We use a common linear approximation to compare the two methods and show that perfect performance at estimation implies chance performance at discrimination.
Dec 1, 2010 · This way of performing computations is fast, accurate, readily learnable, and robust to various forms of noise. Here we analyze the computation ...
We use a common linear approximation to compare the two methods and show that perfect performance at estimation implies chance performance at discrimination.
Change-based inference in attractor nets: Linear analysis: Neural ...
dl.acm.org › doi › NECO_a_00051
This way of performing computations is fast, accurate, readily learnable, and robust to various forms of noise. Here we analyze the computation of stimulus ...
We use a common linear approximation to compare the two methods and show that perfect performance at estimation implies chance performance at discrimination.
This change-based readout makes decisions based on the way a statistic of the state of the network changes over time [3]. We showed that this method can perform ...
Autor: Moazzezi, R et al.; Genre: Poster; Online veröffentlicht: 2009-01; Titel: Change-based inference in attractor nets: Linear analysis.
Change-based inference in attractor nets: Linear analysis. Author(s) -. Peter Dayan. Publication year - 2009. Publication title -. frontiers in systems ...
Change-based inference for invariant discrimination 239 ... attractor network with only one line attractor (Figure 4b) with change-based readout ... line attractor) ...
Our work demonstrates how attractors can implement a dynamic Bayesian inference algorithm in a biologically plausible manner, and it makes testable predictions ...