research
Publications
Behavioral fingerprinting of the naked mole-rat uncovers signatures of eusociality and social touch
Schwark, R., Ogundare, S., Weinreb, C., Sheng, P., Foster, W., Toussaint, A., Chang, P., Tsai, Y., Comfere, M., Arnold, A., Guadagnino, A., McCloskey, D., Schaffer, E., Rajan, K., Datta, S., Abdus-Saboor, I.
AI-powered autonomous weapons risk geopolitical instability and threaten AI research
Simmons-Edler, R., Badman, R.P., Longpre, S. & Rajan, K.
Reflections on simplicity and complexity in computational neuroscience
Rajan, K., Treves, A. & Gaudenzi, R.
Aversive experience drives offline ensemble reactivation to link memories across days
Zaki, Y., Pennington, Z.T., Morales-Rodriguez, D., Francisco, T.R., La Banca, A.R., Dong, Z., Carrillo Segura, S., Silva, A.J., Shuman, T., Fenton, A., Rajan, K., & Cai, D.J.
Amplified cortical neural responses as animals learn to use novel activity patterns
Akitake, B., Douglas, H. M., LaFosse, P. K., Deng, Y., Rajan, K., Histed, M. H.
Representations of information value in mouse orbitofrontal cortex during information seeking
Bussell J.J., Badman R.P., Márton C.D., Bromberg-Martin E.S, Abbott L.F., Rajan K., Axel R.
A ‘programming’ framework for recurrent neural networks
Beiran, M., Spencer-Salmon, C.A., Rajan, K.
The computational brain
Clem, R.L., Cai, D.J., Rajan, K., Shuman, T., Wu, X., & Wu, Z.
The computational brain
Clem R.L., Cai D.J., Rajan K., Shuman T., Wu X., & Wu Z.
Network models to enhance the translational impact of cross-species studies
Brynildsen J.K., Rajan K., Henderson M.X., & Bassett D.S.
Temporally-specific sequences of neural activity across interconnected corticolimbic structures during reward anticipation
Young M.E., Spencer-Salmon C., Mosher C., Tamang S., Rajan K., Rudebeck P.H.
Inferring brain-wide interactions using data-constrained recurrent neural network models
Perich, M.G., Arlt, C., Soares, S., Zhou, S., Beiran, M., Andalman A.S., Benster, T., Young, M.E., Mosher, C.P., Minxha, J., Carter, E., Rutishauser, U., Rudebeck, P.H., Harvey, C.D., Deisseroth, K., Rajan, K.
CRF neurons establish resilience via stress-history dependent BNST modulation
Haynes, S., Lacagnina, A., Seong, H.S., Afzal, M.F., Morel, C., Menigoz, A., Rajan, K., Clem, R.L., Mayberg, H.S., Rannie, D.G., Young, L.J., Han, M.
Linking task structure and neural network dynamics
Márton C.D., Zhou S. & Rajan K.
Temporally-specific sequences of neural activity across interconnected corticolimbic structures during reward anticipation
Young, M.E., Spencer-Salmon, C., Mosher, C., Tamang, S., Rajan, K.*, Rudebeck, P.H.
Minian: An open-source miniscope analysis pipeline
Dong Z., Mau W., Feng Y., Pennington Z.T., Chen L., Zaki Y., Rajan K., Shuman T., Aharoni D., Cai D.J.
TRAKR - A reservoir-based tool for fast and accurate classification of neural time-series patterns
Afzal M. Q., Márton C. D., Mayberg, H.M., Rich E. L., Rajan K.
Contributions and synaptic basis of diverse cortical neuron responses to task performance
Albanna, B.F.*, Insanally, M.N.*, B.F., Toth, J., DePasquale, B., Fadaei, S.S., Gupta, T., Lombardi, O., Kuchibhotla, K., Rajan, K., Froemke, R.C.
Curriculum learning as a tool to uncover learning principles in the brain
Kepple, D.R., Engelken, R.E., Rajan, K.*
Efficient and robust multi-task learning in the brain with modular latent primitives
Márton, C. D., Gagnon L., Lajoie G.^, Rajan K.^
A brain basis of dynamical intelligence for AI and computational neuroscience
Monaco, J.D., Rajan, K., Hwang, G.M.
The Learning Salon: Toward a new participatory science
Momennejad, I., Krakauer, J.W., Sun, C., Yezerets, E., Rajan, K., Vogelstein, J.T., Wyble, B.
Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings
Martini, M.L., Valliani, A.A., Sun, C., Costa, A.B., Zhao, S., Panov, F., Ghatan, S., Rajan, K.*, Oermann E.K.*
Rethinking brain-wide interactions through multi-region ‘network of networks’ models
Perich, M.G, Rajan, K.
Machine learning with feature domains elucidates candidate drivers of hospital readmission following spine surgery in a large single-center patient cohort
Martini, M.L., Neifert, S.N., Oermann, E.K., Gal, J., Rajan, K., Nistal, D.A., Caridi, J.M.
How to study the neural mechanisms of multiple tasks
Yang, G.R., Cole, M.W., Rajan, K.
Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons
Insanally, M.N., Carcea, I., Field, R.E., Rodgers, C.C., DiPasquale, B., Rajan, K., DeWeese, M.R., Albanna, B.F., Froemke, R.C.
Task-dependent changes in the large-scale dynamics and necessity of cortical regions
Pinto, L., Rajan, K., DePasquale, B., Thiberge, S.Y., Tank, D.W., Brody, C.D.
Neuronal dynamics regulating brain and behavioral state transitions
Andalman, A.S., Burns, V.M., Lovett-Barron, M., Broxton, M. Poole, B. Yang, S.J., Grosenick, L., Lerner, T.N., Chen, R., Benster, T., Mourrain, P., Levoy, M., Rajan, K., Deisseroth, K.
Full-FORCE: A least-squares algorithm for training recurrently connected neural networks
DiPasquale, B., Cueva, C., Rajan, K., Escola, G.S., Abbott, L.F.
Recurrent network models of sequence generation and memory
Rajan, K., Harvey, C.D., Tank, D.W.
Maximally informative “stimulus energies” in the analysis of neural responses to natural signals
Rajan, K., Bialek, W.
Learning quadratic receptive fields from neural responses to natural stimuli: Information theoretic and likelihood methods
Rajan, K., Marre, O., Tkacik, G.
Stimulus-dependent suppression of chaos in recurrent neural networks
Rajan, K., Abbott, L.F., Sompolinsky, H.
Inferring stimulus selectivity from the spatial structure of neural network dynamics
Rajan, K., Abbott, L.F., Sompolinsky, H.
Stimulus-dependent suppression of intrinsic variability in recurrent neural networks
Rajan, K., Abbott, L.F., Sompolinsky, H.
Nonchaotic responses from randomly connected networks of model neurons
Interactions between intrinsic and stimulus dependent activity in recurrent neural networks
Rajan, K., Abbott, L.F., Sompolinsky, H.
Eigenvalue spectra of random matrices for neural networks
Rajan, K., Abbott, L.F.
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