Timing Of Postsynaptic Spikes Research Articles

Messages in Spike Timing-Dependent Plasticity: Pros and Cons

Spike-timing dependent plasticity (STDP), a synaptic modification depending on a relative timing of presynaptic and postsynaptic spikes, has fascinated researchers in the fields of neurophysiology and computational neuroscience, because it is not only conceptually simple or biologically reasonable but is also versatile in neural network simulations. The STDP rule may be valid only under specific conditions, however. We propose herein a method that could find more natural and potent rules of synaptic plasticity.

Spike‐timing‐dependent plasticity in hippocampal CA3 neurons

Synaptic plasticity of different inputs converging onto CA3 pyramidal neurons is central to theories of hippocampal function. The mossy fibre (MF) input to these neurons is thought to exhibit plasticity that is in nearly all aspects fundamentally different from plasticity in other brain regions: in particular, when induced by high frequency presynaptic stimulation, plasticity at these synapses is independent of NMDA receptor (NMDAR) activation and presynaptically expressed. Here, we show that different stimulation protocols that depend on the close timing of MF activity and postsynaptic spikes induce bidirectional plasticity in CA3 neurons in 3-week-old rats. Long-term potentiation (LTP) is observed when an excitatory postsynaptic potential (EPSP), evoked by MF stimulation, precedes a single postsynaptic action potential (AP) or a brief AP burst by 10 ms. Instead, timing-dependent long-term depression (LTD) requires the pairing of a single AP to an EPSP with a delay of 30 ms. The pairing of APs to synaptic activity is required for plasticity induction, since the application of unpaired APs or EPSPs did not alter synaptic strength. Furthermore, our results demonstrate that both timing-dependent LTP and LTD critically depend on the activation of NMDARs. Specifically blocking postsynaptic NMDARs prevents plasticity, demonstrating that NMDARs important to spike-timing-dependent plasticity in CA3 neurons are required at postsynaptic sites. In summary, this study shows that the close timing of APs to MF excitatory synaptic input can alter synaptic efficacy in CA3 neurons in a bidirectional manner.

Maturation of GABAergic Inhibition Promotes Strengthening of Temporally Coherent Inputs among Convergent Pathways

Spike-timing-dependent plasticity (STDP), a form of Hebbian plasticity, is inherently stabilizing. Whether and how GABAergic inhibition influences STDP is not well understood. Using a model neuron driven by converging inputs modifiable by STDP, we determined that a sufficient level of inhibition was critical to ensure that temporal coherence (correlation among presynaptic spike times) of synaptic inputs, rather than initial strength or number of inputs within a pathway, controlled postsynaptic spike timing. Inhibition exerted this effect by preferentially reducing synaptic efficacy, the ability of inputs to evoke postsynaptic action potentials, of the less coherent inputs. In visual cortical slices, inhibition potently reduced synaptic efficacy at ages during but not before the critical period of ocular dominance (OD) plasticity. Whole-cell recordings revealed that the amplitude of unitary IPSCs from parvalbumin positive (Pv+) interneurons to pyramidal neurons increased during the critical period, while the synaptic decay time-constant decreased. In addition, intrinsic properties of Pv+ interneurons matured, resulting in an increase in instantaneous firing rate. Our results suggest that maturation of inhibition in visual cortex ensures that the temporally coherent inputs (e.g. those from the open eye during monocular deprivation) control postsynaptic spike times of binocular neurons, a prerequisite for Hebbian mechanisms to induce OD plasticity.

Spike timing-dependent plasticity interacts with neural dynamics to enhance information transmission

Event Abstract Back to Event Spike timing-dependent plasticity interacts with neural dynamics to enhance information transmission Guillaume Hennequin1*, Jean-Pascal Pfister2 and Wulfram Gerstner3 1 SV and Brain-Mind Institute, EPFL, Switzerland 2 University of Cambridge, Department of Engineering , United Kingdom 3 LCN , EPFL, Switzerland Spike Timing-Dependent Plasticity (STDP) - the fact that synapses vary their strengths in a way that depends on the pre- and postsynaptic spike times at a millisecond timescale - has been computationally studied in various ways during the last two decades. A particularly fruitful approach has been the use of minimal phenomenological models [1, 2]. These can be fitted to experimental data [3, 4], resulting in data-grounded and compact models from which the functional role of STDP can be infered only indirectly. A complementary approach has been to tackle the reverse problem : a hypothesis is made about the functional role of plasticity, and an optimal learning rule to achieve this goal is derived [5, 6, 7, 8]. For example, maximizing the mutual information between input and output spike trains yields a learning rule with STDP-like features [9]. Here we extend the framework of information maximization to include spike-frequency adaptation (SFA) of the postsynaptic neuron. In the resulting learning rule, potentiation that occurs after a pre-before-post pairing event decreases with the distance to the previous postsynaptic spike. This is in agreement with minimal models where triplets of spikes (pre-post-post or post-pre-post) are essential building blocks [3]. Intuitively, we can understand the triplet effect in the limit of highly reliable neurons. Optimal information transfer (at a fixed average firing rate) would be achieved by Poisson distributed output spikes. Therefore, plasticity has to work against refractoriness and SFA, so that events with short intervals post-post need to be enhanced. We compare our optimal learning rule with the minimal triplet rule [3] and a standard pair-based STDP rule [1, 2] on a task where a single postsynaptic neuron receives input with a given spatio-temporal statistics. We show that, with the infomax and the triplet rule, the neuron specialises on spatial and temporal aspects of the stimulus whereas the standard pair-based rule picks up only spatial aspects.

Gain in sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses

Spike-timing-dependent plasticity (STDP) is considered a physiologically relevant form of Hebbian learning. However, behavioral learning often involves action of reinforcement or reward signals such as dopamine. Here, we examined how dopamine influences the quantitative rule of STDP at glutamatergic synapses of hippocampal neurons. The presence of 20 muM dopamine during paired pre- and postsynaptic spiking activity expanded the effective time window for timing-dependent long-term potentiation (t-LTP) to at least -45 ms, and allowed normally ineffective weak stimuli with fewer spike pairs to induce significant t-LTP. Meanwhile, dopamine did not affect the degree of t-LTP induced by normal strong stimuli with spike timing (ST) of +10 ms. Such dopamine-dependent enhancement in the sensitivity of t-LTP was completely blocked by the D1-like dopamine receptor antagonist SCH23390, but not by the D2-like dopamine receptor antagonist sulpiride. Surprisingly, timing-dependent long-term depression (t-LTD) at negative ST was converted into t-LTP by dopamine treatment; this conversion was also blocked by SCH23390. In addition, t-LTP in the presence of dopamine was completely blocked by the NMDA receptor antagonist 2-amino-5-phosphonovaleric acid, indicating that D1-like receptor-mediated modulation appears to act through the classical NMDA receptor-mediated signaling pathway that underlies STDP. These results provide a quantitative and mechanistic basis for a previously undescribed learning rule that depends on pre- and postsynaptic ST, as well as the global reward signal.

Activity-dependent scaling of excitability and its influence on spike timing dependent plasticity

Neurons show plasticity in neuronal and synaptic properties due to development and/or learning, affecting both the input levels to the neuron as well as the neural excitability. However, neurons have a limited dynamic range, i.e. the range over which they are sensitive to the input and are not in either a quiescent or a saturated activity state. This suggests neurons possess control mechanisms that match neural excitability and synaptic input levels. Recent experimental studies suggest that neurons indeed show a homeostatic scaling of excitability (HSE) by sensing activity levels and adapting the neural excitability via regulation of specific membrane conductance densities. The maintenance of sensitivity to synaptic input is also central to learning processes. In one form of learning it has been demonstrated that synaptic modification depends on the exact timing of presynaptic inputs and postsynaptic spikes. The performance of this spike timing dependent plasticity (STDP) is expected to be affected by a decrease in the sensitivity of the neuron to its input. At the one hand this suggests an important role for HSE in the functioning of STDP, at the other hand it leads to the question whether HSE could interfere with the learning of input patterns via STDP. Here, we address these issues by using both mathematical analysis and numerical simulations of a neuron that shows HSE and that receives input from synapses showing STDP. Based on experimental results, HSE is implemented as activity-dependent shifts of the input-output function. We use the multiplicative formulation of STDP in which the changes in synaptic strength depend on the synaptic strength itself. We show that while background input levels vary greatly, HSE keeps the neuron within its dynamic range and does not affect the synaptic weight distribution. HSE can also easily compensate for variations in the shape of the STDP learning window and maintain the sensitivity to correlations in the input. However, in neurons without HSE, the sensitivity to correlations in the input depends strongly on the various parameters. The effects of HSE are further explored by examining the neuron response to input patterns. We show that when neural excitability is controlled by HSE, STDP leads to changes of the synaptic weights as a function of the properties of the input pattern, i.e. the number of inputs forming the pattern and the strength of the correlation within the pattern. Learning of a pattern increases the probability of it generating a postsynaptic spike, depending on the properties of the pattern. HSE makes the effect of learning input patterns almost independent of the background levels. The results suggest HSE does not interfere with STDP and that HSE has a central role in maintaining the learning capabilities of the neuron in its highly plastic environment.

Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning

In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up- and downregulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. If the presynaptic spike arrives before the desired postsynaptic spike timing, our optimal learning rule predicts that the synapse should become potentiated. The dependence of the potentiation on spike timing directly reflects the time course of an excitatory postsynaptic potential. However, our approach gives no unique reason for synaptic depression under reversed spike timing. In fact, the presence and amplitude of depression of synaptic efficacies for reversed spike timing depend on how constraints are implemented in the optimization problem. Two different constraints, control of postsynaptic rates and control of temporal locality, are studied. The relation of our results to spike-timing-dependent plasticity and reinforcement learning is discussed.

Two Coincidence Detectors for Spike Timing-Dependent Plasticity in Somatosensory Cortex

Many cortical synapses exhibit spike timing-dependent plasticity (STDP) in which the precise timing of presynaptic and postsynaptic spikes induces synaptic strengthening [long-term potentiation (LTP)] or weakening [long-term depression (LTD)]. Standard models posit a single, postsynaptic, NMDA receptor-based coincidence detector for LTP and LTD components of STDP. We show instead that STDP at layer 4 to layer 2/3 synapses in somatosensory (S1) cortex involves separate calcium sources and coincidence detection mechanisms for LTP and LTD. LTP showed classical NMDA receptor dependence. LTD was independent of postsynaptic NMDA receptors and instead required group I metabotropic glutamate receptors and calcium from voltage-sensitive channels and IP3 receptor-gated stores. Downstream of postsynaptic calcium, LTD required retrograde endocannabinoid signaling, leading to presynaptic LTD expression, and also required activation of apparently presynaptic NMDA receptors. These LTP and LTD mechanisms detected firing coincidence on approximately 25 and approximately 125 ms time scales, respectively, and combined to implement the overall STDP rule. These findings indicate that STDP is not a unitary process and suggest that endocannabinoid-dependent LTD may be relevant to cortical map plasticity.

Spatiotemporal Specificity of Neuronal Activity Directs the Modification of Receptive Fields in the Developing Retinotectal System

The precise temporal relation between pre- and postsynaptic activity modulates the strength of synaptic connections. Despite its extensive characterization in vivo and in vitro, the degree to which spike timing-dependent plasticity can shape receptive field properties is unclear. We use in vivo patch-clamp recordings of tectal neurons in developing Xenopus tadpoles to control the precise timing of action potentials with respect to the arrival of a subset of visual inputs evoked by local light stimulation on the retina. The pattern of visual inputs onto a tectal neuron was tracked over time by rapid reverse correlation mapping of receptive fields. Spike timing-dependent potentiation or depression of a subset of synapses reliably shifts the spatial receptive fields toward or away from the trained subregion of visual space, respectively. These results demonstrate that natural patterns of activity evoked by sensory stimuli play an instructive role in the developing nervous system.

Matching storage and recall: hippocampal spike timing–dependent plasticity and phase response curves

Hippocampal area CA3 is widely considered to function as an autoassociative memory. However, it is insufficiently understood how it does so. In particular, the extensive experimental evidence for the importance of carefully regulated spiking times poses the question as to how spike timing-based dynamics may support memory functions. Here, we develop a normative theory of autoassociative memory encompassing such network dynamics. Our theory specifies the way that the synaptic plasticity rule of a memory constrains the form of neuronal interactions that will retrieve memories optimally. If memories are stored by spike timing-dependent plasticity, neuronal interactions should be formalized in terms of a phase response curve, indicating the effect of presynaptic spikes on the timing of postsynaptic spikes. We show through simulation that such memories are competent analog autoassociators and demonstrate directly that the attributes of phase response curves of CA3 pyramidal cells recorded in vitro qualitatively conform with the theory.

Spike Timing Precision and Neural Error Correction: Local Behavior

The effects of spike timing precision and dynamical behavior on error correction in spiking neurons were investigated. Stationary discharges-phase locked, quasiperiodic, or chaotic-were induced in a simulated neuron by presenting pacemaker presynaptic spike trains across a model of a prototypical inhibitory synapse. Reduced timing precision was modeled by jittering presynaptic spike times. Aftereffects of errors-in this communication, missed presynaptic spikes-were determined by comparing postsynaptic spike times between simulations identical except for the presence or absence of errors. Results show that the effects of an error vary greatly depending on the ongoing dynamical behavior. In the case of phase lockings, a high degree of presynaptic spike timing precision can provide significantly faster error recovery. For nonlocked behaviors, isolated missed spikes can have little or no discernible aftereffects (or even serve to paradoxically reduce uncertainty in postsynaptic spike timing), regardless of presynaptic imprecision. This suggests two possible categories of error correction: high-precision locking with rapid recovery and low-precision nonlocked with error immunity.

Input and frequency‐specific entrainment of postsynaptic firing by IPSPs of perisomatic or dendritic origin

Correlated activity of cortical neurons underlies cognitive processes. Networks of several distinct classes of gamma-aminobutyric acid (GABA)ergic interneurons are capable of synchronizing cortical neurons at behaviourally relevant frequencies. Here we show that perisomatic and dendritic GABAergic inputs provided by two classes of GABAergic cells, fast spiking and bitufted interneurons, respectively, entrain the timing of postsynaptic spikes differentially in both pyramidal cells and interneurons at beta and gamma frequencies. Entrainment of pyramidal as well as regular spiking non-pyramidal cells was input site and inhibitory postsynaptic potential frequency dependent. Gamma frequency input from fast spiking cells entrained pyramidal cells on the positive phase of an intrinsic cellular theta oscillation, whereas input from bitufted cells was most effective in gamma frequency entrainment on the negative phase of the theta oscillation. The discharge of regular spiking interneurons was phased at gamma frequency by dendritic input from bitufted cells, but not by perisomatic input from fast spiking cells. Action potentials in fast spiking GABAergic neurons were phased at gamma frequency by both other fast spiking and bitufted cells, regardless of whether the presynaptic GABAergic input was at gamma or beta frequency. The interaction of cell type-specific intrinsic properties and location-selective GABAergic inputs could result in a spatio-temporally regulated synchronization and gating of cortical spike propagation in the network.

Estimating use-dependent synaptic gain in autonomic ganglia by computational simulation and dynamic-clamp analysis.

Biological gain mechanisms regulate the sensitivity and dynamics of signaling pathways at the systemic, cellular, and molecular levels. In the sympathetic nervous system, gain in sensory-motor feedback loops is essential for homeostatic regulation of blood pressure and body temperature. This study shows how synaptic convergence and plasticity can interact to generate synaptic gain in autonomic ganglia and thereby enhance homeostatic control. Using a conductance-based computational model of an idealized sympathetic neuron, we simulated the postganglionic response to noisy patterns of presynaptic activity and found that a threefold amplification in postsynaptic spike output can arise in ganglia, depending on the number and strength of nicotinic synapses, the presynaptic firing rate, the extent of presynaptic facilitation, and the expression of muscarinic and peptidergic excitation. The simulations also showed that postsynaptic refractory periods serve to limit synaptic gain and alter postsynaptic spike timing. Synaptic gain was measured by stimulating dissociated bullfrog sympathetic neurons with 1-10 virtual synapses using a dynamic clamp. As in simulations, the threshold synaptic conductance for nicotinic excitation of firing was typically 10-15 nS, and synaptic gain increased with higher levels of nicotinic convergence. Unlike the model, gain in neurons sometimes declined during stimulation. This postsynaptic effect was partially blocked by 10 microM Cd2+, which inhibits voltage-dependent calcium currents. These results support a general model in which the circuit variations observed in parasympathetic and sympathetic ganglia, as well as other neural relays, can enable functional subsets of neurons to behave either as 1:1 relays, variable amplifiers, or switches.

The Functional Consequences of Changes in the Strength and Duration of Synaptic Inputs to Oscillatory Neurons

We studied the effect of synaptic inputs of different amplitude and duration on neural oscillators by simulating synaptic conductance pulses in a bursting conductance-based pacemaker model and by injecting artificial synaptic conductance pulses into pyloric pacemaker neurons of the lobster stomatogastric ganglion using the dynamic clamp. In the model and the biological neuron, the change in burst period caused by inhibitory and excitatory inputs of increasing strength saturated, such that synaptic inputs above a certain strength all had the same effect on the firing pattern of the oscillatory neuron. In contrast, increasing the duration of the synaptic conductance pulses always led to changes in the burst period, indicating that neural oscillators are sensitive to changes in the duration of synaptic input but are not sensitive to changes in the strength of synaptic inputs above a certain conductance. This saturation of the response to progressively stronger synaptic inputs occurs not only in bursting neurons but also in tonically spiking neurons. We identified inward currents at hyperpolarized potentials as the cause of the saturation in the model neuron. Our findings imply that activity-dependent or modulator-induced changes in synaptic strength are not necessarily accompanied by changes in the functional impact of a synapse on the timing of postsynaptic spikes or bursts.

Mathematical formulations of Hebbian learning.

Several formulations of correlation-based Hebbian learning are reviewed. On the presynaptic side, activity is described either by a firing rate or by presynaptic spike arrival. The state of the postsynaptic neuron can be described by its membrane potential, its firing rate, or the timing of backpropagating action potentials (BPAPs). It is shown that all of the above formulations can be derived from the point of view of an expansion. In the absence of BPAPs, it is natural to correlate presynaptic spikes with the postsynaptic membrane potential. Time windows of spike-time-dependent plasticity arise naturally if the timing of postsynaptic spikes is available at the site of the synapse, as is the case in the presence of BPAPs. With an appropriate choice of parameters, Hebbian synaptic plasticity has intrinsic normalization properties that stabilizes postsynaptic firing rates and leads to subtractive weight normalization.

Maturation of Synaptic Transmission at End-Bulb Synapses of the Cochlear Nucleus

Neurons of the avian nucleus magnocellularis transmit phase-locked action potentials of the auditory nerve in a pathway that contributes to sound localization based on interaural timing differences. We studied developmental changes in synaptic transmission that enable the end-bulb synapse to function as a synaptic relay. In chick, although the auditory system begins to function early in embryonic development, maturation of audition around the time of hatching suggested that synaptic transmission in the cochlear nucleus of young chicks may undergo further developmental changes. Synaptic physiology was investigated via patch-clamp recordings from bushy cells in brainstem slices during stimulation of auditory nerve fibers at 35 degrees C. Compared with embryonic synapses (embryonic day 18), post-hatch chicks (post-hatch days 1-11) exhibited high probability of firing a well timed postsynaptic action potential during high-frequency stimulation of the auditory nerve. Improvements in reliability and timing of postsynaptic spikes were accompanied by a developmental increase in steady-state EPSCs during stimulus trains and a decline in the extent of synaptic depression. Synchrony of EPSCs during stimulus trains improved with age. An increased pool of synaptic vesicles, lower release probability, larger and faster transmitter quanta, and reduced AMPA receptor desensitization contributed to these changes. Together, these factors improve the ability of cochlear nucleus magnocellularis neurons to faithfully transmit timing information encoded by the auditory nerve.

Stimulus Timing-Dependent Plasticity in Cortical Processing of Orientation

The relative timing of presynaptic and postsynaptic spikes plays a critical role in activity-induced synaptic modification. Here we examined whether plasticity of orientation selectivity in the visual cortex depends on stimulus timing. Repetitive pairing of visual stimuli at two orientations induced a shift in orientation tuning of cat cortical neurons, with the direction of the shift depending on the temporal order of the pair. Induction of a significant shift required that the interval between the pair fall within ±40 ms, reminiscent of the temporal window for spike timing-dependent synaptic plasticity. Mirroring the plasticity found in cat visual cortex, similar conditioning also induced a shift in perceived orientation by human subjects, further suggesting functional relevance of this phenomenon. Thus, relative timing of visual stimuli can play a critical role in dynamic modulation of adult cortical function, perhaps through spike timing-dependent synaptic plasticity.

Learning spatio-temporal stimuli with networks of spiking neurons and dynamic synapses

A network of spiking neurons and dynamic synapses is introduced to yield a mechanism for learning spatio-temporal stimulus patterns. Integrate-and-fire postsynaptic neurons receive input spike trains from multiple dynamic synapses. The synaptic dynamics is based on exact pre- and postsynaptic spike timing and exhibits short-term facilitation and depression. In addition, dependent on their adaptable long-term configuration (learning) the synapses automatically implement specific delays in their peak response. Each postsynaptic neuron with its set of incoming synapses gets tuned to a specific spatio-temporal pattern. The whole network is capable of discriminating between stimuli with one output per learned stimulus type.

Temporal clustering with spiking neurons and dynamic synapses: towards technological applications

We apply spiking neurons with dynamic synapses to detect temporal patterns in a multi-dimensional signal. We use a network of integrate-and-fire neurons, fully connected via dynamic synapses, each of which is given by a biologically plausible dynamical model based on the exact pre- and post-synaptic spike timing. Dependent on their adaptable configuration (learning) the synapses automatically implement specific delays. Hence, each output neuron with its set of incoming synapses works as a detector for a specific temporal pattern. The whole network functions as a temporal clustering mechanism with one output per input cluster. The classification capability is demonstrated by illustrative examples including patterns from Poisson processes and the analysis of speech data.

An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing.

The precise times of occurrence of individual pre- and postsynaptic action potentials are known to play a key role in the modification of synaptic efficacy. Based on stimulation protocols of two synaptically connected neurons, we infer an algorithm that reproduces the experimental data by modifying the probability of vesicle discharge as a function of the relative timing of spikes in the pre- and postsynaptic 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 50 ms before the postsynaptic spike, the probability of vesicle discharge is upregulated, while the probability of vesicle discharge is downregulated if the presynaptic spike occurs up to 50 ms after the postsynaptic spike. When neurons fire irregularly with Poisson spike trains at constant mean firing rates, the probability of vesicle discharge converges toward a characteristic value determined by the pre- and postsynaptic 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 that generalize Hebbian and Bienenstock-Cooper-Munro rules. We conclude that the proposed spike-based synaptic learning algorithm provides a general framework for regulating neurotransmitter release probability.