Biology subjects

Sharpee, T.

Publications and source records attributed to Sharpee, T..

7 recordsLinked to original sources

bioRxiv2025

Parsing auditory neural code into maximum-entropy packets

Deciphering the neural code requires identifying its fundamental symbols or code-words. Neural activity is usually interpreted either as a rate code - based on average spike counts - or as a temporal code, which distinguishes patterns with identical counts. Yet, the symbols of the code remain undefined. Here we show that the symbols can be clearly defined by parsing auditory spike trains into variable-duration "packets", within which precise spike timing is irrelevant. Because packets vary in duration, this is not a rate code. A single neuron could encode very different stimuli depending on the number of spikes it produced per packet. The packet-based code enables real-time readout upon packet completion due to its instantaneous code structure and maximizes information capacity at both single-neuron and population levels.

neuroscience↗

bioRxiv2025

Multiscale Hyperbolic Embedding for Cell Hierarchies in Large-Scale Bioinformatics Data

The rapid expansion of biological and computational datasets demands scalable methods that support both visualization and quantitative interpretation. Hyperbolic embeddings are well suited to represent hierarchical structure, but existing approaches are limited by fixed curvature assumptions or poor scalability to large datasets. We introduce MuH-MDS, a multiscale hyperbolic multidimensional scaling algorithm that employs an adi-abatic optimization strategy: local positions are iteratively refined while cluster centroids are temporarily fixed. This strategy accelerates computation by 103 and enables scaling to datasets with over 80,000 samples. Applied to diverse benchmarks, including C. elegans embryogenesis scRNA-seq data, MuH-MDS uncovers intrinsic hierarchical organization and improves both pseudotime inference and lineage reconstruction relative to UMAP and other standard methods. In contrast to UMAP and t-SNE, which prioritize local neighborhoods at the expense of global coherence and metric fidelity, MuH-MDS pre-serves both local detail and global hierarchy, providing a metrically faithful framework for multiscale analysis of complex biological systems.

biophysics↗

bioRxiv2025

The spinal premotor network driving scratching flexor and extensor alternation

Rhythmic motor behaviors are generated by neural networks termed central pattern generators (CPGs). Although locomotor CPGs have been extensively characterized, it remains unknown how the neuronal populations composing them interact to generate adaptive rhythms. We explored the non-linear cooperation dynamics among the three main populations of ipsilaterally projecting spinal CPG neurons - V1, V2a, V2b neurons - in scratch reflex rhythmogenesis. Ablation of all three neuronal subtypes reduced the oscillation frequency. Activation of excitatory V2a neurons enhanced the oscillation frequency, while activating inhibitory V1 neurons caused atonia. These findings required the development of a novel neuromechanical model that consists of flexor and extensor modules coupled via inhibition, in which rhythm in each module is generated by self-bursting excitatory populations and accelerated by intra-module inhibition. Inter-module inhibition coordinates the phases of flexor and extensor activity and slows the oscillations, while facilitation mechanisms in excitatory neurons explain the V2a activation-driven increase in frequency.

neuroscience↗

bioRxiv2022

Quadratic computations maintain neural specificity to natural stimuli across stages of visual processing

Despite recent successes in machine vision, artificial recognition systems continue to be less robust than biological systems. The brittleness of artificial recognition system has been attributed to the linearity of the core operation that matches inputs to target patterns at each stage of the system. Here we analyze responses of neurons from the visual areas V1, V2, and V4 of the brain using the framework that incorporates quadratic computations into multi-stage models. These quadratic computations make it possible to capture local recurrent computation, and in particular, nonlinear suppressive interactions between visual features. We find that incorporating quadratic computation not only strongly improved predictive power of the resulting model, but also revealed several computation motifs that increased the selectivity of neural responses to natural stimuli. These motifs included the organization of excitatory and suppressive features along mutually exclusive hypotheses about incoming stimuli, such as orthogonal orientations or opposing motion directions. The balance between excitatory and suppressive features was largely maintained across brain regions. These results emphasize the importance and properties of quadratic computations that are necessary for achieving robust object recognition.

neuroscience↗

bioRxiv2020

Hyperbolic geometry of gene expression

Understanding the patterns of gene expression is key to elucidating the differences between cell types and across disease conditions. The overwhelmingly large number of genes involved generally makes this problem intractable. Yet, we find that gene expression patterns in five different data datasets can all be described using a small number of variables. These variables describe differences between cells according to a hyperbolic metric. We reach this conclusion by developing methods that, starting with an initial assumption of a Euclidean geometry, can detect the presence of other geometries in the data. The Euclidean metric is used in most of current studies of gene expression, primarily because it is difficult to use other non-linear metrics in high dimensional spaces. The hyperbolic metric is much more suitable for describing data produced by a hierarchically organized network, which is relevant for many biological processes. We find that the hyperbolic effects, but not the space dimensionality, increase with the number of genes that are taken into account. The hyperbolic curvature was the smallest for mouse embryonic stem cells, stronger for mouse kidney, lung and brain cells, and reached the largest value in a set of human cells integrated from multiple sources. We show that taking into account hyperbolic geometry strongly improves the visualization of gene expression data compared to leading visualization methods. These results demonstrate the advantages of knowing the underlying geometry when analyzing high-dimensional data.

bioinformatics↗

bioRxiv2018

Using global t-SNE to preserve inter-cluster data structure

The t-distributed Stochastic Neighbor Embedding (t-SNE) method is one of the leading techniques for data visualization and clustering. This method finds lower dimensional embeddings of data points while minimizing distortions in distances between neighboring data points. By construction, t-SNE discards information about large scale structure of the data. We show that adding a global cost function to the t-SNE cost function makes it possible to cluster the data while preserving global inter-cluster data structure. We test the new \"global t-SNE\" (g-SNE) method on one synthetic and two real data sets on flowers and human brain cells which have significant and meaningful global structures. In all cases, g-SNE outperforms t-SNE in preserving the global structure. The weight parameter {lambda} of the global cost function determines the balance between local and global distances preservations. For the human brain atlas data set, we show the tradeoff of {lambda} in representing global structure of data. Using g-SNE with the optimized {lambda} may therefore yield biological insights into how data is organized on multiple scales. The MATLAB code is available at: https://github.com/gyrheart/gsne

bioinformatics↗

bioRxiv2018

Decoding neural responses with minimal information loss

Cortical tissue has a circuit motif termed the cortical column, which is thought to represent its basic computational unit but whose function remains unclear. Here we propose, and show quantitative evidence, that the cortical column performs computations necessary to decode incoming neural activity with minimal information loss. The cortical decoder achieves higher accuracy compared to simpler decoders found in invertebrate and subcortical circuits by incorporating specific recurrent network dynamics. This recurrent dynamics also makes it possible to choose between alternative stimulus categories. The structure of cortical decoder predicts quadratic dependence of cortex size relative to subcortical parts of the brain. We quantitatively verify this relationship using anatomical data across mammalian species. The results offer a new perspective on the evolution and computational function of cortical columns.

neuroscience↗