Neuromorphic architecture for unsupervised pattern detection and feature learning

1. An apparatus for computation comprising:

a first neuronal module comprising a first plurality of neurons, wherein each neuron in the first plurality of neurons operates with a different learning mechanism selected from a first set of learning mechanisms;

a second neuronal module comprising a second plurality of neurons, wherein each neuron in the second plurality of neurons operates with a different learning mechanism selected from a second set of learning mechanisms; and

an arbitration mechanism operatively connected to the first neuronal module and the second neuronal module;

wherein the first set of learning mechanisms and the second set of learning mechanisms comprise an application of an inverted learning mechanism following an application of a learning formula comprising spike timing dependent plasticity;

wherein the spike-timing dependent plasticity comprises an enhancement selected from either selective potentiation or selective depression;

wherein the enhancement is based on a measure indicative of a learning status, the measure comprising a cumulative strength corresponding to, for the first set of learning mechanisms, a first total post-synaptic potential of activated synapses of the first neuronal module, and for the second set of learning mechanisms, a second total post-synaptic potential of activated synapses of the second neuronal

module;

wherein at least one first neuron of the first plurality of neurons of the first neuronal module is decoupled from the first neuronal module, wherein the at least one first decoupled neuron operates independently from the other neurons of the first plurality of neurons, and activates in response to an input matching features learned by the at least one first decoupled neuron; and

wherein at least one second neuron of the second plurality of neurons of the second neuronal module is decoupled from the second neuronal module, wherein the at least one second decoupled neuron operates independently from the other neurons of the second plurality of neurons, and activates in response to an input matching features learned by the at least one second decoupled neuron.

2. The apparatus of claim 1 , wherein the first set of learning mechanisms is the same as the second set of learning mechanisms.

3. The apparatus of claim 1 , wherein the first set of learning mechanisms is different from the second set of learning mechanisms.

4. The apparatus of claim 1 , wherein a learning mechanism in the first set of learning mechanisms comprises spike-timing dependent plasticity with selective potentiation.

5. The apparatus of claim 1 , wherein a learning mechanism in the first set of learning mechanisms comprises spike-timing dependent plasticity with selective depression.

6. The apparatus of claim 1 , wherein a learning mechanism in the first predefined set of learning mechanisms comprises inverted spike-timing dependent plasticity.

7. The apparatus of claim 1 , wherein the arbitration mechanism disables either the first neuronal module or the second neuronal module.

8. The apparatus of claim 1 , wherein the first plurality of neurons comprises at least a first level tuned neuron, the second plurality of neurons comprises at least a second level tuned neuron, and the arbitration mechanism comprises a third neuron that provides a level output signal that modifies the behavior of the first level tuned neuron and the second level tuned neuron.

9. The apparatus of claim 1 , wherein the first neuronal module comprises at least a first neuron with inhibitory link, the second neuronal module comprises at least a second neuron with inhibitory link, and the arbitration mechanism comprises an inhibitory output that modifies the behavior of the first neuron and the second neuron.

10. The apparatus of claim 1 , wherein the measure comprises a count of synapses of the neuron that have high weight values.

11. The apparatus of claim 1 , wherein the measure comprises the firing of at least one neuron in the first plurality of neurons.

12. The apparatus of claim 1 , wherein the total post-synaptic potential of a neuron j is calculated as tPSP j =Σ i=1 m w ij x i , wherein x i is an input spike and w ij are synaptic weights of the neuron j.

13. The method of claim 12 , wherein:

the first total post-synaptic potential of activated synapses of neurons of the first neuronal module and the second total post-synaptic potential of activated synapses of neurons of the second neuronal module are each separately integrated into a neuronal membrane potential; and

in response to the membrane potential crossing a threshold, a post-synaptic spike is emitted and the membrane potential is reset to zero.

14. A method for pattern detection and feature learning comprising:

training a first neuronal module, wherein the first neuronal module comprises a first neuron having a first learning mechanism;

training a second neuronal module, wherein the second neuronal module comprises a second neuron having a second learning mechanism;

providing an arbitration mechanism connected to the first neuronal module and the second neuronal module;

applying an input to the first neuronal module and the second neuronal module;

wherein the first learning mechanism and the second learning mechanism comprise an application of an inverted learning mechanism following an application of a learning formula comprising spike timing dependent plasticity;

wherein the spike-timing dependent plasticity comprises an enhancement selected from either selective potentiation or selective depression;

wherein the enhancement is based on a measure indicative of a learning status, the measure comprising a cumulative strength corresponding to, for the first learning mechanism, a first total post-synaptic potential of activated synapses of the first neuronal module, and for the second learning mechanism, a second total post-synaptic potential of activated synapses of the second neuronal module;

decoupling at least one first neuron of a first plurality of neurons of the first neuronal module from the first neuronal module, wherein the at least one first decoupled neuron operates independently from the other neurons of the first plurality of neurons, and activates in response to an input matching features learned by the at least one first decoupled neuron; and

decoupling at least one second neuron of a second plurality of neurons of the second neuronal module from the second neuronal module, wherein the at least one second decoupled neuron operates independently from the other neurons of the second plurality of neurons, and activates in response to an input matching features learned by the at least one second decoupled neuron.

15. The method of claim 14 , further comprising training a third neuronal module, wherein the third neuronal module comprises a third neuron having a third learning mechanism, and applying the input to the third neuronal module.

16. The method of claim 14 , wherein the first learning mechanism comprises spike-timing dependent plasticity with selective potentiation.

17. The method of claim 14 , wherein the first learning mechanism comprises spike-timing dependent plasticity with selective depression.

18. The method of claim 14 , wherein the first learning mechanism comprises inverted spike-timing dependent plasticity.

19. The method of claim 14 , further comprising enhancing a spike-timing dependent plasticity of the first neuron with a selective potentiation that is controlled by a measure indicative of a learning status.

20. An apparatus for computation comprising:

a first neuronal module comprising a first level tuned neuron, wherein the first level tuned neuron has a first learning mechanism;

a second neuronal module comprising a second level tuned neuron, wherein the second level tuned neuron has a second learning mechanism; and

an arbitration mechanism operatively connected to the first neuronal module and the second neuronal module and comprising a third neuron that provides a level output that modifies the behavior of the first neuronal module and second neuronal module;

wherein the first learning mechanism and the second learning mechanism comprise an application of an inverted learning mechanism following an application of a learning formula comprising spike timing dependent plasticity;

wherein the spike-timing dependent plasticity comprises an enhancement selected from either selective potentiation or selective depression;

wherein the enhancement is based on a measure indicative of a learning status, the measure comprising a cumulative strength corresponding to, for the first learning mechanism, a first total post-synaptic potential of activated synapses of the first neuronal module, and for the second learning mechanism, a second total post-synaptic potential of activated synapses of the second neuronal module;

wherein the first level tuned neuron of the first neuronal module is decoupled from the first neuronal module, wherein the first level tuned decoupled neuron operates independently from other neurons of a first plurality of neurons of the first neuronal module, and the first level tuned decoupled neuron activates in response to an input matching features learned by the first level tuned decoupled neuron; and

wherein the second level tuned neuron of the second neuronal module is decoupled from the second neuronal module, wherein the second level tuned decoupled neuron operates independently from other neurons of a second plurality of neurons of the second neuronal module, and the second level tuned decoupled neuron activates in response to an input matching features learned by the second level tuned decoupled neuron.