Synaptic learning rules for sequence learning
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Germany
- Einstein Center for Neurosciences Berlin, Germany
Figures
Figure 1
Rationale for temporal-order learning via phase precession.
Top: Behavioral events (A to D) happen on a time scale of seconds. Middle: These events are represented by different cells (a–d), which fire a burst of stochastic action potentials in response to …
Figure 2
Model of two sequentially activated phase-precessing cells.
(A) Oscillatory firing-rate profiles for two cells (solid blue and cyan lines). The black curve depicts the population theta oscillation. For easier comparison of the two different frequencies, the …
Figure 3
Temporal-order learning for narrow learning windows ().
(A) The average synaptic weight change depends on the temporal separation between the firing fields. Phase precession (blue) yields higher weight changes than phase locking (red). Simulation …
Figure 4
Effect of the learning-window width on temporal-order learning for overlapping fields (here: ).
(A) Average weight change as a function of width (for the asymmetric window in Equation 14) for phase precession and phase locking (colored curves). The solid black line depicts the …
Figure 5
Temporal-order learning for non-overlapping firing fields using wide, asymmetric learning windows.
(A) Firing rates of two example cells with non-overlapping firing fields. (B) Cross-correlation of the two cells from (A). (C) Asymmetric learning window with large width ( s). (D) Resulting …
Author response image 1
Comparison of pairwise and triplet STDP.
(A) Average weight change for the pairwise Bi-and-Poo learning rule (circles) and the minimal triplet model from Pfister and Gerstner, 2006 (squares). Bluish symbols represent phase precession, …
