A central task in understanding how neurons collectively process information is to map how neurons influence each other in local cortical networks. As defined here, local cortical networks will consist of tens to hundreds of neurons. Influence will be defined as how well knowledge of activity in one neuron will allow the activity in another neuron to be predicted. Three methods for measuring influence between neurons will be explored. To assess these methods, they will be used on data from simple, and then realistic, models of cortical networks where the underlying connectivity structure is known. After refinement, the methods will be applied to recordings from hundreds of cortical neurons in small slice cultures of brain tissue. Over 100 cortical neurons at a time will be recorded through the use of an advanced, 512 electrode array. In addition, measures of influence will be applied to data taken from 16 wire electrodes placed in behaving rats. These in vivo recordings will serve as a first step toward linking influence maps in cortical networks to behavior. This research is expected to provide new knowledge that could aid the design of brain-like computing devices. In addition, it could ultimately be used as a tool to identify differences in influence patterns between healthy and pathological brains.
The three methods for measuring influence will include directed information, transfer entropy, and Granger causality. Special care will be taken to identify situations where these measurements may produce false positive connections. These include cases where two neurons are driven by a common source at different delays, and cases where one neuron influences another neuron indirectly through an intercalated neuron. Such false positive connections will be identified and corrected, to the extent possible, by comparing raw pairwise measures of influence with conditional measures of influence. Simulations will also provide an estimate of how often neurons outside the recorded population can contribute to false positive connections. These estimates will be used to place confidence limits on the influence maps extracted from actual data. In neurons where influences converge, synergistic interactions between influences will be measured. The map of influence will serve to identify locations within the network where synergistic transformations of information, or computations, occur.
