Elements of artificial neural networks
Elements of artificial neural networks
Computing with action potentials
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Spikes: exploring the neural code
Spikes: exploring the neural code
The bifurcating neuron network 2: an analog associative memory
Neural Networks
Bayesian computation in recurrent neural circuits
Neural Computation
Modified Bifurcating Neuron with leaky-integrate-and-fire model
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Biophysics of Computation: Information Processing in Single Neurons (Computational Neuroscience Series)
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Robust sound onset detection using leaky integrate-and-fire neurons with depressing synapses
IEEE Transactions on Neural Networks
Multilayer and multipathway simulation on retina
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
A flexible edge matching technique for object detection in dynamic environment
Applied Intelligence
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
Main Retina Information Processing Pathways Modeling
International Journal of Cognitive Informatics and Natural Intelligence
Block-matching algorithm based on harmony search optimization for motion estimation
Applied Intelligence
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A biologically inspired visual system capable of motion detection and pursuit motion is implemented using a Discrete Leaky Integrate-and-Fire (DLIF) neuron model. The system consists of a visual world, a virtual retina, the neural network circuitry (DLIF) to process the information, and a set of virtual eye muscles that serve to move the input area (visual field) of the retina within the visual world. Temporal aspects of the DLIF model are heavily exploited including: spike propagation latency, relative spike timing, and leaky potential integration. A novel technique for motion detection is employed utilizing coincidence detection aspects of the DLIF and relative spike timing. The system as a whole encodes information using relative spike timing of individual action potentials as well as rate coded spike trains. Experimental results are presented in which the motion of objects is detected and tracked in real and animated video. Pursuit motion is successful using linear and also sinusoidal paths which include object velocity changes. The visual system exhibits dynamic overshoot correction heavily exploiting neural network characteristics. System performance is within the bounds of real-time applications.