Biological neural network system and methods

A method for using a dictionary of neural-based embeddings to perform a task using a system comprising:

• (i) a large language model (LLM);
• and (ii) at least one processor, the method comprising:
• using the system to perform:
• receiving text comprising one or more tokens to be processed by the LLM in furtherance of performing a task;
• accessing a dictionary of neural-based embeddings from the memory, the dictionary of neural-based embeddings comprising a respective neural-based embedding for each of the one or more tokens;
• determining, for each token of the one or more tokens, a corresponding neural-based embedding using the dictionary of neural-based embeddings by mapping the respective token to the respective neural-based embedding in the dictionary of neural-based embeddings, the dictionary of neural based embeddings being generated by stimulating a biological neural network (BNN) comprising neurons arranged on a multi-electrode array with stimulation patterns corresponding to respective tokens in a dictionary of tokens;
• 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.

A method for using a dictionary of neural-based embeddings to process text using a large language model (LLM), the LLM having an embedding layer and a plurality of transformers, each of the transformers having one or more attention heads, the method comprising:

• using at least one computer hardware processor to perform:
• generating the dictionary of neural-based embeddings using a biological and artificial neural network (BANN) system comprising (i) a multi-electrode array (MEA);
• (ii) a biological neural network (BNN) comprising neurons arranged on the MEA;
• (iii) an artificial neural network (ANN) trained to process multiple features derived from at least one response of the BNN to a stimulation pattern;
• and (iv) the least at one processor computer hardware at least in part by:
• accessing a dictionary of neural-based embeddings from the memory, the dictionary of neural-based embeddings comprising a respective neural-based embedding for each token of the one or more tokens of the text;
• determining a corresponding token embedding for each of the one or more tokens by using parameters of the embedding layer to obtain token embeddings, the parameters of the embedding layer determined using the dictionary of neural-based embeddings, by mapping the respective token to the respective neural-based embedding in the dictionary of neural-based embeddings, the dictionary of neural-based embeddings having been generated by stimulating a biological neural network (BNN) comprising neurons arranged on a multi-electrode array with stimulation patterns corresponding respective tokens in a dictionary of tokens;
• and
• processing the obtained token embeddings using the plurality of transformers part of the LLM to obtain LLM output.