Spiking Cell Decoder Using Neural Trajectory Library
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Solution Overview
Problem
Current Brain-Machine Interfaces (BMIs) face challenges in accurately decoding spiking cell activity to determine biological states and corresponding actions, particularly in real-time applications, due to the vast number of possible neural states and spike patterns, which makes efficient computation and decoding intractable.
Innovation Solution
The system employs a method to decode observed spike counts by calculating probabilities based on stored information regarding possible biological states, using a hardware processor to identify biological states and determine actions, and utilizes a model of neural activity in a multi-dimensional state space to estimate neural and behavioral states, allowing for real-time or offline decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system decodes all possible neural states and spike patterns comprehensively, then the accuracy of biological state identification is improved, but the computational complexity and processing time increase exponentially
Solution Approach 1:
The patent segments the vast space of possible neural states into a library of predefined neural trajectories. Instead of evaluating all possible states, the system divides the state space into discrete trajectory segments that represent biologically plausible pathways. This segmentation reduces the computational burden while maintaining decoding accuracy by focusing only on relevant state transitions.
Solution Approach 2:
The system performs preliminary action by precomputing and storing a library of neural trajectories before actual decoding occurs. These trajectories are prepared in advance and stored for rapid retrieval during real-time decoding. This preliminary preparation eliminates the need for complex on-the-fly computations, enabling fast and accurate state identification.
2Productivity
If the system processes real-time spike counts for multiple spiking cells, then the capability for real-time control is improved, but the computational load becomes intractable
Solution Approach 1:
The system precomputes neural trajectories and stores them in a library before real-time operation. During real-time decoding, the system only needs to retrieve and compare incoming spike counts against these precomputed trajectories, dramatically reducing computational load while maintaining real-time processing capability.
Solution Approach 2:
The computational task is segmented into trajectory retrieval and probability calculation rather than comprehensive state evaluation. By dividing the problem into these smaller, manageable segments that leverage precomputed data, the system achieves real-time performance without intractable computational requirements.
3Adaptability or versatility
If the system considers all possible time sequences of biological states, then the completeness of action recognition is improved, but the decoding efficiency decreases
Solution Approach 1:
All possible time sequences of biological states are precomputed and stored as neural trajectories in a library. This preliminary action ensures completeness of action recognition while enabling fast retrieval during decoding, as the system only needs to query the precomputed library rather than generate sequences in real-time.
Solution Approach 2:
The complete set of biological states is segmented into discrete time sequences organized as neural trajectories. This segmentation allows the system to maintain completeness by covering all plausible state sequences while improving efficiency through organized, searchable storage structures that enable rapid decoding.
Data Source
AI summary
Mechanisms including: receiving a first set of observed spike counts (FSoOSCs) for the spiking cells; determining a set of probabilities (SoPs) by: retrieving the SoPs from stored information (SI); or calculating the SopS based on the SI, wherein the SI regards possible biological states (BSs) of a subject, wherein each of the possible BSs belongs to at least one of a plurality of time sequences (PoTSs) of BSs, wherein each of the PoTSs of BSs corresponds to a possible action of the subject, and wherein each probability in the set of probabilities indicates a likelihood of observing a possible spike count for one of the plurality of spiking cells; identifying using a hardware processor a first identified BS of the subject from the possible BSs based on the FSoOSCs and the set of probabilities; and determining an action to be performed based on the first identified BS.