Kanaka Rajan

Kanaka Rajan, PhD

Associate Professor of Neurobiology

NeuroAI: Using Experimental Data and Computational Models to Reveal the Operating Principles of a Biological Brain

We are a computational neuroscience lab where we bring together the fields of brain research and artificial intelligence/machine learning to figure out how the brain works. We use mathematical and computational models based on data collected from neuroscience experiments to design an artificial system that can perform realistic behaviors using only the machinery the biological nervous system has access to (i.e., neurons and synapses operating at a fast timescale). We build such systems and then we ‘reverse engineer’ them to reveal the operating principles of the real brain.

Our work is unified by the question of how slow cognitive processes such as learning, remembering, and deciding are accomplished by the cooperative activity of neurons and synapses working at a much faster time scale. Connecting neural network theory, artificial intelligence/machine learning, and experimental data analysis, we theorize how this gap in time scales is bridged by neural circuits in the brain. Although designed to use only the biophysical properties of synapses and neurons that form the nervous system, the models we build have the power to extend beyond the details of a single experiment, task, or brain region. They reveal unexpected design principles of the real brain, particularly the mechanisms and features that are responsible for producing or mediating many time-varying behaviors.

Our integrative theories and models can transform the way we study the brain, by making specific, quantifiable predictions that lead to new experiments and drive new hypotheses about how the brain works.

Publications View
Network models to enhance the translational impact of cross-species studies.
Authors: Authors: Brynildsen JK, Rajan K, Henderson MX, Bassett DS.
Nat Rev Neurosci
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Aversive experience drives offline ensemble reactivation to link memories across days.
Authors: Authors: Zaki Y, Pennington ZT, Morales-Rodriguez D, Francisco TR, LaBanca AR, Dong Z, Lamsifer S, Segura SC, Chen HT, Christenson Wick Z, Silva AJ, van der Meer M, Shuman T, Fenton A, Rajan K, Cai DJ.
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Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation.
Authors: Authors: Young ME, Spencer-Salmon C, Mosher C, Tamang S, Rajan K, Rudebeck PH.
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Amplified cortical neural responses as animals learn to use novel activity patterns.
Authors: Authors: Akitake B, Douglas HM, LaFosse PK, Beiran M, Deveau CE, O'Rawe J, Li AJ, Ryan LN, Duffy SP, Zhou Z, Deng Y, Rajan K, Histed MH.
Curr Biol
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Minian, an open-source miniscope analysis pipeline.
Authors: Authors: Dong Z, Mau W, Feng Y, Pennington ZT, Chen L, Zaki Y, Rajan K, Shuman T, Aharoni D, Cai DJ.
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Linking task structure and neural network dynamics.
Authors: Authors: Márton CD, Zhou S, Rajan K.
Nat Neurosci
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The Learning Salon: Toward a new participatory science.
Authors: Authors: Momennejad I, Krakauer JW, Sun C, Yezerets E, Rajan K, Vogelstein JT, Wyble B.
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Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings.
Authors: Authors: Martini ML, Valliani AA, Sun C, Costa AB, Zhao S, Panov F, Ghatan S, Rajan K, Oermann EK.
Sci Rep
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Rethinking brain-wide interactions through multi-region 'network of networks' models.
Authors: Authors: Perich MG, Rajan K.
Curr Opin Neurobiol
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Machine Learning With Feature Domains Elucidates Candidate Drivers of Hospital Readmission Following Spine Surgery in a Large Single-Center Patient Cohort.
Authors: Authors: Martini ML, Neifert SN, Oermann EK, Gal J, Rajan K, Nistal DA, Caridi JM.
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