Neuronal interactions improve cortical population coding of movement direction

Journal ArticleInteractions among groups of neurons in primary motor cortex (MI) may convey information about motor behavior. We investigated the information carried by interactions in MI of macaque monkeys using a novel multielectrode array to record simultaneously from 12-16 neurons during an arm-reaching task. Pairs of simultaneously recorded cells revealed significant correlations in their trial-to-trial firing rate variation when estimated over broad (600 msec) time intervals. This covariation was only weakly related to the preferred directions of the individual MI neurons estimated from the firing rate and did not vary significantly with interelectrode distance. Most significantly, in a portion of cell pairs, correlation strength varied with the direction of the arm movement. We evaluated to what extent correlated activity provided additional information about movement direction beyond that available in single neuron firing rate. A multivariate statistical model successfully classified direction from single trials of neural data. However, classification was consistently better when correlations were incorporated into the model as compared to one in which neurons were treated as independent encoders. Information-theoretic analysis demonstrated that interactions caused by correlated activity carry additional information about movement direction beyond that based on the firing rates of independently acting neurons. These results also show that cortical representations incorporating higher order features of population activity would be richer than codes based solely on firing rate, if such information can exploited by the nervous system

Queueing Network Modeling of Human Performance and Mental Workload in Perceptual-Motor Tasks.

Integrated with the mathematical modeling approaches, this thesis uses Queuing Network-Model Human Processors (QN-MHP) as a simulation platform to quantify human performance and mental workload in four representative perceptual-motor tasks with both theoretical and practical importance: discrete perceptual-motor tasks (transcription typing and psychological refractory period) and continuous perceptual-motor tasks (visual-manual tracking and vehicle steering with secondary tasks). The properties of queuing networks (queuing/waiting in processing information, serial and parallel information processing capability, overall mathematical structure, and entity-based network arrangement) allow QN-MHP to quantify several important aspects of the perceptual-motor tasks and unify them into one cognitive architecture. In modeling the discrete perceptual-motor task in a single task situation (transcription typing), QN-MHP quantifies and unifies 32 transcription typing phenomena involving many aspects of human performance--interkey time, typing units and spans, typing errors, concurrent task performance, eye movements, and skill effects, providing an alternative way to model this basic and common activities in human-machine interaction. In quantifying the discrete perceptual-motor task in a dual-task situation (psychological refractory period), the queuing network model is able to account for various experimental findings in PRP including all of these major counterexamples of existing models with less or equal number of free parameters and no need to use task-specific lock/unlock assumptions, demonstrating its unique advantages in modeling discrete dual-task performance. In modeling the human performance and mental workload in the continuous perceptual-motor tasks (visual-manual tracking and vehicle steering), QN-MHP is used as a simulation platform and a set of equations is developed to establish the quantitative relationships between queuing networks (e.g., subnetwork s utilization and arrival rate) and P300 amplitude measured by ERP techniques and subjective mental workload measured by NASA-TLX, predicting and visualizing mental workload in real-time. Moreover, this thesis also applies QN-MHP into the design of an adaptive workload management system in vehicles and integrates QN-MHP with scheduling methods to devise multimodal in-vehicle systems. Further development of the cognitive architecture in theory and practice is also discussed.Ph.D.Industrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/55678/2/changxuw_1.pd