Efficient Spiking Neural Network Model of Pattern Motion Selectivity in Visual Cortex
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Simulating large-scale models of biological motion perception is challenging, due to the required memory to store the network structure and the computational power needed to quickly solve the neuronal dynamics. A low-cost yet high-performance approach to simulating large-scale neural network models in real-time is to leverage the parallel processing capability of graphics processing units (GPUs). Based on this approach, we present a two-stage model of visual area MT that we believe to be the first large-scale spiking network to demonstrate pattern direction selectivity. In this model, component-direction-selective (CDS) cells in MT linearly combine inputs from V1 cells that have spatiotemporal receptive fields according to the motion energy model of Simoncelli and Heeger. Pattern-direction-selective (PDS) cells in MT are constructed by pooling over MT CDS cells with a wide range of preferred directions. Responses of our model neurons are comparable to electrophysiological results for grating and plaid stimuli as well as speed tuning. The behavioral response of the network in a motion discrimination task is in agreement with psychophysical data. Moreover, our implementation outperforms a previous implementation of the motion energy model by orders of magnitude in terms of computational speed and memory usage. The full network, which comprises 153,216 neurons and approximately 40 million synapses, processes 20 frames per second of a 40 × 40 input video in real-time using a single off-the-shelf GPU. To promote the use of this algorithm among neuroscientists and computer vision researchers, the source code for the simulator, the network, and analysis scripts are publicly available.
KeywordsPattern motion selectivity Spiking neural network MT GPU Real-time CARLsim
This work was supported by the Defense Advanced Research Projects Agency (DARPA) subcontract 801888-BS. We thank Jayram M. Nageswaran for his work developing the custom spiking neural network simulator. We also thank Michael Avery, Kris Carlson, and Steve Grossberg for valuable feedback and discussion on this project.
Conflict of Interest
The authors have no conflicts of interest with this manuscript.
- Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems (Computational neuroscience). Cambridge: Massachusetts Institute of Technology Press.Google Scholar
- Fidjeland, A. K., & Shanahan, M. P. (2010). Accelerated simulation of spiking neural networks using GPUs. In Neural Networks (IJCNN), The 2010 International Joint Conference on, 18–23 July 2010 (pp. 1–8). doi: 10.1109/IJCNN.2010.5596678.
- Freeman, W. T., & Adelson, E. H. (1991). The design and use of steerable filters. In IEEE Pattern Analysis and Machine Intelligence (Vol. 13, pp. 891–906).Google Scholar
- Izhikevich, E. M. (2007). Dynamical systems in neuroscience: The geometry of excitability and bursting (Computational neuroscience). Cambridge: MIT Press.Google Scholar
- Khan, M., Lester, D., Plana, L., Rast, A., Jin, X., & Painkras, E. SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor. In IEEE International Joint Conference on Neural Networks, 2008 (pp. 2849–2856).Google Scholar
- Koch, C. (1999). Biophysics of computation: Information processing in single neurons (Computational neuroscience). New York: Oxford University Press.Google Scholar
- Layton, O. W., Mingolla, E., & Browning, N. A. (2012). A motion pooling model of visually guided navigation explains human behavior in the presence of independently moving objects. Journal of Vision, 12(1), doi: 10.1167/12.1.20.
- Movshon, J. A., Adelson, E. H., Gizzi, M. S., & Newsome, W. T. (1985). The analysis of moving visual patterns (Pattern recognition mechanisms). New York: Springer.Google Scholar
- Nageswaran, J. M., Dutt, N., Krichmar, J. L., Nicolau, A., & Veidenbaum, A. V. (2009). A configurable simulation environment for the efficient simulation of large-scale spiking neural networks on graphics processors. Neural Networks, 22(5–6), 791–800. doi: 10.1016/j.neunet.2009.06.028.PubMedCrossRefGoogle Scholar
- Perrone, J. A. (2012). A neural-based code for computing image velocity from small sets of middle temporal (MT/V5) neuron inputs. Journal of Vision, 12(8), doi: 10.1167/12.8.1.
- Yudanov, D., Shaaban, M., Melton, R., & Reznik, L. (2010). GPU-based simulation of spiking neural networks with real-time performance & high accuracy. In Neural Networks (IJCNN), The 2010 International Joint Conference on, 18–23 July 2010 (pp. 1–8). doi: 10.1109/IJCNN.2010.5596334.