The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain

By Ariel Zylberberg, Diego Fernández Slezak, Pieter R. Roelfsema, Stanislas Dehaene, Mariano Sigman. 2010

131 citations recorded by the source corpus.

The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

On the Binding Problem in Artificial Neural Networks (2020, methodology): “Several mechanisms have been proposed that range from static binding using conjunction cells (Ghose and Maunsell, 1999) to dynamic information routing through dedicated circuitry (Olshausen et al., 1993; Zylberberg et al., 2010) or attention using common location tags (Reynolds and Desimone, 1999; R”
Toward a computational theory of conscious processing (2013, unknown): “These effects have been related to a global parietal and prefrontal network [94,98], and have been partially captured in simulations of spiking neurons [69,99] (Figure 1).”
Communication dynamics in complex brain networks (2017, background): “…processing is important, but information that travels through all possible paths and walks can lead to an overloaded and congested sys-tem 53,107 , in which loss and corruption of information are more likely and the energetic cost required to ensure the spread of information is exceedingly…”
What must a global theory of cortex explain? (2014, methodology): “We shall make references to some recent examples [6 ,7 ,8 ,9 ] in order to contrast some of the currently pursued alternatives.”