Hippocampus-Inspired Cognitive Architecture (HICA) for Operant Conditioning

TL;DR

This paper introduces HICA, a hippocampus-inspired neural architecture that models operant conditioning with few trials by combining cortical-like modules and subcortical brain region modules for predictive learning and instinctive behaviors.

Contribution

The paper presents a novel neural architecture, HICA, that integrates cortical and subcortical modules to enable efficient operant conditioning learning in artificial agents.

Findings

HICA can learn new behaviors in few trials, mimicking mammalian operant conditioning.

Modules in HICA update locally, allowing heterarchical network structures.

HICA's hippocampus module effectively simulates and predicts top-level signals for learning.

Abstract

The neural implementation of operant conditioning with few trials is unclear. We propose a Hippocampus-Inspired Cognitive Architecture (HICA) as a neural mechanism for operant conditioning. HICA explains a learning mechanism in which agents can learn a new behavior policy in a few trials, as mammals do in operant conditioning experiments. HICA is composed of two different types of modules. One is a universal learning module type that represents a cortical column in the neocortex gray matter. The working principle is modeled as Modulated Heterarchical Prediction Memory (mHPM). In mHPM, each module learns to predict a succeeding input vector given the sequence of the input vectors from lower layers and the context vectors from higher layers. The prediction is fed into the lower layers as a context signal (top-down feedback signaling), and into the higher layers as an input signal (bottom-up feedforward signaling). Rewards modulate the learning rate in those modules to memorize meaningful sequences effectively. In mHPM, each module updates in a local and distributed way compared to conventional end-to-end learning with backpropagation of the single objective loss. This local structure enables the heterarchical network of modules. The second type is an innate, special-purpose module representing various organs of the brain's subcortical system. Modules modeling organs such as the amygdala, hippocampus, and reward center are pre-programmed to enable instinctive behaviors. The hippocampus plays the role of the simulator. It is an autoregressive prediction model of the top-most level signal with a loop structure of memory, while cortical columns are lower layers that provide detailed information to the simulation. The simulation becomes the basis for learning with few trials and the deliberate planning required for operant conditioning.