Michigan State University - Research Groups

(Excerpt from the Book Introduction - Chapter 1): In Chapter 5, Eldawlatly and Oweiss take a more general approach to identifying distributed neural circuits by inferring connectivity between neurons locally within and globally across multiple brain areas, distinguishing between two types of connectivity: functional and effective. They first review techniques that have been classically used to infer connectivity between various brain regions from continuous-time signals such as fMRI and EEG data.Given that inferring connectivity among neurons is more challenging because of the stochastic and discrete nature of their spike trains and the large dimensionality of the neural space, the authors provide an in-depth focus on this problem using graphical techniques deeply rooted in statistics and machine learning. They demonstrate that graphical models offer a number of advantages over other techniques, for example by distinguishing between mono-synaptic and polysynaptic connections and in inferring inhibitory connections among other features that existing methods cannot capture. The authors demonstrate the application of their method in the analysis of neural activity in the medial prefrontal cortex (mPFC) of an awake, behaving rat performing a working memory task. Their results demonstrate that the networks inferred for similar behaviors are strongly consistent and exhibit graded transition between their dynamic states during the recall process, thereby providing additional evidence in support of the long withstanding Hebbian cell assembly hypothesis (Hebb, 1949).