Neural network learning and collaboration apparatus and methods
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.
1. A non-transitory computer readable medium configured to store at least one computer program thereon, the computer program comprising a plurality of instructions configured to when executed:
establish a data connection to a synaptic device;
receive status information from the synaptic device;
issue a command to the synaptic device, the synaptic device being configured to execute an action based at least in part on the command;
receive feedback input from a user; and
forward the feedback input to the synaptic device via the data connection;
wherein:
the forwarding of the feedback input causes the synaptic device to alter a behavioral trait;
the alteration of the behavioral trait comprises an adjustment of a configuration of an artificial neural network disposed at least in part on the synaptic device, the artificial neural network being comprised of a plurality of spiking neurons; and
the alteration of the behavioral trait comprises at least one of (i) a potentiation or (ii) a depression of a subset of the plurality of spiking neurons.
2. The computer readable medium of claim 1 , wherein the instructions are further configured to, when executed, present the user with one or more menus from which to select the feedback input.
3. The computer readable medium of claim 1 , wherein the instructions are further configured to, when executed, display one or more of audio and visual representations of the status information.
4. A training device configured to manage activity in at least one spiking neural network comprising a plurality of spiking neurons, the training device comprising:
at least one network interface configured to:
establish an operative link to the at least one spiking neural network;
receive one or more activity indicators associated with the at least one spiking neural network; and
transmit feedback input to the at least one spiking neural network;
a user interface configured to:
based at least in part on the one or more activity indicators, display one or more human perceptible signals; and
provide a user with at least one menu from which to select training options; and
logic in operative communication with the user interface and the at least one network interface, the logic configured to process a selected training option to generate the feedback input;
wherein the transmission of the feedback input is configured to depress at least a portion of the plurality of spiking neurons.
5. The training device of claim 4 , wherein the training device comprises a mobile wireless device coupled to the at least one spiking neural network via the at least one network interface.
6. The training device of claim 5 , wherein the operative link comprises one or more of a wireless local area network (WLAN) link and a personal area network (PAN) link.
7. The training device of claim 4 , wherein the training device is further configured to obtain an image of the at least one spiking neural network.
8. The training device of claim 4 , wherein the feedback input alters one or more states of individual ones of the plurality of neurons in the at least one spiking neural network.
9. A method of managing activity within a spiking neural network, the method comprising:
establishing a data link to a user interface device;
sending one or more status updates related to a plurality of activity states of neurons in the spiking neural network;
receiving, via the link, one or more feedback indicators, the one or more feedback indicators being based at least on part on a selected training option from the user interface device; and
based on at least one rule, associating the one or more feedback indicators with a subset of the neurons in the spiking neural network;
wherein associating the one or more feedback indicators comprises potentiating the subset of neurons.
10. The method of claim 9 , wherein associating the one or more feedback indicators further comprises depressing the subset of neurons.
11. The method of claim 9 , wherein the spiking neural network is at least partially disposed on a computerized neuromorphic apparatus in operative communication with the user interface device.
12. A computerized neuromorphic apparatus comprising:
one or more functional modules;
a network interface configured to establish a link to a training device; and
logic configured to:
send, to the training device, status indicators related to a neural network disposed at least in part on the computerized neuromorphic apparatus;
receive feedback based on a selected one or more of a plurality of available management options; and
alter a state of one or more of a neuron and a connection in accordance with the feedback and at least one timing rule;
wherein:
a magnitude and a direction of the alteration are based on at least the feedback; and
a target of the alteration is selected based on the at least one timing rule.
13. A computerized neuromorphic apparatus comprising:
one or more functional modules;
a network interface configured to establish a link to a training device; and
logic configured to:
send, to the training device, status indicators related to a neural network disposed at least in part on the computerized neuromorphic apparatus;
receive feedback based on a selected one or more of a plurality of available management options; and
alter a state of one or more of a neuron and a connection in accordance with the feedback and at least one timing rule;
wherein:
the status indicators comprise an identified action and an identifier for one or more contributory neurons; and
the plurality of available management options comprise a positive feedback option and a negative feedback option.
14. The apparatus of claim 12 , wherein
the neuromorphic apparatus is configured to operate the spiking neural network; and
the neuromorphic apparatus is disposed on a robot.
15. The apparatus of claim 14 , wherein the one or more functional modules comprise at least one sensor component disposed on the robot.
16. The apparatus of claim 15 , wherein:
the artificial neural network is configured to generate one or more discrete output signals in response to an input from the at least one sensor component; and
the discrete output signals cause an action by at least one of the one or more functional modules.
17. The apparatus of claim 16 , wherein:
the computerized neuromorphic apparatus is configured to display information comprising one or more visual representations of the input from the at least one sensor component; and
at least one indicator is configured to indicate one or more neurons involved in the generation of the one or more discrete output signals.
18. The apparatus of claim 13 , wherein the link comprises a data link effectuated via at least a personal area network.
19. The apparatus of claim 13 , wherein the link comprises a data session over at least a Wi-Fi compliant connection.
20. A computerized neuromorphic apparatus comprising:
one or more functional modules;
a remote interface configured to establish a link to a training device; and
logic configured to:
send, to the training device, status indicators related to a neural network disposed at least in part on the computerized neuromorphic apparatus;
receive feedback based on a selected one or more of a plurality of available management options; and
alter a state of one or more of a neuron and a connection in accordance with the feedback and at least one timing rule;
wherein one or more feedback elements comprise positive feedback to potentiate one or more neurons of the neural network.
21. The apparatus of claim 20 , wherein the one or more feedback elements comprise negative feedback designed to suppress an output generated by the one or more neurons of the neural network.
22. The computer readable medium of claim 3 , wherein the alteration of the behavioral trait comprises potentiation of individual neurons of the subset of the plurality of spiking neurons.
23. The apparatus of claim 21 , wherein the training device is configured to present a user with one or more menus from which to select feedback input.
24. The apparatus of claim 21 , wherein the training device is configured to present one or more menus configured to effectuate a selection of one or more of the plurality of available management options.
25. The apparatus of claim 24 , wherein the training device is configured to display one or more of audio and visual representations of the status indicators.
26. The apparatus of claim 25 , wherein the link comprises a wireless communication link.