Christopher Parisien

Solving the problem of negative synaptic weights in cortical models

In cortical neural networks, connections from a given neuron are either inhibitory or excitatory but not both. This constraint is often ignored by theoreticians who build models of these systems. There is currently no general solution to the problem of converting such unrealistic network models into biologically plausible models that respect this constraint. We demonstrate a constructive transformation of models that solves this problem for both feedforward and dynamic recurrent networks. The resulting models give a close approximation to the original network functions and temporal dynamics of the system, and they are biologically plausible. More precisely, we identify a general form for the solution to this problem. As a result, we also describe how the precise solution for a given cortical network can be determined empirically.

An Incremental Bayesian Model for Learning Syntactic Categories

We present an incremental Bayesian model for the unsupervised learning of syntactic categories from raw text. The model draws information from the distributional cues of words within an utterance, while explicitly bootstrapping its development on its own partially-learned knowledge of syntactic categories. Testing our model on actual child-directed data, we demonstrate that it is robust to noise, learns reasonable categories, manages lexical ambiguity, and in general shows learning behaviours similar to those observed in children.

Robosemantics: How Stanley the Volkswagen Represents the World

One of the most impressive feats in robotics was the 2005 victory by a driverless Volkswagen Touareg in the DARPA Grand Challenge. This paper discusses what can be learned about the nature of representation from the car’s successful attempt to navigate the world. We review the hardware and software that it uses to interact with its environment, and describe how these techniques enable it to represent the world. We discuss robosemantics, the meaning of computational structures in robots. We argue that the car constitutes a refutation of semantic arguments against the possibility of strong artificial intelligence.

Perspective-taking behavior as the probabilistic weighing of multiple domains.

Our starting point is the apparently-contradictory results in the psycholinguistic literature regarding whether, when interpreting a definite referring expressions, listeners process relative to the common ground from the earliest moments of processing. We propose that referring expressions are not interpreted relative solely to the common ground or solely to ones Private (or egocentric) knowledge, but rather reflect the simultaneous integration of the two perspectives. We implement this proposal in a Bayesian model of reference resolution, focusing on the models predictions for two prior studies: Keysar, Barr, Balin, and Brauner (2000) and Heller, Grodner and Tanenhaus (2008). We test the models predictions in a visual-world eye-tracking experiment, demonstrating that the original results cannot simply be attributed to different perspective-taking strategies, and showing how they can arise from the same perspective-taking behavior.