Plasticity Kernels and Temporal Statistics
Dayan, Peter, Häusser, Michael, London, Michael
–Neural Information Processing Systems
Figure l(Dl-Gl)* depict some of the main STDP findings/ of which the best-investigated are shown in figure l(Dl;El), and are variants of based a'standard' STDP rule. Earlier work considered rate-based rather than spike temporal rules, and so we adopt the broader term'time dependent plasticity' or TDP. Note the strong temporal asymmetry in both the standard rules. Although the theoretical studies have provided us with excellent tools for modeling the detailed consequences of different time-dependent rules, and understanding characteristics such as long-run stability and the relationship with non-temporal learning rules such as BCM,6 specifically computational ideas about TDP are rather thinner on the ground. Two main qualitative notions explored in various of the works cited above are that the temporal asymmetries in TDP rules are associated with causality or prediction. However, looking specifically at the standard STDP rules, models interested in prediction *We refer to graphs in this figure by row and column.
Neural Information Processing Systems
Dec-31-2004
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