I’m a theoretical physicist by training. I transitioned to work in theoretical cognitive neuroscience as a postdoctoral researcher. My research has been focused on constructing mathematical and network models of learning and memory over multiple timescales. My research interests include predictive algorithms and prediction error based learning algorithms. My overall research efforts are directed towards understanding the neural basis of human cognition and constructing optimal learning algorithms for intelligent systems mimicking human-like cognitive features.
Recent Selected Publications :
- K. H. Shankar and M. W. Howard (2016). Neural Mechanism to simulate a scale-invariant future timeline. Neural Computation, in press. arXiv:1503.03322
- M. W. Howard and K. H. Shankar (2016). Neural scaling laws for an uncertain world. arXiv:1607.04886.
- K. H. Shankar (2016). Horizonless, singularity-free compact shells satisfying NEC. Gen. Relativity and Gravitation, in press. arXiv:1510.00851
- K. H. Shankar and A. Balaraman and K. C. Wali (2012). Metric theory of gravity with torsion in an extra dimension, Phys. Rev. D 86 (2), 024007, arxiv: gr-qc/1203.5552.
Scale invariant Fuzzy Memory : A short talk bridging cognitive neuroscience and neural engineering.