Dominant eigenvalues

Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Classical theoretical analyses of dynamics in EI networks have revealed key principles such as EI balance or paradoxical responses to external inputs. These seminal results assume that synaptic strengths depend on the type of neurons they connect but are otherwise statistically independent. However, recent synaptic physiology datasets have uncovered connectivity patterns that deviate significantly from independent connection models. Simultaneously, studies of task-trained recurrent networks have emphasize

Understanding how connectivity structure shapes network dynamics is paramount in the field of neuroscience. Theoretical investigations of multi-population neuronal networks often consider statistically homogeneous populations and incorporate either only the population-averaged mean or i.i.d. fluctuations in synaptic couplings. A newly released synaptic physiology dataset highlighted the strong presence of motifs – specific connectivity patterns between pairs and triplets of neurons–beyond the scope of mean connectivity[1] However, it is a priori not clear which of the experimentally identifie

One of the key questions in neuroscience is how the connectivity structure of cortical networks determines the collective dynamics of neural activity. Two complementary approaches have been developed to address this question: (i) specifying connectivity in terms of local statistics of excitatory-inhibitory motifs [Trousdale et al. 2012, Aljadeff et al. 2015]; (ii) specifying connectivity through a global low-rank structure that determines the low-dimensional dynamics [Mastrogiuseppe and Ostojic 2018, Beiran et al. 2021, Dubreuil et al. 2020]. It is however currently unclear how local connectiv