Journal article

Predictive Visual Motion Extrapolation Emerges Spontaneously and without Supervision at Each Layer of a Hierarchical Neural Network with Spike-Timing-Dependent Plasticity

Anthony N Burkitt, Hinze Hogendoorn



The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localization of a moving object. One way this problem might be solved is extrapolation: using an object's past trajectory to predict its location in the present moment. Here, we investigate how a simulated in silico layered neural network might implement such extrapolation mechanisms, and how the necessary neural circuits might develop. We allowed an unsupervised hierarchical network of velocity-tuned neurons to learn its connectivity through spike-timing-dependent plasticity (STDP). We show that the temporal contingencies between the different neural popu..

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Awarded by Australian Research Council

Awarded by Australian Government

Awarded by ONR MURI

Funding Acknowledgements

We thank Hamish Meffin and Stefan Bode for helpful comments on the manuscript. H.H. was supported by the Australian Research Council's Discovery Projects Funding Scheme Project DP180102268. A.N.B. was supported by the Australian Government, via Grant AUSMURIB000001 associated with ONR MURI Grant N00014-19-1-2571.