Apparatus and methods for learning and training in neural network-based devices. In one implementation, the devices each comprise multiple spiking neurons, configured to process sensory input. In one approach, alternate heterosynaptic plasticity mechanisms are used to enhance learning and field diversity within the devices. The selection of alternate plasticity rules is based on recent post-synaptic activity of neighboring neurons. Apparatus and methods for simplifying training of the devices are also disclosed, including a computer-based application. A data representation of the neural network may be imaged and transferred to another computational environment, effectively copying the brain. Techniques and architectures for achieve this training, storing, and distributing these data representations are also disclosed.
Please help me write a proper abstract based on the patent claims. CLAIM: 1.-28. (canceled) 29. A method of behavioral programming in an artificial neural network, the method comprising: generating a data link to at least one device configured to run the artificial neural network; receiving one or more data elements indicating a current status associated with the artificial neural network; causing display of information related to at least a portion of the one or more data elements; receiving user input from a user interface; generating one or more feedback elements based at least in part on the user input; and transmitting the one or more feedback elements to the artificial neural network via the data link. 30. The method of claim 29, wherein the at least one device configured to run the artificial neural network comprises a robot comprising one or more functional modules. 31. The method of claim 30, wherein the one or more functional modules comprise at least one sensory device. 32. The method of claim 31, wherein: the artificial neural network is configured to generate one or more discrete output signals in response to input from the at least one sensory device; and the discrete output signals cause an action at least one of the one or more functional modules. 33. The method of claim 32, wherein: the displayed information comprises an one or more visual representations of the input from the sensor; and at least one indicator configured to indicate one or more neurons involved in the generation of the one or more discrete output signals. 34. The method of claim 29, wherein the display and the user interface are disposed on a mobile wireless-enabled device. 35. The method of claim 34, wherein the generated data link is effected via at least a personal area network. 36. The method of claim 34, wherein the generated data link comprises a data session over at least a Wi-Fi compliant connection. 37. The method of claim 29, wherein the one or more feedback elements comprise negative feedback designed to suppress an output generated by one or more neurons of the artificial neural network. 38. The method of claim 29, wherein the one or more feedback elements comprise positive feedback to potentiate one or more neurons of the artificial neural network. 39. The method of claim 29, further comprising downloading an image of the artificial neural network to the at least one device from a cloud-based server. 40. The method of claim 39, further comprising causing recommendation of the image via at least one application running on the cloud-based server, the recommendation being based at least in part on a task designated by the user.