Learning in Volatile Environments With the Bayes Factor Surprise
Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting ...
Enhanced Signal Detection by Adaptive Decorrelation of Interspike Intervals
Spike trains with negative interspike interval (ISI) correlations, in which long/short ISIs are more likely followed by short/long ISIs, are common in many neurons. They can be described by stochastic models with a spike-triggered adaptation variable. We ...
Predicting the Ease of Human Category Learning Using Radial Basis Function Networks
Our goal is to understand and optimize human concept learning by predicting the ease of learning of a particular exemplar or category. We propose a method for estimating ease values, quantitative measures of ease of learning, as an alternative to ...
Deeply Felt Affect: The Emergence of Valence in Deep Active Inference
The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a ...
Whence the Expected Free Energy?
The expected free energy (EFE) is a central quantity in the theory of active inference. It is the quantity that all active inference agents are mandated to minimize through action, and its decomposition into extrinsic and intrinsic value terms is key to ...
Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes
Formation of stimulus equivalence classes has been recently modeled through equivalence projective simulation (EPS), a modified version of a projective simulation (PS) learning agent. PS is endowed with an episodic memory that resembles the internal ...
A Novel Neural Model With Lateral Interaction for Learning Tasks
We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some ...
Stability Conditions of Bicomplex-Valued Hopfield Neural Networks
Hopfield neural networks have been extended using hypercomplex numbers. The algebra of bicomplex numbers, also referred to as commutative quaternions, is a number system of dimension 4. Since the multiplication is commutative, many notions and theories of ...
