Abstract
We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow "vertical" translation between physiological properties of neural systems and emergent "whole-system" performance-enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data.
Highlights
We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior
These results are consistent with the selective deficit in the right posterior parietal cortex (PPC) patients being linked to poor arousal, which is improved in the arousal condition
The four worst performing left hemisphere/occipital patients showed 0% improvement in the arousal condition, whereas four right PPC patients with a mean overall score matched to those of the worst left hemisphere/ occipital patients had a mean improvement of 15% on two-item trials in the arousal condition
Summary
We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow “vertical” translation between physiological properties of neural systems and emergent “whole-system” performance— enabling psychological results to be simulated from implemented networks and inferences to be made from simulations concerning processing at a neural level These models emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. One of the difficulties is that cognition can be described at many different levels— from the high-level computational principles that may shape the landscape within which processes must function, through to the physiological principles by which neurons operate. A variety of empirical approaches have been proposed in attempts to break down whole-system behavior into underlying component
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