MEA-Based Biological Neural Network Embeddings for LLMs
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Solution Overview
Problem
The increasing demand for artificial neural networks (ANNs) in various applications is accompanied by significant time, energy, and data requirements for training, which existing technologies struggle to efficiently address.
Innovation Solution
A biological and artificial neural network (BANN) system is employed, comprising a multi-electrode array (MEA) with biological neural networks (BNNs) and trained statistical models, which encodes input signals as stimulation patterns, measures responses, and processes them with ANNs to perform tasks efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If artificial neural networks are used to process complex problems, then problem-solving capability is improved, but training time and energy consumption increase
Solution Approach 1:
The system segments the neural network training process into two distinct phases: (1) initial training of an artificial neural network using conventional methods, and (2) deployment of trained weights to a biological neural network for inference. This segmentation allows the energy-intensive training to be performed once, while subsequent processing leverages the energy-efficient biological system.
Solution Approach 2:
The patent introduces an intermediary system that bridges artificial and biological neural networks. The ANN serves as a mediator that processes input data and generates stimulation patterns for the BNN, while the BNN's biological responses are converted back into usable output. This intermediary approach allows leveraging both the programmability of ANNs and the energy efficiency of BNNs.
2Adaptability or versatility
If artificial neural networks are used to process complex problems, then problem-solving capability is improved, but training time increases
Solution Approach 1:
The system segments the neural network training process into two distinct phases: (1) initial training of an artificial neural network using conventional methods, and (2) deployment of trained weights to a biological neural network for inference. This segmentation allows the energy-intensive training to be performed once, while subsequent processing leverages the energy-efficient biological system.
Solution Approach 2:
The patent applies preliminary action by pre-training the artificial neural network before deploying it to the biological system. The ANN is trained in advance using conventional methods, and its learned weights are then transferred to the BNN. This preliminary training phase enables the biological network to perform complex processing without requiring repeated training, thus reducing overall time loss.
3Use of energy by moving object
If biological neural networks are used, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The system merges artificial and biological neural networks into a hybrid architecture that combines the strengths of both approaches. The ANN provides programmability and ease of training, while the BNN contributes energy efficiency and biological processing capabilities. This merging creates a unified system that achieves energy efficiency without sacrificing too much in terms of operational complexity.
Solution Approach 2:
The patent introduces an intermediary system that bridges artificial and biological neural networks. The ANN serves as a mediator that processes input data and generates stimulation patterns for the BNN, while the BNN's biological responses are converted back into usable output. This intermediary approach allows leveraging both the programmability of ANNs and the energy efficiency of BNNs.
4Measurement precision
If biological neural networks are used, then accuracy is improved, but device complexity increases
Solution Approach 1:
The system merges artificial and biological neural networks into a hybrid architecture that combines the strengths of both approaches. The ANN provides programmability and ease of training, while the BNN contributes energy efficiency and biological processing capabilities. This merging creates a unified system that achieves energy efficiency without sacrificing too much in terms of operational complexity.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the stimulation patterns applied to the biological neural network based on the input data and ANN-generated control signals. By dynamically modifying stimulation parameters (amplitude, frequency, duration), the system optimizes the BNN's processing accuracy for different tasks while managing the inherent device complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The BANN system reduces the power and training iterations required for tasks, offering improved accuracy and energy efficiency compared to ANNs alone, particularly in applications like time series data analysis, video processing, natural language processing, and real-time decision making.
Implementation Method 1
stimulating the BNN by using the MEA to generate electrical signals in accordance with the at least one stimulation pattern
Implementation Method 2
measuring, using the MEA, at least one response of the BNN that is responsive to the stimulating
Data Source
AI summary
Techniques for creating and using a dictionary of neural-based embeddings to perform a task using a system. The system comprises a large language model (LLM) and at least one processor. The method comprises using the system to perform: receiving text comprising one or more tokens to be processed by the LLM in furtherance of performing a task; determining, for each token of the one or more tokens, a corresponding neural-based embedding using the dictionary of neural-based embeddings, the dictionary of neural based embeddings being generated using a biological neural network (BNN) comprising neurons arranged on a multi-electrode array; processing the one or more tokens using the LLM by inputting to the LLM the determined corresponding neural-based embeddings for each of the one or more tokens to obtain an output; and using the output in furtherance of performing the task.