Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in “Appendix”

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

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

Instrumentation, Monitoring, and Modeling of the I-35W Bridge

The new I-35W Bridge was instrumented incorporating "smart bridge technology" by Figg Engineering Group in conjunction with Flatiron-Manson. The purpose of the instrumentation was to monitor the structure during service, and to use this information to investigate the design and performance of the bridge. Instrumentation included static sensors (vibrating wire strain gages, resistive strain gages and thermistors in the foundation, bridge piers, and superstructure, as well as fiber optic sensors and string potentiometers in the superstructure) and dynamic sensors (accelerometers in the superstructure). Finite element models were constructed, taking into account measured material properties, to further explore the behavior of the bridge. The bridge was tested using static and dynamic truck load tests, which were used, along with continually collected ambient data under changing environmental conditions, to validate the finite element models. These models were applied to gain a better understanding of the structural behavior, and to evaluate the design assumptions presented in the Load Rating Manual for the structure. This report documents the bridge instrumentation scheme, the material testing, finite element model construction methodology, the methodology and results of the truck tests, validation of the models with respect to gravity loads and thermal effects, measured and modeled dynamic modal characteristics of the structure, and documentation of the investigated assumptions from the Load Rating Manual. It was found that the models accurately recreated the response from the instrumented bridge, and that the bridge had behaved as expected during the monitoring period.Department of Civil Engineering, University of Minnesota; Minnesota Department of Transportatio