The Attentional Routing Circuit: Receptive Field Modulation Through Nonlinear Dendritic Interactions

We present a model of attentional routing called the Attentional Routing Circuit (ARC) that extends an existing model of spiking neurons with dendritic nonlinearities. Specifically, we employ the Poirazi et al. (2003) pyramidal neuron in a population coding framework. ARC demonstrates that the dendritic nonlinearities can be exploited to result in selective routing, with a decrease in the number of cells needed by a factor of ~5 as compared with a linear dendrite model.

Routing of attended information occurs through the modulation of feedforward visual signals by a cortical control signal specifying the location and size of the attended target. The model is fully specified in spiking single cells. Our approach differs from past work on shifter circuits by having more efficient control, and using a more biologically detailed substrate. Our approach differs from existing models that use gain fields by providing precise hypotheses about how the control signals are generated and distributed in a hierarchical model in spiking neurons. Further, the model accounts for numerous experimental findings regarding the timing, strength and extent of attentional modulation in ventral stream areas, and the perceived contrast enhancement of attended stimuli.

To further demonstrate the plausibility of ARC, it is applied to the attention experiments of Womelsdorf et al. (2008) and tested in detail. For the simulations, the model has only two free parameters that influence its ability to match the experimental data, and without fitting, we show that it can account for the experimental observations of changes in receptive field (RF) gain and position with attention in macaques. In sum, the model provides an explanation of RF modulation as well as testable predictions about nonlinear cortical dendrites and attentional changes of receptive field properties

Mind games : a reply to Daniela Hill

In her discussion of my original article, Hill reconstructs an argument I may have been making, argues that the distinction between natural and artificial minds is not exclusive, and suggests that my reliance on behaviour as a determiner of “mindedness” is a dangerous slip back to philosophical behaviourism. In reply, I note that the logical fallacy of which I’m accused (circular reasoning) is not the one present in the reconstruction of my argument (besides the point), and offer a non-fallacious reconstruction. More importantly, I note that logical analysis does not seem appropriate for the discussion in the target article. I then agree that natural and artificial minds do not make up two discrete categories for mindedness. Finally, I note that my research program belies any behaviourist motivations, and reiterate that even though behaviour is typically important for identifying minds, I do not suggest that it is a substitute for theory. However, the target article is not about such theory, but about the near-term likelihood of sophisticated artificial mind

On the eve of artificial minds

I review recent technological, empirical, and theoretical developments related to building sophisticated cognitive machines. I suggest that rapid growth in robotics, brain-like computing, new theories of large-scale functional modeling, and financial resources directed at this goal means that there will soon be a significant increase in the abilities of artificial minds. I propose a specific timeline for this development over the next fifty years and argue for its plausibility. I highlight some barriers to the development of this kind of technology, and discuss the ethical and philosophical consequences of such a development. I conclude that researchers in this field, governments, and corporations must take care to be aware of, and willing to discuss, both the costs and benefits of pursuing the construction of artificial minds

Inferential Role Semantics for Natural Language

Cognitive models have long been used to study linguistic phe-nomena spanning the domains of phonology, syntax, and se-mantics. Of these domains, semantics is somewhat unique inthat there is little clarity concerning what a model needs to beable to do in order to provide an account of how the mean-ings of complex linguistic expressions, such as sentences, areunderstood. To help address this problem, we introduce a tree-structured neural model that is trained to generate further sen-tences that follow from an input sentence. These further sen-tences chart out the “inferential role” of the input sentence,which we argue constitutes an important part of its meaning.The model is trained using the Stanford Natural Language In-ference (SNLI) dataset, and to evaluate its performance, we re-port entailment prediction accuracies on a set of test sentencesnot present in the training data. We also report the results of asimple study that compares human plausibility ratings for bothground-truth and model-generated entailments for a randomselection of sentences in this test set. Finally, we examine anumber of qualitative features of the model’s ability to gener-alize. Taken together, these analyses indicate that our modelis able to accurately account for important inferential relation-ships amongst linguistic expressions