A Distributed Framework for Large-Scale Data Analysis Using Bio-Inspired Sensory-Motor Algorithms

Abstract

In the process of detecting anomalies or deviations from expected behavior in continuously streaming data is complex and necessitates the development of effective models that can adaptively retrain over time. The human brain serves as a prime example of such a system, as it continuously learns throughout life, with past experiences that once seemed erroneous gradually becoming integrated into commonplace knowledge. While modern neural network models have made significant advancements in recognizing text and images, they have diverged considerably from the original neuron models and no longer represent a singular algorithm akin to that which our brains utilize. Networks such as LSTM (Long Short-Term Memory) can account for both distant and immediate past information; however, they exhibit limitations in their retrainability. We align with the theories proposed by Jeff Hawkins, a prominent researcher in the field of bio-inspired intelligence, whose team is developing innovative cortical algorithms that emulate current research on the functioning of the intelligent brain. In this context, vision and hearing can be conceptualized as sensors, with the data they provide being integrated within the model to generate continuous predictions for each input signal. In our article, we explore contemporary theories on this subject and present a custom implementation of these concepts using the Erlang programming language.