Functional Synapse Determination via Probabilistic Structural Filtering
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Solution Overview
Problem
Current models fail to accurately determine functional synapses from structural connections in neuronal circuits due to the discrepancy between structural and functional connectivity, requiring a computer-implemented method to identify a subset of structural connectivity for functional activation.
Innovation Solution
A processor-based system that determines functional synapses by admitting structural touches as potential synapses with a first probability and selecting them as predetermined synapses with a second probability, which depends on the number of potential synapses, while leaving a portion of connections unused for activation by plasticity mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all structural touches are considered as potential synapses, then the completeness of structural connectivity is improved, but the accuracy of functional connectivity determination deteriorates
Solution Approach 1:
The patent extracts only the functional subset of synapses from the complete set of structural touches. By applying probabilistic filtering and plasticity mechanism simulations, the system identifies and isolates the specific connections that are functionally active, separating them from the larger pool of structural but non-functional contacts.
Solution Approach 2:
The patent changes the parameter of synapse selection from deterministic (all structural touches) to probabilistic (random selection with defined probabilities). This parameter change allows the system to transition from complete structural connectivity to a more accurate representation of functional connectivity by introducing probability-based filtering.
2Device complexity
If a fixed probability is used to select functional synapses, then the simplicity of the model is improved, but the adaptability to different neuronal circuits deteriorates
Solution Approach 1:
The patent introduces dynamic probability parameters that can be adjusted based on the specific characteristics of different neuronal circuits. Instead of using fixed probabilities, the system allows probability values to vary according to circuit-specific features, enabling adaptation to diverse neuronal architectures while maintaining the probabilistic framework.
Solution Approach 2:
The patent enables parameter adjustment by allowing the probabilities in the multi-step selection process to be modified based on experimental data and circuit characteristics. This parameter flexibility allows the same basic model structure to adapt to different neuronal circuits by changing the probability values at each selection step.
3Measurement precision
If multiple probabilistic selection steps are implemented, then the accuracy of functional synapse determination is improved, but the computational complexity increases
Solution Approach 1:
The patent segments the synapse selection process into multiple distinct probabilistic steps, where each step filters the pool of potential synapses further. This segmentation allows the complex task of identifying functional synapses to be broken down into manageable sequential operations, improving accuracy through progressive filtering.
Solution Approach 2:
The patent performs preliminary probabilistic filtering in early selection steps to reduce the pool of candidate synapses before more computationally intensive analysis. By eliminating non-functional connections early in the process, the system reduces the computational burden of subsequent steps while maintaining high accuracy in the final functional synapse identification.
Data Source
AI summary
Computer-implemented methods, software, and systems for determining functional synapses from given structural touches between cells in a neuronal circuit are described. One computer-implemented method for determining functional synapses from predetermined synapses of connections between two cells in a neuronal circuit, includes determining, from the predetermined synapses, the functional synapses by leaving a portion of the connections unused, e.g. for activation by plasticity mechanisms.