Abstract
Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.
Highlights
Precise and recurring spatio-temporal patterns of action potentials are observed in various biological neuronal networks
To this end we introduced a synaptic plasticity mechanism that is based on the requirement to balance the membrane potential and uses the postsynaptic membrane potential rather than postsynaptic spike times as the relevant signal for synaptic changes (Membrane Potential Dependent Plasticity, MPDP)
We propose Membrane Potential Dependent Plasticity as a viable mechanism for spike time learning in biological spiking neuronal networks
Summary
Precise and recurring spatio-temporal patterns of action potentials are observed in various biological neuronal networks. Learning of Spikes with Membrane Potential Dependent Plasticity neurons are measured, the variability of latencies relative to the onset of a externally induced stimulus is often higher than if the latencies are measured relative to other sensory neurons [2, 3]; spike times covary. Information about the stimulus is coded in spatio-temporal spike patterns. Theoretical considerations show that in some situations spike-time coding is superior to rate coding [4]. Xu and colleagues demonstrated that through associative training it is possible to imprint new sequences of activations in visual cortex [5], which shows that there are plasticity mechanisms which are used to learn precise sequences
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