Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme.
Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential-a phenomenon referred to as "phase-of-firing coding" (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions-only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents ( approximately 10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.
Masquelier
Timothée
T
Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain. timothee.masquelier@alum.mit.edu
Hugues
Etienne
E
Deco
Gustavo
G
Thorpe
Simon J
SJ
D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D007257:Information Theory; D007858:Learning / Q000502:physiology*; D009415:Nerve Net; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010507:Periodicity; D016012:Poisson Distribution; D013569:Synapses / Q000502:physiology; D013997:Time Factors