Supervisory neuron for continuously adaptive neural network
A system and method for real-time time series forecasting using a compound large codeword model with integrated supervisory neurons. The system processes diverse inputs through adaptive codebook generation and codeword allocation. A projection network fuses different data types, creating unified representations for a latent transformer-based machine learning core. The core contains local neural network regions of interconnected operational neurons, monitored by supervisory neurons. These supervisory neurons receive activation data from operational neurons, perform real-time statistical analysis, determine necessary structural modifications, and initiate their implementation during operation. This architecture enables efficient handling of multi-modal data, capturing complex relationships between different input types. The combination of adaptive codebook generation and the supervisory neuron system ensures responsiveness to evolving data patterns and task requirements. This approach provides more accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure to maintain optimal performance.
1 . A deep learning system for real-time time series forecasting using a compound large codeword model, comprising one or more computers with executable instructions that, when executed, cause the deep learning system to:
receive real-time time series data comprising a plurality of data types from a plurality of data sources;
allocate codewords to each data input using a plurality of adaptive codebooks, wherein codewords are mapped to a corresponding codebook specific to each data type;
fuse codewords of dissimilar data types together into a single codeword representation using a projection network that preserves inter-relationships between the dissimilar data types;
process the single codeword representation through a machine learning core comprising a transformer-based architecture, wherein the machine learning core comprises:
a plurality of operational neurons interconnected to form multiple local neural network regions within the machine learning core; and
a plurality of supervisory neurons, each supervisory neuron operatively connected to a respective local neural network region and configured to:
receive activation data from the operational neurons in real-time during inference operations, wherein the activation data comprises weights, biases, inputs, and outputs from each monitored neuron collected over multiple time cycles;
perform statistical analysis on the received activation data including temporal and spatial Fourier transforms to identify frequency components in neuron activations and wavelet analysis for multi-scale examination of activation patterns to identify patterns and anomalies in activation patterns over time;
determine, based on the statistical analysis, one or more structural modifications to the respective local neural network regions by maintaining a state-action value function updated based on performance impact of past modifications and comparing current activation patterns against a historical record database implemented as a circular buffer storing past activation pattern;
initiate implementation of the determined structural modifications during operation of the respective local neural network region using gradient-based optimization techniques to smoothly transition the network structure while ensuring stability during modifications;
monitor performance of the respective local neural network region before and after implementing the structural modifications; and
revert modifications that do not improve performance;
generate short-term forecasts based on a plurality of single codeword representations; and
output the short-term forecasts as a continuously updated time series prediction;
wherein the adaptive codebooks are continuously updated based on the real-time time series data to maintain prediction accuracy as data patterns evolve over time.
2 . The system of claim 1 , wherein the machine learning core uses a latent transformer-based architecture.
3 . The system of claim 1 , wherein the supervisory neuron is further configured to:
maintain a historical record of activation patterns in the local neural network region;
compare current activation patterns to the historical record; and
determine structural modifications based on identified changes in activation patterns over time.
4 . The system of claim 1 , wherein the statistical analysis performed by the supervisory neuron comprises calculating at least one of average activation levels, activation frequency, activation patterns, or inter-neuron correlation.
5 . The system of claim 1 , wherein the supervisory neuron is further configured to adjust parameters of the operational neurons based on the statistical analysis.
6 . The system of claim 1 , wherein initiating implementation of the determined structural modifications comprises sending control signals to a network modification module.
7 . The system of claim 1 , wherein the local neural network region is part of a larger neural network, and wherein the supervisory neuron is configured to communicate with other supervisory neurons monitoring other regions of the larger neural network.
8 . A method for real-time time series forecasting using a compound large codeword model comprising the steps of:
receiving real-time time series data comprising a plurality of data types from diverse data sources;
allocating codewords to each data input using a plurality of adaptive codebooks, wherein codewords are mapped to a corresponding codebook specific to each data type;
fusing codewords of dissimilar data types together into a single codeword representation using a projection network that preserves inter-relationships between the dissimilar data types;
processing the single codeword representation through a machine learning core comprising a transformer-based architecture, wherein the machine learning core comprises a plurality of operational neurons interconnected to form multiple local neural network regions;
monitoring, by a plurality of supervisory neurons each supervisory neuron operatively connected to a respective local neural network region, activation data from the operational neurons in real-time during inference, the activation data comprising weights, biases, inputs, and outputs collected over multiple time cycle;
performing, by the supervisory neurons, statistical analysis on the monitored activation data including temporal and spatial Fourier transforms to identify frequency components in neuron activations and wavelet analysis for multi-scale examination of activation patterns to identify patterns and anomalies in activation patterns over time;
determining, based on the statistical analysis, one or more structural modifications to the respective local neural network regions to optimize performance for processing the fused codeword representations, wherein determining comprises maintaining a state-action value function updated based on the performance impact of past modifications and comparing current activation patterns against a historical record database implemented as a circular buffer storing past activation patterns, and wherein the structural modifications include at least one of neuron splitting, neuron pruning, neurogenesis, or connection modification;
initiating implementation of the determined structural modifications during operation of the respective local neural network regions using gradient-based optimization techniques to smoothly transition the network structure while ensuring stability during modifications and without interrupting ongoing processing operations;
monitoring performance of the respective local neural network regions before and after implementing the structural modifications;
reverting modifications that do not improve performance;
generating short-term forecasts based on a plurality of single codeword representations;
outputting the short-term forecasts as a continuously updated time series prediction; and
continuously updating the adaptive codebooks based on the real-time time series data to maintain prediction accuracy as data patterns evolve over time.
9 . The system of claim 8 , wherein the machine learning core uses a latent transformer-based architecture.
10 . The method of claim 8 , further comprising:
tracking changes in activation patterns over time;
identifying trends or anomalies in the activation patterns; and
determining structural modifications based on the identified trends or anomalies.
11 . The method of claim 8 , wherein the structural modifications comprise dynamically adjusting the number of operational neurons in the local neural network region.
12 . The method of claim 8 , further comprising communicating information about local structural modifications to a higher-level supervisory component of a larger neural network.