Topic: eigenvalue distribution

A common approach to model local neural circuits is to assume random connectivity. But how is our choice of randomness informed by known network properties? And how does it affect the network's behavior? Previous approaches have focused on prescribing increasingly sophisticated statistics of synaptic strengths and motifs. However, experimental data on parallel dynamics of neurons is more readily accessible than their microcircuitry. We therefore propose a paradigm shift, specifying connectivity in the space that directly controls the dynamics – the space of eigenmodes. We develop a theory for

The statistical structure of neural population responses is a topic of intense interest in systems neuroscience. The signal eigenspectrum (eigenvalues ordered from high to low) is a fundamental summary statistic that characterizes the dimensionality and efficiency or redundancy of population tuning curves. However, the problem of characterizing signal eigenvalue spectra from limited, noisy neural response data is complicated by two factors. First, noise in the neural responses (aka ``noise correlations'') can interfere with the estimation of the eigenvalues of the signal distribution. Second,