Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-Abbreviations: DCM, dynamic causal Model(ing); EEG, electroencephalography; ERF, event-related field; ERP, event-related potential;

The incoming speech stream contains a rich amount of temporal information. In particular, information on slow time scales, the delta and theta band (125 – 1000 ms, 1 – 8 Hz), corresponds to prosodic and syllabic information while information on faster time scales (20-40 ms, 25 – 50 Hz) corresponds to feature/phonemic information. In order for speech perception to occur, this signal must be segregated into meaningful units of analysis and then processed in a distributed network of brain regions. Recent evidence suggests that low frequency phase information in the delta and theta bands of the Magnetoencephalography (MEG) signal plays an important role for tracking and segmenting the incoming signal into units of analysis. This thesis utilized a novel method of analysis, Mutual Information (MI) to characterize the relative information contributions of these low frequency phases. Reliable information pertaining to the stimulus was present in both delta and theta bands (3 – 5 Hz, 5 – 7 Hz) and information within each of these three sub-bands was independent of each other. A second experiment demonstrated that the information present in these bands differed significantly for speech and a non-speech control condition, suggesting that contrary to