Complex Cell-like Direction Selectivity through Spike-Timing Dependent Plasticity

Abstract

Complex cells in primary visual cortex exhibit highly nonlinear receptive field properties such as phase-invariant direction selectivity and antagonistic interactions between individually excitatory stimuli. Traditional models assume that these properties are governed by the outputs of antecedent simple cells, but these models are at odds with studies showing that complex cells may receive direct inputs from the lateral geniculate nucleus (LGN) or can be driven by stimuli that fail to activate simple cells. Using a biophysically detailed model of recurrently connected cortical neurons, we show that complex cell-like direction selectivity may emerge without antecedent simple ceil inputs, as a consequence of spike-timing dependent synaptic plasticity during visual development. The directionally-selective receptive fields of model neurons, as determined by reverse correlation and 2-bar interaction maps, were similar to those obtained from complex cells in awake monkey primary visual cortex. These results suggest a new interpretation of complex cells as integral components of an adaptive cortical circuit for motion detection and prediction.

Indexing terms:

• Neuroscience
• Visual perception
• Neural networks
• Motion detection
• Prediction

Additional information

Notes on contributors

Rajesh P N Rao

Rajesh P N Rao completed his high school in Hyderabad, India in 1988 and graduated summa cum laude in 1992 with bachelor's degrees in Compuer Science and Mathematics from Angelo State University in Texas. He received his M S and PhD degrees in Computer Science from the University of Rochester, New York, and was an Alfred P Sloan Postdoctoral Fellow in computational neuroscience from 1997–2000 at the Salk Institute for Biological Studies in La Jolla, California. He is currently an assistant professor in the Computer Science and Engineering department and in the Neurobiology and Behaviour program at the University of Washington, Seattle. He received a Sloan Research Fellowship for junior faculty in 2001, an NSF career award and a David and Lucile Packard Fellowship in 2002, and an ONR Young Investigator Award in 2003. His current research interests span the areas of computational neuroscience, machine learning, computer vision, robotics, and brain-computer interfaces.

Terrence J Sejnowski

Terrence Sejnowski, PhD, is a Howard Hughes Medical Institute Investigator, Professor and Director of the Computational Neuro biology Laboratory at The Salk Institute. He is also Professor of Biology and Adjunct Professor of Physics, Neurosciences, Psycho-logy, Cognitive Science, Electrical and Computer Engineering, and Computer Science and Engineering at UCSD, where he is Director of the Institute for Neural Computation and Director of training programs in Computational Neurobiology and Cognitive Neuroscience. The goal of his research is to build linking principles from brain to behavior using computational models, and a combination of theoretical and experimental approaches ranging from the biophysical to the systems level. He is founding Editor of “Neural Computation” and President of the Neural Information Processing Systems (NIPS) Foundation. He is a past recipient of the Presidential Young Investigator Award, and was Wiersma Visiting Professor of Neurobiology and a Sherman Fairchild Distinguished Scholar at the California Institute of Technology where he continues as a part-time Visiting Professor. He is a Fellow of the IEEE and has received the Pioneer Award from the IEEE Neural Network Society.