Preprint on spike-timing dependent synaptic plasticity

[Walter Senn]

The following preprint,

`An algorithm for modifying neurotransmitter release probability based
on pre- and post-synaptic spike timing',

accepted at Neural Computation, can be downloaded from
http://www.cns.unibe.ch/~wsenn/#pub.  The paper reproduces data of Markram
et al. (Science, 1997, including unpublished ones). It also investigates
the connection to the BCM-rule and to correlation rules based on (Poisson)
firing  rates.

Walter Senn, Henry Markram, Misha Tsodyks


Abstract:

The precise times of occurrence of individual pre- and post-synaptic
action potentials is known to play a key role in the modification of the
synaptic efficacy. Based on stimulation protocols of two synaptically
connected neurons, we infer an algorithm which reproduces the experimental
data by modifying the probability of vesicle discharge as a function of
the relative timing of spikes in the pre- and post-synaptic neurons. The
primary feature of this algorithm is an asymmetry with respect to the
direction of synaptic modification depending on whether the presynaptic
spikes precede or follow the postsynaptic spike. Specifically, if the
presynaptic spike occurs up to 50ms before the postsynaptic spike, the
probability of vesicle discharge is up-regulated while the probability of
vesicle discharge is down-regulated if the presynaptic spike occurs up to
50ms after the postsynaptic spike. In the case where neurons fire
irregularly with Poisson spike trains at constant mean firing rates, the
probability of vesicle discharge converges towards a characteristic value
which is determined by the pre- and post-synaptic firing rates. On the
other hand, if the mean rates of the Poisson spike trains slowly change
with time our algorithm predicts modifications in the probability of
release which generalize Hebbian and BCM rules. We conclude that the
proposed spike-based synaptic learning algorithm provides a general
framework for regulating neurotransmitter release probability.