no code implementations • NeurIPS 2014 • Evan W. Archer, Urs Koster, Jonathan W. Pillow, Jakob H. Macke
Moreover, because the nonlinear stimulus inputs are mixed by the ongoing dynamics, the model can account for a relatively large number of idiosyncratic receptive field shapes with a small number of nonlinear inputs to a low-dimensional latent dynamical model.
no code implementations • NeurIPS 2013 • Il Memming Park, Evan W. Archer, Kenneth Latimer, Jonathan W. Pillow
We also establish a condition for equivalence between the cascade-logistic and the 2nd-order maxent or "Ising'' model, making cascade-logistic a reasonable choice for base measure in a universal model.
1 code implementation • NeurIPS 2013 • Evan W. Archer, Il Memming Park, Jonathan W. Pillow
Shannon's entropy is a basic quantity in information theory, and a fundamental building block for the analysis of neural codes.
no code implementations • NeurIPS 2013 • Il Memming Park, Evan W. Archer, Nicholas Priebe, Jonathan W. Pillow
The quadratic form characterizes the neuron's stimulus selectivity in terms of a set linear receptive fields followed by a quadratic combination rule, and the invertible nonlinearity maps this output to the desired response range.
