Intrinsic Stability of Temporally Shifted Spike-Timing Dependent Plasticity

Department of Neuroscience, Columbia University, Center for Theoretical Neuroscience, New York, New York, United States of America.
PLoS Computational Biology (Impact Factor: 4.62). 11/2010; 6(11):e1000961. DOI: 10.1371/journal.pcbi.1000961
Source: PubMed


Spike-timing dependent plasticity (STDP), a widespread synaptic modification mechanism, is sensitive to correlations between presynaptic spike trains and it generates competition among synapses. However, STDP has an inherent instability because strong synapses are more likely to be strengthened than weak ones, causing them to grow in strength until some biophysical limit is reached. Through simulations and analytic calculations, we show that a small temporal shift in the STDP window that causes synchronous, or nearly synchronous, pre- and postsynaptic action potentials to induce long-term depression can stabilize synaptic strengths. Shifted STDP also stabilizes the postsynaptic firing rate and can implement both Hebbian and anti-Hebbian forms of competitive synaptic plasticity. Interestingly, the overall level of inhibition determines whether plasticity is Hebbian or anti-Hebbian. Even a random symmetric jitter of a few milliseconds in the STDP window can stabilize synaptic strengths while retaining these features. The same results hold for a shifted version of the more recent "triplet" model of STDP. Our results indicate that the detailed shape of the STDP window function near the transition from depression to potentiation is of the utmost importance in determining the consequences of STDP, suggesting that this region warrants further experimental study.

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    • ", 2007 ) seems to suggest a more multiplicative type rule ( van Rossum et al . , 2000 ; Babadi and Abbott , 2010 ) . Together , these results suggest that the amounts of homogenization and competition expressed by Hebbian mechanisms may vary from area - to - area , or maybe developmentally regulated , depending on the "
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    • "Long-term stability can be problematic for correlative learning rules (e.g., Figure 5C), since bounded Hebbian synapses destabilize plastic networks by maximally potentiating or depressing synapses. Additional mechanisms such as weight-dependent weight changes (van Rossum et al., 2000) or fine tuning of window parameters (Kempter and Gerstner, 2001; Babadi and Abbott, 2010) have been shown to be able to keep weights in check. In contrast, owing to its plasticity dynamics during on-line probability estimation, spike-based BCPNN naturally demonstrated weight dependence (Figure 5B) along with a stable unimodal equilibrium weight distribution when exposed to prolonged uncorrelated stimulation. "
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