Wednesday, September 29th:
Simona Doboli , Computer Science Department, Hofstra University
Title: A Computational Model for Learning Unknown Sequences of Relevant Patterns Embedded in Distractors
Abstract: Sequential information processing is ubiquitous throughout the nervous system. Speech recognition, language processing, spatial and motor tasks all rely on various forms of sequential processing. Sometimes, the patterns of a sequence are unknown a priori and may be interspersed with other, irrelevant information. The talk will give an overview of some of the existing neural computational models for sequence learning tasks, and will present in detail a model for discovering unknown sequences with relevant patterns embedded in distractors.
Wednesday, October 27th:
Vincent Brown , Psychology Department, Hofstra University
Title: An Associative Memory Model of Group Brainstorming
Abstract: Groups and teams are popular and often-utilized in government, business, and industry for brainstorming, problem solving, and decision making. While groups may be effective in performing many tasks, laboratory evidence makes it clear that when it comes to idea generation, groups do not perform as well as an equal number of individuals brainstorming alone. There are a number of social-psychological reasons for this, including evaluation apprehension, free riding, comparison and matching processes, and production blocking. However, from a cognitive-psychological viewpoint there are theoretical reasons for believing that group brainstorming could be quite effective if the inhibitory social factors could be overcome. Computer simulations of a model based on the concept of associative memory suggest specific conditions under which brainstorming in a group could be more effective than brainstorming alone. One key aspect of the model is the notion of accessibility of ideas, which predicts that many creative ideas may be more easily generated in the context of the diverse ideas of other group members than in the familiar context of an individual's own set of ideas. Recent experimental results support many aspects of this model, which has been a useful tool for organizing and explaining much of the data on group brainstorming.
Wednesday, November 24th:
Hua Tang , ECE Department, SUNY Stony Brook
Title: Refinement based Synthesis of Continuous-Time Analog Filters Through Successive Domain Pruning, Plateau Search and Adaptive Sampling