Neural Circuits and Algorithms

A complex diagram incorporating representations of neurons.

Our goal is to understand how the brain analyzes large and complex datasets streamed by sensory organs in order to aid efforts at building artificial neural systems and treating mental illness.

We analyze experimental data, assembling connectomes from high-throughput electron microscopy and determining neuronal dynamics from calcium imaging and multi-electrode recordings. In addition, we are developing a novel algorithmic theory of neural computation.

Chklovskii Lab

Projects

Connectomics Read More

Digital illustration of a Hebbian and anti-Hebbian network

Theory Read More

Automatic detection of neurons and neuronal processes contained in the data streams generated by brains of test subjects. Here, the pink and yellow objects are active neurons or neuronal processes detected in the brain of a mouse engaged in a behavioral task

Calcium Imaging Data Analysis Read More

Publications

April 20, 2026

Reproducibility and model-selection stability in connectome-constrained circuit modeling

Christos Karaneen, E. Schomburg, D. Chklovskii

Connectome-constrained neural network models aim to link anatomical connectivity with functional computation by training networks whose architectures reflect biological circuits.…

bioRxiv: 2026.04. 18.717873

March 14, 2026

A Network of Biologically Inspired Rectified Spectral Units (ReSUs) Learns Hierarchical Features Without Error Backpropagation

S. Qin, J. Pughe-Sanford, A. Genkin, Pembe Gizem Ozdil, P. Greengard, A. Sengupta, D. Chklovskii

We introduce a biologically inspired, multilayer neural architecture composed of Rectified Spectral Units (ReSUs). Each ReSU projects a recent window…

Proceedings of the AAAI Conference on Artificial Intelligence

January 21, 2026

Neurons as Detectors of Coherent Sets in Sensory Dynamics

J. Pughe-Sanford, X. Ding, J. Moore, A. Sengupta, C. Epstein, P. Greengard, D. Chklovskii

We model sensory streams as observations from high-dimensional stochastic dynamical systems and conceptualize sensory neurons as self-supervised learners of compact…

The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025)

News & Announcements

Biological Brains Inspire a New Building Block for Artificial Neural Networks

An artist's illustration of a digital hand and a human hand each drawing the other.

New Computational Model of Real Neurons Could Lead to Better AI Rachel Tompa

Researchers Create First-Ever Map of a Single Animal’s Early Visual System By Thomas Sumner

Software

CaImAn Python

Computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.

CaImAn-MATLAB

Complete Matlab pipeline for large scale calcium imaging data analysis.

Group Members

Dmitri ‘Mitya’ Chklovskii, Ph.D.

Group Leader, Neural Circuits and Algorithms, CCN

The goal of Mitya Chklovskii’s research is to reverse engineer the brain on the algorithmic level. Informed by anatomical and physiological neuroscience data, his group develops algorithms that model brain computation and solve machine learning tasks.

Agnish Kumar Behera, Ph.D.

Flatiron Research Fellow, CCN

Agnish is a Flatiron Research Fellow in the Center for Computational Neuroscience. He earned his Ph.D. at the University of Chicago.

Nourah Boujaber-Diederichs

Associate Research Analyst, Neural Circuits and Algorithms, CCN

Nourah Boujaber-Diederichs joined the foundation in January 2025 as an Associate Research Analyst.

Xuehao Ding, Ph.D.

Flatiron Research Fellow, Neural Circuits and Algorithms, CCN

Xuehao Ding joined the foundation in April 2025 as a Flatiron Research Fellow.

Amy Hong

Research Analyst, Neural Circuits and Algorithms, CCN

Amy is an Associate Research Analyst at CCN.

Philip Kidd, Ph.D.

Flatiron Research Fellow, Neural Circuits and Algorithms, CCN

Phil is a Flatiron Research Fellow in the Neural Circuits and Algorithms group at the Center for Computational Neuroscience (CCN).

Erik Schomburg, Ph.D.

Research Scientist, Neural Circuits and Algorithms, CCN

Erik Schomburg joined the Flatiron Institute as a Research Scientist to develop and maintain software for the Center for Computational Neuroscience.

Ruo-Yu Shang, Ph.D.

Flatiron Research Fellow, Neural Circuits and Algorithms

Ruo-Yu is a Flatiron Research Fellow in the Center for Computational Neuroscience (CCN).

Aya Shinomiya

Research Analyst, Neural Circuits and Algorithms, CCN

Aya Shinomiya joined the Flatiron institute as a Research Analyst in August 2021.

Kazunori Shinomiya, Ph.D.

Research Scientist, Neural Circuits and Algorithms, CCN

Kazunori Shinomiya is a Research Scientist in the Neural Circuits and Algorithms group working on the wasp brain connectome project.