Network neuroscience is the emerging discipline concerned with investigating the complex
patterns of interconnections found in neural systems, and identifying principles with which to …

Neural circuits reliably encode and transmit signals despite the presence of noise at multiple
stages of processing. The efficient coding hypothesis, a guiding principle in computational …

Descriptive statistical models of neural responses generally aim to characterize the mapping
from stimuli to spike responses while ignoring biophysical details of the encoding process …

Non-random connectivity can emerge without structured external input driven by activity-
dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we …

Which statistical features of spiking activity matter for how stimuli are encoded in neural
populations? A vast body of work has explored how firing rates in individual cells and …

The rise of large-scale recordings of neuronal activity has fueled the hope to gain new
insights into the collective activity of neural ensembles. How can one link the statistics of …

We investigate the ability of the pairwise maximum entropy (PME) model to describe the
spiking activity of large populations of neurons recorded from the visual, auditory, motor, and …

Identifying network architecture from observed neural activities is crucial in neuroscience
studies. A key requirement is knowledge of the statistical input-output relation of single …

Interactions among different brain regions are usually examined through functional
connectivity (FC) analysis, which is exclusively based on measuring pairwise correlations in …

While recent recordings from neural populations show beyond-pairwise, or higher-order,
correlations (HOC), we have little understanding of how HOC arise from network interactions …