DYNAMICALLY RECONFIGURABLE STOCHASTIC LEARNING APPARATUS AND METHODS
First Claim
1. Computer readable apparatus comprising a storage medium, said storage medium comprising a plurality of instructions to operate a network, comprising a plurality of spiking neurons, the instructions configured to, when executed:
- based at least in part on receiving a task indication, select first group and second group from said plurality of spiking neurons;
operate said first group in accordance with first learning rule, based at least in part on an input signal and training signal; and
operate said second group in accordance with second learning rule, based at least in part on input signal.
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Accused Products
Abstract
Generalized learning rules may be implemented. A framework may be used to enable adaptive signal processing system to flexibly combine different learning rules (supervised, unsupervised, reinforcement learning) with different methods (online or batch learning). The generalized learning framework may employ average performance function as the learning measure thereby enabling modular architecture where learning tasks are separated from control tasks, so that changes in one of the modules do not necessitate changes within the other. Separation of learning tasks from the control tasks implementations may allow dynamic reconfiguration of the learning block in response to a task change or learning method change in real time. The generalized learning apparatus may be capable of implementing several learning rules concurrently based on the desired control application and without requiring users to explicitly identify the required learning rule composition for that application.
183 Citations
26 Claims
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1. Computer readable apparatus comprising a storage medium, said storage medium comprising a plurality of instructions to operate a network, comprising a plurality of spiking neurons, the instructions configured to, when executed:
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based at least in part on receiving a task indication, select first group and second group from said plurality of spiking neurons; operate said first group in accordance with first learning rule, based at least in part on an input signal and training signal; and operate said second group in accordance with second learning rule, based at least in part on input signal. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. Computer readable apparatus comprising a storage medium, said storage medium comprising a plurality of instructions to operate a processing apparatus, the instructions configured to, when executed:
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based at least in part on first task indication at first instance, operate said processing apparatus in accordance with first stochastic hybrid learning rule configured to produce first learning signal based at least in part on first input signal and first training signal, associated with said first task indication; and based at least in part on second task indication at second instance, subsequent to first instance operate said processing apparatus in accordance with second stochastic hybrid learning rule configured to produce second learning signal based at least in part on second input signal and second training signal, associated with said second task indication; wherein; said first hybrid learning rule is configured to effectuate first rule combination; and said second hybrid learning rule is configured to effect second rule combination, said second combination being different from said first combination. - View Dependent Claims (15, 16, 17, 18)
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19. A computer-implemented method of operating a computerized spiking network, comprising a plurality of nodes, the method comprising:
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based at least in part on first task indication at a first instance, operating said plurality of nodes in accordance with a first stochastic hybrid learning rule configured to produce first learning signal based at least in part on first input signal and first training signal, associated with said first task indication; and based at least in part on second task indication at second instance, subsequent to first instance; operating first portion of said plurality of nodes in accordance with second stochastic hybrid learning rule configured to produce second learning signal based at least in part on second input signal and second training signal, associated with said second task indication; and operating second portion of said plurality of nodes in accordance with third stochastic learning rule configured to produce third learning signal based at least in part on second input signal associated with said second task indication; wherein; said first hybrid learning rule is configured to effect first rule combination; and said second hybrid learning rule is configured to effect second rule combination, said second combination substantially different from said first combination. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26)
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Specification