Dissecting Components of Network Spiking Activity

I have long been interested in neural attributes that explain whole network spiking activity (a key observable thanks to tremendous advances in micro-electrode array recording technologies). The range of complexities are staggering, with issues arising from differences in "spontaneous" to sensory-evoked time periods, the specifics of sensory stimulation, prescribed time windows, etc. The dynamics of spike statistics is strongly implicated in function, i.e., scale-free `criticality' <=> info transmission/storage. Along with some olfactory papers, see a b c (where we use linear response theory on recurrent heterogeneous networks).

Olfactory System

Simultaneous dual-array recordings by collaborators (Shew Lab) have resulted in many fascinating research questions. Cells in the olfactory bulb (OB) appear to be selective for odor modality (ortho vs retro). How might the selectivity be preserved in cortex (CX)? See 1 2, and Michelle's paper s ) .

Dynamics of Heterogeneity

The probability density of phase differences of 2 cells in the same coupled heterogeneous network, captured by an asymptotic calculation with weak noise, coupling, and correlation (see Ly SIAM J Appl Dyn Syst 2014).

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Theory from Ly JCNS 2015 and application here. Some ideas were motivated by electric fish data by the Marsat Lab; see Kyle's work too.

Cardiovascular Modeling

The sinus node (SAN) pacemakers are heterogeneous and entrains the rest of the heart. Realistic models with >30 variables for each cell present mathematical challenges to characterize dynamics that we have tackled (Ly & Weinberg J Theor Biol 2018). We hope to extend models and analyses to other regions; pinpointing dynamics of detailed models allows theoretical explorations of specific mutations (e.g., ion channels) that can ultimately help cure cardiac diseases.

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Automaticity is possible via ephaptic coupling in cardiac myocytes, see Ly & Weinberg 22.

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