In this talk, a new computational paradigm is presented using photonics to mimic the functionality of a spiking neuron. Spike processing is both computationally efficient and scalable, adopting the best features of both analog and digital computing. Whereas biological neurons implement spike processing using electrochemical spikes on a millisecond time scale, our “photonic neuron” performs spike processing using optoelectronic technology in picoseconds. Like it’s physiological counterpart, the photonic neuron consists of a reconfigurable FIR filter at it’s front end followed by integration and thresholding stages. A single photonic neuron circuit accomplishing feature recognition and imitating the escape response of a crayfish will be presented. One of the most powerful capabilities of neurons is their ability to learn and adapt to the environment based on experience. This is accomplished by adjusted the strength of synaptic connections based on correlations between pre-synaptic and post-synaptic activity. The first demonstration of learning using photonic technology will be presented, potentially laying the foundation for learning at speeds a billion times faster than biological neurons.