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Divyansha Lachi

Alumni
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Publications

BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings
Alexandre Andre
Shivashriganesh P. Mahato
Vinam Arora
Keshav Balaji
Divyansha Lachi
Nanda H. Krishna
Jingyun Xiao
Yizi Zhang
Ximeng Mao
Wenrui Ma
Han Yu
International Brain Laboratory
Daniel Birman
Niccolò Bonacchi
Gaelle A. Chapuis
Joana A. Catarino
Felicia Davatolhagh
Mayo Faulkner
Laura Freitas-Silva
Fei Hu … (see 22 more)
Julia M. Huntenburg
Anup Khanal
Inês Laranjeira
Petrina Lau
Guido T. Meijer
Nathaniel J. Miska
Jean-Paul Noel
Alejandro Pan-Vazquez
Georg Raiser
Cyrille Rossant
Karolina Z. Socha
Anne E. Urai
Miles J. Wells
Steven J. West
Olivier Winter
Blake Richards
Guillaume Lajoie
Cole Hurwitz
Mehdi Azabou
Matthew R. Whiteway
Liam Paninski
Eva L. Dyer
Advances in large-scale neural recording have made it possible to collect data across many animals and distributed brain regions, raising th… (see more)e question of whether this scale can be exploited to learn general-purpose neural representations transferable across diverse downstream tasks. Yet, progress toward this goal has been limited by fragmented evaluation protocols and a narrow focus on individual task domains. Here, we present BrainWideBench, a benchmark for evaluating across-animal transfer on multi-region neural recordings, built on the International Brain Laboratory Brainwide Map dataset of neural and behavioral recordings spanning 276 brain regions from 139 mice performing a sensory-guided decision-making task. The benchmark is organized around three complementary task suites that evaluate whether learned representations support downstream decoding of behavior, can predict masked or future neural activity, and can recover biologically meaningful anatomical organization. With this benchmark, we systematically evaluate pretraining methods across transfer settings, including finetuning on downstream objectives and zero-shot generalization to unseen animals. Our results confirm pretraining improves performance over matched single-session baselines, but we show current methods exhibit heterogeneity in transfer capabilities: gains depend strongly on the alignment between pretraining objectives and downstream tasks. No single approach performs uniformly well across all three suites, and most methods are designed to only address a subset of them. Together, these findings suggest that learning representations that jointly generalize across behavior, dynamics, and anatomy remains an open challenge. By providing a unified and reproducible evaluation suite, BrainWideBench establishes a framework for measuring progress toward general-purpose models of the mouse brain.
2026-09-17
arXiv (preprint)
doi.org
arxiv.org
Know Thyself by Knowing Others: Learning Neuron Identity from Population Context
Vinam Arora
Divyansha Lachi
Ian J. Knight
Mehdi Azabou
Blake Richards
Cole Hurwitz
Joshua H. Siegle
Eva L. Dyer
Identifying the functional identity of individual neurons is essential for interpreting circuit dynamics, yet it remains a major challenge i… (see more)n large-scale _in vivo_ recordings where anatomical and molecular labels are often unavailable. Here we introduce NuCLR, a self-supervised framework that learns context-aware representations of neuron identity by modeling each neuron's role within the broader population. NuCLR employs a spatio-temporal transformer that captures both within-neuron dynamics and across-neuron interactions. It is trained with a sample-wise contrastive objective that encourages temporally-stable and discriminative embeddings. Across multiple open-access datasets, NuCLR outperforms prior methods in both cell type and brain region classification. Critically, it exhibits strong zero-shot generalization to entirely new populations, without any retraining or access to stimulus labels. Furthermore, we demonstrate that our framework scales effectively with data size. Overall, our results demonstrate that modeling population context is crucial for understanding neuron identity and that rich signal for cell-typing and neuron localization is present in neural activity alone.Code available at: https://github.com/nerdslab/nuclr.
2025-12-02
NEURIPS (Accept (poster))
openreview.net
openreview.net
Stochastic Wiring of Cell Types Enhances Fitness by Generating Phenotypic Variability
Divyansha Lachi
Ann Huang
Augustine N. Mavor-Parker
Arna Ghosh
Blake Richards
Anthony Zador
The development of neural connectivity is a crucial biological process that gives rise to diverse brain circuits and behaviors. Neural devel… (see more)opment is a stochastic process, but this stochasticity is often treated as a nuisance to overcome rather than as a functional advantage. Here we use a computational model, in which connection probabilities between discrete cell types are genetically specified, to investigate the benefits of stochasticity in the development of neural wiring. We show that this model can be viewed as a generalization of a powerful class of artificial neural networks—Bayesian neural networks—where each network parameter is a sample from a distribution. Our results reveal that stochasticity confers a greater benefit in large networks and variable environments, which may explain its role in organisms with larger brains. Surprisingly, we find that the average fitness over a population of agents is higher than a single agent defined by the average connection probability. Our model reveals how developmental stochasticity, by inducing a form of non-heritable phenotypic variability, can increase the probability that at least some individuals will survive in rapidly changing, unpredictable environments. Our results suggest how stochasticity may be an important feature rather than a bug in neural development.
2024-08-07
bioRxiv (preprint)
doi.org