@article{Representation:3399,
      recid = {3399},
      author = {Levy, Maayan},
      title = {Network Representation of Stimuli in Murine Visual Cortex},
      publisher = {The University of Chicago},
      school = {Ph.D.},
      address = {2021-08},
      pages = {181},
      abstract = {To meet the demands of survival, the central nervous  system has to simultaneously encode the external world and  internal states in the spiking activity of neurons.  Populations of neurons are connected by non-random synaptic  wiring, shaped by previous experience, and in turn give  rise to variable yet correlated spiking activity. This  works attempts to relate the structure of spiking activity  to its underlying, interconnected substrate on the one  hand, and to the external variables they presumably encode  on the other. To do so, statistical dependencies in the  activity of neurons are summarized as functional networks  (FNs), where neurons are nodes and the statistical  regularities between them are edges. In this dissertation,  FNs are utilized in encoding, decoding and both generative  and discriminative models to gain insights into the circuit  level representation of visual stimuli.As networks, FNs can  be readily compared to anatomical and synaptic connectivity  in neural network model. This comparison reveals that the  structure of statistical dependencies depends on the  timescale at which they are computed, and that neural  activity has a more clustered structure than the synaptic  wiring used in the model. In mouse primary visual cortex,  this increased clustering is found to be characteristic of  neurons that do not have a clear stimulus preference (i.e.  untuned neurons). Moreover, regardless of single cell  selectivity, the correlations between neurons are  stimulus-specific and hence informative of the visual  stimulus. Subsequently, a sparse subset of  stimulus-specific pairwise interactions is identified.  These correlations reliably manifest as pairs of  coordinated spikes on a time-point by time-point basis,  building up spatio-temporal sequences. When only these  spikes are considered, single neurons still display high  trial-to-trial variability. Nonetheless, these spikes have  enhanced readability by hypothetical downstream elements. },
      url = {http://knowledge.uchicago.edu/record/3399},
      doi = {https://doi.org/10.6082/uchicago.3399},
}