Adaptive cognitive prosthetic and applications thereof
An adaptive cognitive prosthetic that learns to replace neural function that was lost due to a brain injury or disease is described herein. In some embodiments, an adaptive cognitive prosthetic comprises a processing unit for converting input data from an input assembly into a cognitive variable and selecting a stimulation pattern for conveying the cognitive variable. The processing unit employs an adaptive algorithm to assemble the stimulation pattern by combination of subset stimulation patterns, the combination of subset stimulation patterns learned by the adaptive algorithm through error analysis.
1. An adaptive cognitive prosthetic comprising:
a processing unit for converting input data related to a patient's visual field comprising neural activity from an input data assembly into a cognitive variable for treating visual dysfunction and selecting a stimulation pattern for conveying the cognitive variable, wherein the processing unit employs an adaptive algorithm to reduce dimensionality of the stimulation pattern via assembling the stimulation pattern by combination of subset stimulation patterns in a hierarchical format, the combination of subset stimulation patterns learned by the adaptive algorithm through error analysis comprising determining whether a combination of subset stimulation patterns meets a set or predetermined accuracy threshold guided by feedback from a behavioral paradigm and/or directly from a patient using the adaptive cognitive prosthetic;
wherein the adaptive algorithm includes discrete cosine transforms to estimate the subset stimulation patterns, and wherein frequency of the discrete cosine transforms increases with increasing complexity of the subset stimulation patterns; and
a stimulation assembly for administering the selected stimulation pattern to treat the visual dysfunction.
2. The adaptive cognitive prosthetic of claim 1 , wherein the hierarchical format classifies the subset stimulation patterns according to the pattern complexity, the pattern complexity being proportional to signal frequencies defining the subset stimulation pattern.
3. The adaptive cognitive prosthetic of claim 2 , wherein the subset stimulation patterns are combined in order of increasing complexity.
4. The adaptive cognitive prosthetic of claim 1 , wherein the input assembly comprises a recording assembly comprising electrodes for recording the neural activity input data.
5. The adaptive cognitive prosthetic of claim 1 , wherein the stimulation assembly comprises electrode arrays for administering the selected stimulation pattern via electrical discharge.
6. The adaptive cognitive prosthetic of claim 1 , wherein the stimulation assembly comprises waveguides for administering the stimulation pattern via electromagnetic stimulation.
7. The adaptive cognitive prosthetic of claim 6 , wherein the waveguides are provided as arrays.
8. The adaptive cognitive prosthetic of claim 1 , wherein the stimulation assembly administers the selected stimulation pattern via fluctuating magnetic fields.
9. The adaptive cognitive prosthetic of claim 1 , wherein the visual dysfunction comprises hemispatial neglect, and the cognitive variable corresponds to visual identification of objects in an affected region of the visual field.
10. A method of providing a stimulation pattern for conveying a cognitive variable for treating visual dysfunction comprising:
providing input data related to a patient's visual field comprising neural activity to a processing unit from an input data assembly;
converting the input data into the cognitive variable for treating visual dysfunction with the processing unit and selecting a stimulation pattern for conveying the cognitive variable with the processing unit, wherein the processing unit employs an adaptive algorithm to reduce dimensionality of the stimulation pattern via assembling the stimulation pattern by combination of subset stimulation patterns in a hierarchical format, the combination of subset stimulation patterns learned by the adaptive algorithm through error analysis comprising determining whether a combination of subset stimulation patterns meets a set or predetermined accuracy threshold guided by feedback from a behavioral paradigm and/or directly from a patient using the adaptive cognitive prosthetic;
wherein the adaptive algorithm includes discrete cosine transforms to estimate the subset stimulation patterns, and wherein frequency of the discrete cosine transforms increases with increasing complexity of the subset stimulation patterns; and
administering the selected stimulation pattern with a stimulation assembly.
11. The method of claim 10 , wherein the hierarchical format classifies the subset stimulation patterns according to the pattern complexity, the pattern complexity being proportional to signal frequencies defining the subset stimulation pattern.
12. The method of claim 11 , wherein the subset stimulation patterns are combined in order of increasing complexity.
13. The method of claim 10 , wherein the error function evaluates accuracy of each combination of subset stimulation patterns in representing one or more functions of the cognitive variable.
14. The method of claim 10 , wherein the visual dysfunction comprises hemispatial neglect, and the cognitive variable corresponds to visual identification of objects in an affected region of the visual field.