Topic: Bayesian framework

Hongjing Lu will present a new computational model of Probabilistic Analogical Mapping (PAM, in collaboration with Nick Ichien and Keith Holyoak) that finds systematic correspondences between inputs generated by machine learning. The model adopts a Bayesian framework for probabilistic graph matching, operating on semantic relation networks constructed from distributed representations of individual concepts (word embeddings created by Word2vec) and of relations between concepts (created by our BART model). We have used PAM to simulate a broad range of phenomena involving analogical mapping by b

Exploring the origins of animal and human behavior within cortical circuits remains a puzzle awaiting resolution. It is established that when animals and humans compare two stimuli (d1 and d2) separated by a delay interval (Fig. 1a), their perception is influenced by a perceptual bias, characterized by a contraction in the perception of the first stimulus towards its distribution’s mean (highlighted in red in Fig. 1a). To shed light on this effect, recurrent neural networks (RNNs) of spiking neurons were trained, and their behavior and firing activity were examined in comparison to those obser