critical slowing down, a well-known early warning signal for loss of stability. Remarkably, the onset of critical slowing down coincides with the breakdown of normalization, which we predict analytically as a function of the synaptic strength and the magnitude of the external input. Our findings suggest that the widespread implementation of normalization across neural systems may derive not only from its computational role, but also to enhance dynamical stability. I. SIGNIFICANCE STATEMENTNeural circuits must remain stable to function correctly, yet strong recurrent connections, which are essential for complex computations, often hinder this stability. We demonstrate that divisive normalization, a canonical neural operation found across many sensory systems, can actively stabilize neural networks. By analyzing a biologically plausible model (ORGaNICs), we find that normalization allows circuits to remain stable even when recurrent interactions are strong enough to otherwise cause exploding neural dynamics. Furthermore, we identify a theoretical link between the breakdown of normalization and critical slowing down, a state where the brain recovers slowly from perturbations. This suggests that loss of normalization may serve as an early warning signal for the onset of pathological instabilities, such as seizures.

neuroscience↗

bioRxiv2025

Persistently increased expression of PKMzeta and unbiased gene expression profiles identify hippocampal molecular traces of a long-term active place avoidance memory and 'shadow' proteins

Long-term memory formation transiently activates Ca2+-calmodulin kinase II and atypical protein kinase C isoform iota/lambda, whereas persistent activation of the other atypical PKC, protein kinase M zeta (PKM{zeta}), together with its interacting partner, the scaffolding-protein KIBRA (Wwc1), are necessary for maintaining potentiated synapses and memory. Here, we use immediate-early gene (IEG) Arc activation during active place avoidance memory expression to tag memory-activated neurons with EYFP-ChR2. PKM{zeta} immunohistochemistry identified which hippocampal synaptic pathways are persistently altered. EYFP-PKM{zeta} colocalization persistently increases in the hippocampal trisynaptic pathway (dentate gyrus [DG][->]CA3[->]CA1) tracing a one-month-old PKM{zeta} engram. DG, CA3, and CA1 transcriptional profiling identifies that memory persistence correlates with upregulated immediate-early-genes Arc, Fos, and NPas4 in DG, but not with PKM{zeta} or most genes known to be crucial for LTP and memory. This rules out strong memory-related transcriptional regulation, but not regulation of mRNA translation or altered stability of "shadow proteins" like PKM{zeta} that, despite being crucial for memory maintenance, evade detection by unbiased transcriptome profiling. In contrast, our method Correlation Signal Co-cluster Reduction (C-SCoRe) incorporates weak linear and non-linear gene correlations and highlights network interaction changes predicting memory, and related IEG and Prkcz/Wwc1 expression. Manifold transcriptional relationships can reveal shadow molecular components of long-term memory.

neuroscience↗

bioRxiv2022

Predicting and Interpreting Protein Developability via Transfer of Convolutional Sequence Representation

Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often, poor protein developability - quantified by expression, solubility, and stability - hinders utility. The ability to predict protein developability from amino acid sequence would reduce the experimental burden when selecting candidates. Recent advances in screening technologies enabled a high-throughput developability dataset for 105 of 1020 possible variants of protein ligand scaffold Gp2. In this work, we evaluate the ability of neural networks to learn a developability representation from a high-throughput dataset and transfer this knowledge to predict recombinant expression beyond observed sequences. The model convolves learned amino acid properties to predict expression levels 44% closer to the experimental variance compared to a non-embedded control. Analysis of learned amino acid embeddings highlights the uniqueness of cysteine, the importance of hydrophobicity and charge, and the unimportance of aromaticity, when aiming to improve the developability of small proteins. We identify clusters of similar sequences with increased developability through nonlinear dimensionality reduction and we explore the inferred developability landscape via nested sampling. The analysis enables the first direct visualization of the fitness landscape and highlights the existence of evolutionary bottlenecks in sequence space giving rise to competing subpopulations of sequences with different developability. The work advances applied protein engineering efforts by predicting and interpreting protein scaffold developability from a limited dataset. Furthermore, our statistical mechanical treatment of the problem advances foundational efforts to characterize the structure of the protein fitness landscape and the amino acid characteristics that influence protein developability. Significance statementProtein developability prediction and understanding constitutes a critical limiting step in biologic discovery and engineering due to limited experimental throughput. We demonstrate the ability of a machine learning model to learn sequence-developability relationships first through the use of high-throughput assay data, followed by the transfer of the learned developability representation to predict the true metric of interest, recombinant yield in bacterial production. Model performance is 44% better than a model not pre-trained using the high-throughput assays. Analysis of model behavior reveals the importance of cysteine, charge, and hydrophobicity to developability, as well as of an evolutionary bottleneck that greatly limited sequence diversity above 1.3 mg/L yield. Experimental characterization of model predicted candidates confirms the benefit of this transfer learning and in-silico evolution approach.

bioinformatics↗

bioRxiv2020

High-Throughput Developability Assays Enable Library-Scale Identification of Producible Protein Scaffold Variants

Proteins require high developability - quantified by expression, solubility, and stability - for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low-throughput in nature, often slowing the developmental pipeline. We evaluated the ability of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a sitewise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

bioengineering↗