Papers

Collection of Papers

From point neurons to recurrent networks

• Mastrogiuseppe, F., & Ostojic, S. (2018). Linking connectivity, dynamics, and computations in low-rank recurrent neural networks. Neuron, 99(3), 609-623.
• Tzilivaki, A., Kastellakis, G., & Poirazi, P. (2019). Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators. Nature communications, 10(1), 1-14.
• Frankle, J., & Carbin, M. (2018). The lottery ticket hypothesis: Finding sparse, trainable neural networks. arXiv preprint arXiv:1803.03635.
• Guerguiev, J., Kording, K. P., & Richards, B. A. (2019). Spike-based causal inference for weight alignment. arXiv preprint arXiv:1910.01689.
• Chalk, M., Tkačik, G., & Marre, O. (2019). Inferring the function performed by a recurrent neural network. bioRxiv, 598086.
• Sederberg, A. J., & Nemenman, I. (2019). Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons. arXiv preprint arXiv:1909.10116.

Cortical Dynamics

• Rubin, D. B., Van Hooser, S. D., & Miller, K. D. (2015). The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex. Neuron, 85(2), 402-417.
• Pattadkal, J. J., Mato, G., van Vreeswijk, C., Priebe, N. J., & Hansel, D. (2018). Emergent orientation selectivity from random networks in mouse visual cortex. Cell reports, 24(8), 2042-2050.
• Murphy, B. K., & Miller, K. D. (2009). Balanced amplification: a new mechanism of selective amplification of neural activity patterns. Neuron, 61(4), 635-648.

Emergence of Orientation Selectivity, Development

• Ben-Yishai, R., Bar-Or, R. L., & Sompolinsky, H. (1995). Theory of orientation tuning in visual cortex. Proceedings of the National Academy of Sciences, 92(9), 3844-3848.
• Von der Malsburg, C. (1973). Self-organization of orientation sensitive cells in the striate cortex. Kybernetik, 14(2), 85-100.
• Finn, I. M., Priebe, N. J., & Ferster, D. (2007). The emergence of contrast-invariant orientation tuning in simple cells of cat visual cortex. Neuron, 54(1), 137-152.
• Chariker, L., Shapley, R., & Young, L. S. (2016). Orientation selectivity from very sparse LGN inputs in a comprehensive model of macaque V1 cortex. Journal of Neuroscience, 36(49), 12368-12384.
• Stevens, J. L. R., Law, J. S., Antolík, J., & Bednar, J. A. (2013). Mechanisms for stable, robust, and adaptive development of orientation maps in the primary visual cortex. Journal of Neuroscience, 33(40), 15747-15766.
• Miconi, T., McKinstry, J. L., & Edelman, G. M. (2016). Spontaneous emergence of fast attractor dynamics in a model of developing primary visual cortex. Nature communications, 7, 13208.

Manifolds 

Methods:

• Elsayed, G. F., & Cunningham, J. P. (2017). Structure in neural population recordings: an expected byproduct of simpler phenomena?. Nature neuroscience, 20(9), 1310.
• Tenenbaum, J. B., De Silva, V., & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. science, 290(5500), 2319-2323.
• Williams, A. H., Kim, T. H., Wang, F., Vyas, S., Ryu, S. I., Shenoy, K. V., ... & Ganguli, S. (2018). Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor component analysis. Neuron, 98(6), 1099-1115.
• Pandarinath, C., O’Shea, D. J., Collins, J., Jozefowicz, R., Stavisky
• . D., Kao, J. C., ... & Henderson, J. M. (2018). Inferring single-trial neural population dynamics using sequential auto-encoders. Nature methods, 1.
• Pandarinath, C., Ames, K. C., Russo, A. A., Farshchian, A., Miller, L. E., Dyer, E. L., & Kao, J. C. (2018). Latent factors and dynamics in motor cortex and their application to brain–machine interfaces. Journal of Neuroscience, 38(44), 9390-9401.

Neural computation:

• Gallego, J. A., Perich, M. G., Naufel, S. N., Ethier, C., Solla, S. A., & Miller, L. E. (2018). Cortical population activity within a preserved neural manifold underlies multiple motor behaviors. Nature communications, 9(1), 4233.
• Gallego, J. A., Perich, M. G., Chowdhury, R. H., Solla, S. A., & Miller, L. E. (2018). A stable, long-term cortical signature underlying consistent behavior. BioRxiv, 447441.
• Chaudhuri, R., Gercek, B., Pandey, B., Peyrache, A., & Fiete, I. (2019). The intrinsic attractor manifold and population dynamics of a canonical cognitive circuit across waking and sleep. Nature neuroscience, 22(9), 1512-1520.

Behaviour:

• Berman, G. J., Bialek, W., & Shaevitz, J. W. (2016). Predictability and hierarchy in Drosophila behavior. Proceedings of the National Academy of Sciences, 113(42), 11943-11948.

Visual System and Perception

• Frégnac, Y., & Bathellier, B. (2015). Cortical correlates of low-level perception: from neural circuits to percepts. Neuron, 88(1), 110-126.
• Bashivan, P., Kar, K., & DiCarlo, J. J. (2019). Neural population control via deep image synthesis. Science, 364(6439), eaav9436.
• Ponce, C. R., Xiao, W., Schade, P. F., Hartmann, T. S., Kreiman, G., & Livingstone, M. S. (2019). Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences. Cell, 177(4), 999-1009.
• Marshel, J. H., Kim, Y. S., Machado, T. A., Quirin, S., Benson, B., Kadmon, J., ... & Shane, J. C. (2019). Cortical layer–specific critical dynamics triggering perception. Science, 365(6453), eaaw5202.
• Hénaff, O. J., Goris, R. L., & Simoncelli, E. P. (2019). Perceptual straightening of natural videos. Nature neuroscience, 22(6), 984.

Brainwide Activity and Neural Variability

• Allen, W. E., Chen, M. Z., Pichamoorthy, N., Tien, R. H., Pachitariu, M., Luo, L., & Deisseroth, K. (2019). Thirst regulates motivated behavior through modulation of brainwide neural population dynamics. Science, 364(6437), 253-253.
• Gründemann, J., Bitterman, Y., Lu, T., Krabbe, S., Grewe, B. F., Schnitzer, M. J., & Lüthi, A. (2019). Amygdala ensembles encode behavioral states. Science, 364(6437), eaav8736.
• Stringer, C., Pachitariu, M., Steinmetz, N., Reddy, C. B., Carandini, M., & Harris, K. D. (2019). Spontaneous behaviors drive multidimensional, brainwide activity. Science, 364(6437), 255-255.
• Shimaoka, D., Steinmetz, N. A., Harris, K. D., & Carandini, M. (2019). The impact of bilateral ongoing activity on evoked responses in mouse cortex. ELife, 8, e43533.
• Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M., & Harris, K. D. (2019). High-dimensional geometry of population responses in visual cortex. Nature, 1.
• Dechery, J. B., & MacLean, J. N. (2018). Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons. PLoS computational biology, 14(5), e1006153.