@article{TEXTUAL,
      recid = {10919},
      author = {Huang, Chengcheng and Pouget, Alexandre and Doiron, Brent},
      title = {Internally generated population activity in cortical  networks hinders information transmission},
      journal = {Science Advances},
      address = {2022-06-01},
      number = {TEXTUAL},
      abstract = {How neuronal variability affects sensory coding is a  central question in systems neuroscience, often with  complex and model-dependent answers. Many studies explore  population models with a parametric structure for response  tuning and variability, preventing an analysis of how  synaptic circuitry establishes neural codes. We study  stimulus coding in networks of spiking neuron models with  spatially ordered excitatory and inhibitory connectivity.  The wiring structure is capable of producing rich  population-wide shared neuronal variability that agrees  with many features of recorded cortical activity. While  both the spatial scales of feedforward and recurrent  projections strongly affect noise correlations, only  recurrent projections, and in particular inhibitory  projections, can introduce correlations that limit the  stimulus information available to a decoder. Using a  spatial neural field model, we relate the recurrent circuit  conditions for information limiting noise correlations to  how recurrent excitation and inhibition can form  spatiotemporal patterns of population-wide activity.},
      url = {http://knowledge.uchicago.edu/record/10919},
}