A neuromorphic depth-from-motion vision model with STDP adaptation.

We propose a simplified depth-from-motion vision model based on leaky integrate-and-fire (LIF) neurons for edge detection and two-dimensional depth recovery. In the model, every LIF neuron is able to detect the irradiance edges passing through its receptive field in an optical flow field, and respond to the detection by firing a spike when the neuron's firing criterion is satisfied. If a neuron fires a spike, the time-of-travel of the spike-associated edge is transferred as the prediction information to the next synapse-linked neuron to determine its state. Correlations between input spikes and their timing thus encode depth in the visual field. The adaptation of synapses mediated by spike-timing-dependent plasticity is used to improve the algorithm's robustness against inaccuracy caused by spurious edge propagation. The algorithm is characterized on both artificial and real image sequences. The implementation of the algorithm in analog very large scale integrated (aVLSI) circuitry is also discussed.

An adaptive visual neuronal model implementing competitive, temporally asymmetric Hebbian learning.

A novel depth-from-motion vision model based on leaky integrate-and-fire (I&F) neurons incorporates the implications of recent neurophysiological findings into an algorithm for object discovery and depth analysis. Pulse-coupled I&F neurons capture the edges in an optical flow field and the associated time of travel of those edges is encoded as the neuron parameters, mainly the time constant of the membrane potential and synaptic weight. Correlations between spikes and their timing thus code depth in the visual field. Neurons have multiple output synapses connecting to neighbouring neurons with an initial Gaussian weight distribution. A temporally asymmetric learning rule is used to adapt the synaptic weights online, during which competitive behaviour emerges between the different input synapses of a neuron. It is shown that the competition mechanism can further improve the model performance. After training, the weights of synapses sourced from a neuron do not display a Gaussian distribution, having adapted to encode features of the scenes to which they have been exposed.

Spike timing dependent plasticity (STDP) can ameliorate process variations in neuromorphic VLSI.

A transient-detecting very large scale integration (VLSI) pixel is described, suitable for use in a visual-processing, depth-recovery algorithm based upon spike timing. A small array of pixels is coupled to an adaptive system, based upon spike timing dependent plasticity (STDP), that aims to reduce the effect of VLSI process variations on the algorithm's performance. Results from 0.35 microm CMOS temporal differentiating pixels and STDP circuits show that the system is capable of adapting to substantially reduce the effects of process variations without interrupting the algorithm's natural processes. The concept is generic to all spike timing driven processing algorithms in a VLSI.