Bill Lotter

Projects

Navigation in rodents
Task Dependence of Visual Representations

CBMM Publications

W. Lotter, Kreiman, G., and Cox, D., “A neural network trained for prediction mimics diverse features of biological neurons and perception”, Nature Machine Intelligence, vol. 2, no. 4, pp. 210 - 219, 2020.
W. Lotter, Kreiman, G., and Cox, D., “A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception.”, Nature Machine Learning, 2020.
W. Lotter, Kreiman, G., and Cox, D., “A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception”, arXiv | Cornell University, arXiv:1805.10734v2, 2018.
H. Tang, Schrimpf, M., Lotter, W., Moerman, C., Paredes, A., Caro, J. Ortega, Hardesty, W., Cox, D., and Kreiman, G., “Recurrent computations for visual pattern completion”, Proceedings of the National Academy of Sciences, 2018.
W. Lotter, Kreiman, G., and Cox, D., “Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning”, in ICLR, 2017.
W. Lotter, Kreiman, G., and Cox, D., “Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning”. 2017.
W. Lotter, Kreiman, G., and Cox, D., “PredNet - "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning" [code]”. 2016.
W. Lotter, Kreiman, G., and Cox, D., “Unsupervised Learning of Visual Structure using Predictive Generative Networks”, in International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, 2016.
W. Lotter, Kreiman, G., and Cox, D., “UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS”. 2015.