A method, system and computer program product, for identifying anomalies in a monitored scene, the method comprising: receiving into a spiking neural network sensor readings from a capture device monitoring a scene; and outputting an indication to a change in the scene, wherein the spiking neural network comprises a multiplicity of layers, each of the multiplicity of layers comprising a neuron per substantially each pixel in a sensor capturing the monitored scene, and wherein one or more of the layers comprises a memory-like unit for comparing states occurring at a time difference.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for identifying anomalies in a monitored scene, comprising: receiving into a spiking neural network sensor readings from a capture device monitoring a scene; and outputting an indication to a change in the scene, wherein the spiking neural network comprises a multiplicity of layers, each of the multiplicity of layers comprising a neuron per substantially each pixel in a sensor capturing the monitored scene, and wherein at least one of the layers comprises a memory-like unit for comparing states occurring at a time difference. 2. The method of claim 1, wherein the memory-like unit uses a spike-timing-dependent plasticity (STDP) process. 3. The method of claim 1, wherein the neural network is implemented in hardware. 4. The method of claim 1, wherein the spiking neural network comprises: a time to first spike layer comprising a grid of first neurons, each of the first neurons receiving a sensor reading and converting the sensor reading into time by firing a first spike; a waver layer comprising a grid of second neurons, each of the second neurons connected to receive as input the first spike issued by a corresponding first neuron, the waver layer configured to perform first noise filtering within the input and fire a second set of spikes; a layer of interest comprising a grid of third neurons, each of the third neurons connected to receive as input spikes from the second set of spikes issued by a corresponding second neuron, the layer of interest configured to perform a second noise filtering stage by at least part of the third neurons firing a third set of spikes substantially simultaneously; and a change layer comprising a grid of fourth neurons, each of the fourth neurons connected to receive as input a spike from the third set of spikes issued by a corresponding third neuron, and detecting a change between a stored state and a current state using the memory-like unit. 5. The method of claim 4, wherein the second neurons of the waver layer are interconnected, and wherein the first noise filtering is performed by at least one of the second neurons firing a spike to another neuron from the second neurons, thereby at least one of the second neurons firing multiple spikes per iteration. 6. The method of claim 4, further comprising a hillclimb neuron for receiving input from a multiplicity of the second neurons and providing output to the third neurons, the hillclimb neuron spiking when a number of input spikes decreases, and making the at least part of the third neurons fire the third set of spikes fire substantially simultaneously. 7. The method of claim 4, wherein an anomaly is detected as change detected in at least a predetermined number of the fourth neurons. 8. The method of claim 1, wherein the method is unsupervised. 9. A computerized system for projecting a machine learning model, the system comprising a processor, the system configured to: receiving sensor readings from a capture device monitoring a scene into a spiking neural network; and outputting by the processor an indication to a change in the scene, wherein the spiking neural network comprises a multiplicity of layers, each of the multiplicity of layers comprising a neuron per substantially each pixel in a sensor capturing the monitored scene, and wherein at least one of the layers comprises a memory-like unit for comparing states occurring at a time difference. 10. The system of claim 9, wherein the memory-like unit uses a spike-timing-dependent plasticity (STDP) process. 11. The system of claim 9, wherein the neural network is implemented in hardware. 12. The system of claim 9, wherein the spiking neural network comprises: a time to first spike layer comprising a grid of first neurons, each of the first neurons receiving a sensor reading and converting the sensor reading into time by firing a first spike; a waver layer comprising a grid of second neurons, each of the second neurons connected to receive as input the first spike issued by a corresponding first neuron, the waver layer configured to perform first noise filtering within the input and fire a second set of spikes; a layer of interest comprising a grid of third neurons, each of the third neurons connected to receive as input spikes from the second set of spikes issued by a corresponding second neuron, the layer of interest configured to perform a second noise filtering stage by at least part of the third neurons firing a third set of spikes fired substantially simultaneously; and a change layer comprising a grid of fourth neurons, each of the fourth neurons connected to receive as input a spike from the third set of spikes issued by a corresponding third neuron, and detecting a change between a stored state and a current state using the memory-like unit. 13. The system of claim 12, wherein the second neurons of the waver layer are interconnected, and wherein the first noise filtering is performed by at least one of the second neurons firing a spike to another neuron from the second neurons, thereby at least one of the second neurons firing multiple spikes per iteration. 14. The system of claim 12, further comprising a hillclimb neuron for receiving input from a multiplicity of the second neurons and providing output to the third neurons, the hillclimb neuron spiking when a number of input spikes decreases, and making the at least part of the third neurons fire the third set of spikes fire substantially simultaneously. 15. The system of claim 12, wherein an anomaly is detected as change detected in at least a predetermined number of the fourth neurons. 16. A computer program product comprising a computer readable storage medium retaining program instructions, which program instructions when read by a processor, cause the processor to perform a method comprising: receiving into a spiking neural network sensor readings from a capture device monitoring a scene; and outputting an indication to a change in the scene, wherein the spiking neural network comprises a multiplicity of layers, each of the multiplicity of layers comprising a neuron per substantially each pixel in a sensor capturing the monitored scene, and wherein at least one of the layers comprises a memory-like unit for comparing states occurring at a time difference.