Praveen K. Pilly

Research Staff Scientist

Information and Systems Sciences Laboratory (ISSL)

HRL Laboratories

 

 

I am interested in building neural and computational models of learning and memory, pattern recognition, spatial navigation, visual processing, and sensory-motor control. I am also interested in integrating these competences into adaptive brain-inspired applications for solving important real-world problems.

 

I have received my B.Tech. degree in Electrical Engineering from Indian Institute of Technology Madras and my M.A. and Ph.D. degrees in Cognitive and Neural Systems from Boston University. I was previously a Research Assistant Professor in the Center for Adaptive Systems (CAS) and the Center for Computational Neuroscience and Neural Technology (CompNet) at Boston University.

 

You can find more about me here. I can be reached at pkpilly@hrl.com.

 

 

DOCTORAL DISSERTATION

Pilly, P.K. (2009). Decision-making during motion perception: Neural modeling and psychophysical experiments. Boston University Graduate School of Arts and Sciences. link, more

 

JOURNAL PUBLICATIONS (* co-first authors)

[1] Grossberg*, S. and Pilly*, P.K. (2008). Temporal dynamics of decision-making during motion perception in the visual cortex. Vision Research, 48(12), 1345-1373. link

 

[2] Seitz, A.R., Pilly, P.K., and Pack, C.C. (2008). Interactions between contrast and spatial displacement in visual motion processing. Current Biology, 18(19), R904-R906. link, more

 

[3] Pilly, P.K. and Seitz, A.R. (2009). What a difference a parameter makes: A psychophysical comparison of random dot motion algorithms. Vision Research, 49(13), 1599-1612. link, more

 

[4] Pilly, P.K., Grossberg, S., and Seitz, A.R. (2010). Low-level sensory plasticity during task-irrelevant perceptual learning: Evidence from conventional and double training procedures. Vision Research, 50(4), 424-432. link

 

[5] Pilly, P.K. and Grossberg, S. (2012). How do spatial learning and memory occur in the brain? Coordinated learning of entorhinal grid cells and hippocampal place cells. Journal of Cognitive Neuroscience, 24(5), 1031-1054. link

 

[6] Grossberg*, S. and Pilly*, P.K. (2012). How entorhinal grid cells may learn multiple spatial scales from a dorsoventral gradient of cell response rates in a self-organizing map. PLoS Computational Biology, 8(10), e1002648. link

 

[7] Pilly, P.K. and Grossberg, S. (2013). Spiking neurons in a hierarchical self-organizing map model can learn to develop spatial and temporal properties of entorhinal grid cells and hippocampal place cells. PLoS One, 8(4), e60599. link

 

[8] Pilly, P.K. and Grossberg, S. (2013). How reduction of theta rhythm by medial septum inactivation may covary with disruption of entorhinal grid cell responses due to reduced cholinergic transmission. Frontiers in Neural Circuits, 7, 173. link

 

[9] Grossberg*, S. and Pilly*, P.K. (2014). Coordinated learning of grid cell and place cell spatial and temporal properties: Multiple scales, attention, and oscillations. Philosophical Transactions of the Royal Society London B, 369(1635), 20120524. link

 

[10] Pilly, P.K. and Grossberg, S. (2014). How does the modular organization of entorhinal grid cells develop? Frontiers in Human Neuroscience, in press. link

 

 

EDUCATIONAL MODULES AND SOFTWARE

Spiking GridPlaceMap model (Pilly & Grossberg, 2013) link

 

MOtion DEcision (MODE) model (Grossberg & Pilly, 2008) link

 

Directional transient cells (Grossberg, Mingolla, & Viswanathan, 2001) link

 

Non-directional transient cells (Grossberg & Rudd, 1992) link

 

Outstar learning law (Grossberg, 1976) link

 

Instar learning law (Grossberg, 1976) link