research
Publications
InputDSA: Demixing, then comparing recurrent and externally driven dynamics
Huang, A., Ostrow, M., Singh, S. H., Kozachkow, L., Fiete, I., & Rajan, K.*
Data-derived agents reveal dynamical reservoirs in mouse cortex for adaptive behavior
Zhou S., Badman, R., Arlt, C., Rajan, K.*, & Harvey, C.
Active electrosensing and communication in MARL-trained weakly electric fish collectives
Singh, S., Johnson-Yu, S., Lu, Z., Walsman, A., Pedraja, F., Turcu, D., Sharma, P., Saphra, N., Sawtell, N., & Rajan, K.
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.*
Stress history modulates corticotropin-releasing factor neurons to establish resilience
Haynes, S. E., Lacagnina, A., Seo, H. S., Li, F., Yang, X., Afzal, M., Morel, C., Menigoz, A., Rajan, K., Clem, R. L., Juarez, B., Mayberg, H. S., Rainnie, D. G., Young, L. J., & Han, M. H.
Dissecting larval zebrafish hunting using deep reinforcement learning trained RNN agents
Malik, R., Singh, S. H., Johnson-Yu, S., Wu, N., Harpaz, R., Engert, F., Rajan, K.
From memories to maps: Mechanisms of in-context reinforcement learning in transformers
Fang, C., & Rajan, K.
A variational manifold embedding framework for nonlinear dimensionality reduction
Vastola, J., Gershman, S. J., & Rajan, K.
POCO: Scalable neural forecasting through population conditioning
Duan, Y., Chaudhry, H.T., Ahrens, M.B., Harvey, C.D., Perich, M.G., Deisseroth, K., & Rajan, K.
Gradient descent as loss landscape navigation: A normative framework for deriving learning rules
Vastola, J., Gershman, S. J., & Rajan, K.
Military AI needs technically-informed regulation to safeguard AI research and its applications
Simmons-Edler, R., Dong, J., Lushenko, P., Rajan, K., & Badman, R.P.
Deep RL needs deep behavior analysis: Exploring implicit planning by model-free agents in open-ended environments
Simmons-Edler, R., Badman, R.P., Berg, F.B., Chua, R., Vastola, J.J., Lunger, J., Qian, W., & Rajan, K.
Measuring and controlling solution degeneracy across task-trained recurrent neural networks
Huang, A., Singh, S. H., & Rajan, K.
Contributions of cortical neuron firing patterns, synaptic connectivity, and plasticity to task performance
Insanally, M.N., Albanna, B.F., Toth, J., DePasquale, B., Fadaei, S.S., Gupta, T., Lombardi, O., Kuchibhotla, K., Rajan, K., & Froemke, R.C.
The time is ripe to reverse engineer an entire nervous system: Simulating behavior from neural interactions
Haspel, G., Baker, B., Beets, I., Boyden, E. S., Brown, J., Church, G., Cohen, N., Colon-Ramos, D., Dyer, E., Fang-Yen, C., Flavell, S., Goodman, M. B., Hart, A. C., Izquierdo, E. J., Kagias, K., Lockery, S., Lu, Y., Marblestone, A., Matelsky, J., Mensh, B., Pereira, T. D., Pfister, H., Rajan, K., Rotstein, H. G., Scholz, M., Shaevitz, J. W., Shlizerman, E., Simeon, Q., Skuhersky, M. A., Tiruvadi, V., Venkatachalam, V., Wei, D., Wester, B., Yang, G. R., Yemini, E., Zimmer, M., & Kording, K. P.
Representations of the intrinsic value of information in mouse orbitofrontal cortex
Bussell, J. J., Badman, R. P., Márton, C. D., Bromberg-Martin, E. S., Abbott, L. F., Rajan, K., & Axel, R.
Offline ensemble co-reactivation links memories across days
Zaki, Y., Pennington, Z. T., Morales-Rodriguez, D., Bacon, M. E., Ko, B., Francisco, T. R., LaBanca, A. R., Sompolpong, P., Dong, Z., Lamsifer, S., Chen, H. T., Carrillo Segura, S., Christenson Wick, Z., Silva, A. J., Rajan, K., van der Meer, M., Fenton, A., Shuman, T., & Cai, D. J.
Reflections on simplicity and complexity in computational neuroscience
Rajan, K., Treves, A. & Gaudenzi, R.
AI-powered autonomous weapons risk geopolitical instability and threaten AI research
Simmons-Edler, R., Badman, R.P., Longpre, S. & Rajan, K.
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.
Temporally specific patterns of neural activity in interconnected corticolimbic structures during reward anticipation
Young, M.E., Spencer-Salmon, C., Mosher, C., Tamang, S., Rajan, K., & Rudebeck, P.H.
Network models to enhance the translational impact of cross-species studies
Brynildsen, J.K., Rajan, K., Henderson, M.X., & Bassett, D.S.
The computational brain
Clem, R.L., Cai, D.J., Rajan, K., Shuman, T., Wu, X., & Wu, Z.
A ‘programming’ framework for recurrent neural networks
Beiran, M., Spencer-Salmon, C.A., & Rajan, K.
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.
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.
A brain basis of dynamical intelligence for AI and computational neuroscience
Monaco, J.D., Rajan, K., & Hwang, G.M.
Efficient and robust multi-task learning in the brain with modular latent primitives
Márton, C. D., Gagnon, L., Lajoie, G.^, & Rajan, K.^
Curriculum learning as a tool to uncover learning principles in the brain
Kepple, D.R., Engelken, R.E., & Rajan, K.*
Reservoir-based tracking (TRAKR) for one-shot classification of neural time-series patterns
Afzal, M. Q., Márton, C. D., Mayberg, H.M., Rich, E. L., & Rajan, K.
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.
Linking task structure and neural network dynamics
Márton, C.D., Zhou, S., & Rajan, K.
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.*
The Learning Salon: Toward a new participatory science
Momennejad, I., Krakauer, J.W., Sun, C., Yezerets, E., Rajan, K., Vogelstein, J.T., & Wyble, B.
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.
Rethinking brain-wide interactions through multi-region ‘network of networks’ models
Perich, M.G, & Rajan, K.
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.
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.
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.
How to study the neural mechanisms of multiple tasks
Yang, G.R., Cole, M.W., & Rajan, K.
Full-FORCE: A least-squares algorithm for training recurrently connected neural networks
DePasquale, 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 intrinsic variability 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 chaos in recurrent neural networks
Rajan, K., Abbott, L.F., & Sompolinsky, H.
Interactions between intrinsic and stimulus dependent activity in recurrent neural networks
Rajan, K., Abbott, L.F., & Sompolinsky, H.
Nonchaotic responses from randomly connected networks of model neurons
Temperature compensation of chemical reactions
Rajan, K., & Abbott, L.F.
Eigenvalue spectra of random matrices for neural networks
Rajan, K., & Abbott, L.F.
Neural network dynamics
Vogels, T.P., Rajan, K., & Abbott, L.F.
%20(1)%20(1).png){kind=logo}