Attention modeled as a two-dimensional neural resource
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1
University of Texas at Austin, United States
Although hundreds or even thousands of experiments invoke the concept of ’attention,’ there is scant specification of its function at the neural level beyond the increased firing of neurons with respect to different task and stimuli conditions. We attempt a more precise computational definition of neural attention with the aim of accounting for data in different kinds of neural recording experiments and find we require two distinct descriptive axes. One measures the numbers of neurons in a network that are allocated to a task. In a network processing quantitative data, we show that increased numerical precision can be traced to additional neurons, and consequently the increased number of spikes in any individual neuron. A second axis reflects the effects of competition between two or more tasks. Experimental data can be simply explained by positing that individual neurons’ spike output is time-shared between tasks. We make these assertions concrete by demonstrating them in a model circuit that learns striate cortex receptive fields from LGN data using gamma-phase coding [1] and a probabilistic version of matching pursuit [2]. gamma-phase coding represents quantitative data with each spike by using a small (0~5ms) lag with respect to a gamma oscillation peak. Probabilistic matching pursuit routs this numerical data through different neurons at different cycles in the gamma signal. Thus while spikes appear random from the vantage point of any particular cell, a deterministic numerical message is sent through the network. A large number of experiments model attention as a gain change wherein the receptive field of a neuron as measured by its peri-stimulus histogram is modified by a scalar multiplier [3]. gamma-phase coding allows numerical data to be sent with different levels of precision. Thus at any instant, a more precise numerical code can be sent by including more neurons with longer lags. However our simulations show that a side effect of this increase in precision is that the receptive field of a neuron, as measured by the peri-stimulus histogram, represents a scalar gain change. When a neuron has a complex receptive field such as those found in cortical area V4, attending to a subfield can increase its firing rate, but attending outside of its receptive field results in a normalized response to all its subfields’ components. For example in the classic experiment of Desimone et al [4] recording from a neuron in V4 with subfields A and B, attending to A produced spikes s(A), but attending outside the receptive field produced spikes (s(A) +s(B))/2. A single circuit can explain this phenomenon if its neurons’ subfields are coded by separate gamma oscillators that differ in overall phase. Thus in the case of attending to A, the stimulus successfully monopolizes a neurons gamma-phase time slots, whereas in attending outsides of the neurons RF, A and B divide the time slots between them.
References
1. TRENDS in Neurosciences 30, 309 (2007)
2. PLoS Comput Biol 5(5): e1000373 (2009)
3. Neuron, 23, 765, (1999)
4. Annual Rev. Neuroscience, 18, 193 (1995)
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Poster and Short Oral Presentation
Topic:
Poster session II
Citation:
Ballard
D
(2010). Attention modeled as a two-dimensional neural resource.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00164
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Received:
02 Mar 2010;
Published Online:
02 Mar 2010.
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Correspondence:
Dana Ballard, University of Texas at Austin, Austin, United States, dana@cs.utexas.edu