Spike-timing Dependent Plasticity Mechanisms Research Articles

Developmental Switch in Spike Timing-Dependent Plasticity at Layers 4–2/3 in the Rodent Barrel Cortex

Sensory deprivation during the critical period induces long-lasting changes in cortical maps. In the rodent somatosensory cortex (S1), its precise initiation mechanism is not known, yet spike timing-dependent plasticity (STDP) at layer 4 (L4)-L2/3 synapses are thought to be crucial. Whisker stimulation causes "L4 followed by L2/3" cell firings, while acute single whisker deprivation suddenly reverses the sequential order in L4 and L2/3 neurons in the deprived column (Celikel et al., 2004). Reversed spike sequence then leads to long-term depression through an STDP mechanism (timing-dependent long-term depression), known as deprivation-induced suppression at L4-L2/3 synapses (Bender et al., 2006a), an important first step in the map reorganization. Here we show that STDP properties change dramatically on postnatal day 13-15 (P13-P15) in mice S1. Before P13, timing-dependent long-term potentiation (t-LTP) was predominantly induced regardless of spiking order. The induction of t-LTP required postsynaptic influx of Ca(2+), an activation of protein kinase A, but not calcium/calmodulin-dependent protein kinase II. Consistent with the strong bias toward t-LTP, whisker deprivation (all whiskers in Row "D") from P7-P12 failed to induce synaptic depression at L4-L2/3 synapses in the deprived column, but clear depression was seen if deprivation occurred after P14. Random activation of L4, L2/3 cells, as may occur in response to whisker stimulation before P13 during network formation, led to potentiation under the immature STDP rule, as predicted from the bias toward t-LTP regardless of spiking order. These findings describe a developmental switch in the STDP rule that may underlie the transition from synapse formation to circuit reorganization at L4-L2/3 synapses, both in distinct activity-dependent manners.

Adaptive and Phase Selective Spike Timing Dependent Plasticity in Synaptically Coupled Neuronal Oscillators

We consider and analyze the influence of spike-timing dependent plasticity (STDP) on homeostatic states in synaptically coupled neuronal oscillators. In contrast to conventional models of STDP in which spike-timing affects weights of synaptic connections, we consider a model of STDP in which the time lags between pre- and/or post-synaptic spikes change internal state of pre- and/or post-synaptic neurons respectively. The analysis reveals that STDP processes of this type, modeled by a single ordinary differential equation, may ensure efficient, yet coarse, phase-locking of spikes in the system to a given reference phase. Precision of the phase locking, i.e. the amplitude of relative phase deviations from the reference, depends on the values of natural frequencies of oscillators and, additionally, on parameters of the STDP law. These deviations can be optimized by appropriate tuning of gains (i.e. sensitivity to spike-timing mismatches) of the STDP mechanism. However, as we demonstrate, such deviations can not be made arbitrarily small neither by mere tuning of STDP gains nor by adjusting synaptic weights. Thus if accurate phase-locking in the system is required then an additional tuning mechanism is generally needed. We found that adding a very simple adaptation dynamics in the form of slow fluctuations of the base line in the STDP mechanism enables accurate phase tuning in the system with arbitrary high precision. Adaptation operating at a slow time scale may be associated with extracellular matter such as matrix and glia. Thus the findings may suggest a possible role of the latter in regulating synaptic transmission in neuronal circuits.

Precise Self-tuning of Spiking Patterns in Coupled Neuronal Oscillators

In this work we discuss and analyze spiking patterns in a generic mathematical model of two coupled non-identical nonlinear oscillators supplied with a spike-timing dependent plasticity (STDP) mechanism. Spiking patterns in the system are shown to converge to a phase-locked state in a broad range of parameters. Precision of the phase locking, i.e. the amplitude of relative phase deviations from a given reference, depends on the natural frequencies of oscillators and, additionally, on parameters of the STDP law. These deviations can be optimized by appropriate tuning of gains (i.e. sensitivity to spike-timing mismatches) of the STDP mechanisms. The deviations, however, can not be made arbitrarily small neither by mere tuning of STDP gains nor by adjusting synaptic weights. Thus if accurate phase-locking in the system is required then an additional tuning mechanism is generally needed. We found that adding a very simple adaptation dynamics in the form of slow fluctuations of the base line in the STDP mechanism enables accurate phase tuning in the system with arbitrary high precision. The scheme applies to systems in which individual oscillators operate in the oscillatory mode. If the dynamics of oscillators becomes bistable then relative phase may fail to converge to a given value giving rise to the emergence of complex spiking sequences.

Interregional synaptic competition in neurons with multiple STDP-inducing signals

Neocortical layer 5 (L5) pyramidal cells have at least two spike initiation zones: Na(+) spikes are generated near the soma, and Ca(2+) spikes at the apical dendritic tuft. These spikes interact with each other and serve as signals for synaptic plasticity. The present computational study explores the implications of having two spike-timing-dependent plasticity (STDP) signals in a neuron, each with its respective regional population of synaptic "pupils." In a detailed model of an L5 pyramidal neuron, competition emerges between synapses belonging to different regions, on top of the competition among synapses within each region, which characterizes the STDP mechanism. Interregional competition results in strengthening of one group of synapses, which ultimately dominates cell firing, at the expense of weakening synapses in other regions. This novel type of competition is inherent to dendrites with multiple regional signals for Hebbian plasticity. Surprisingly, such interregional competition exists even in a simplified model of two identical coupled compartments. We find that in a model of an L5 pyramidal cell, the different synaptic subpopulations "live in peace" when the induction of Ca(2+) spikes requires the back-propagating action potential (BPAP). Thus we suggest a new key role for the BPAP, to maintain the balance between synaptic efficacies throughout the dendritic tree, thereby sustaining the functional integrity of the entire neuron.

Low frequency oscillations facilitate STDP-based pattern learning and decoding

Event Abstract Back to Event Low frequency oscillations facilitate STDP-based pattern learning and decoding Recent experiments have established that information can be encoded in the spike times of some neurons relative to the phase of a background low frequency (1-10 Hz) LFP oscillation [1, 2, 3] - a phenomenon referred to as “phase-of-firing coding” (PoFC) [3]. 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 [4, 5, 6, 7]. However, it remains unclear whether this phase preference is only an epiphenomenon or if it really affects neuronal interactions - only then could it have a functional role. Here we demonstrate that PoFC at low frequencies has remarkable effects on downstream learning and decoding when coupled with Spike Timing Dependent Plasticity (STDP). Consider the abstract problem of a single neuron equipped with STDP receiving activation patterns via thousands of afferent excitatory fibers. How might the neuron detect a repeating activation pattern that affects an unknown subset of its afferents, under conditions where the duration of the patterns is unpredictable and where the patterns recur at unpredictable intervals? Two conventional coding options would involve converting the activation patterns into spikes using either a Poisson rate-coding scheme or Leaky-Ingrate-and-Fire neurons that generate variable firing rates. However, learning to detect the repeating patterns with STDP using this sort of coding is difficult or even impossible. In contrast, when the afferent population receives an oscillatory modulation current, and remarkably even when a small fraction of its afferents (~10%) exhibits PoFC, we show that a neuron can rapidly learn to respond selectively every time the repeating activation pattern occurs. The ability of STDP to detect repeating patterns had been noted before [8], but not in an oscillatory mode. Here we show how the partial formatting of the spike times by the oscillation, so that they mainly depend on the current input current, can be efficiently decoded by STDP and recognized after learning in just one oscillation cycle. This suggests a main functional role for the low frequency oscillations that are found almost everywhere in the brain. More generally, our study demonstrates that an oscillation and the STDP mechanism, by the existence of their intrinsic timescales, can together have a functional role.

Spike Timing–Dependent Plasticity: A Hebbian Learning Rule

Spike timing-dependent plasticity (STDP) as a Hebbian synaptic learning rule has been demonstrated in various neural circuits over a wide spectrum of species, from insects to humans. The dependence of synaptic modification on the order of pre- and postsynaptic spiking within a critical window of tens of milliseconds has profound functional implications. Over the past decade, significant progress has been made in understanding the cellular mechanisms of STDP at both excitatory and inhibitory synapses and of the associated changes in neuronal excitability and synaptic integration. Beyond the basic asymmetric window, recent studies have also revealed several layers of complexity in STDP, including its dependence on dendritic location, the nonlinear integration of synaptic modification induced by complex spike trains, and the modulation of STDP by inhibitory and neuromodulatory inputs. Finally, the functional consequences of STDP have been examined directly in an increasing number of neural circuits in vivo.