Synapses in the brain are bidirectionally modifiable, but the routes of induction are diverse. In various experimental paradigms, N-methyl-D-aspartate receptor-dependent long-term depression and long-term potentiation have been induced selectively by varying the membrane potential of the postsynaptic neurons during presynaptic stimulation of a constant frequency, the rate of presynaptic stimulation, and the timing of pre-and postsynaptic action potentials. In this paper, we present a mathematical embodiment of bidirectional synaptic plasticity that is able to explain diverse induction protocols with a fixed set of parameters. The key assumptions and consequences of the model can be tested experimentally; further, the model provides the foundation for a unified theory of N-methyl-D-aspartate receptordependent synaptic plasticity.S ynapses throughout the brain are bidirectionally modifiable.This property, postulated in almost every theoretical description of synaptic plasticity, has been most clearly demonstrated at the Schaffer collateral-CA1 synapse in the hippocampus. Here, it was shown that a low-frequency tetanus induces long-term depression (LTD), whereas high-frequency stimulation produces long-term potentiation (LTP) of the stimulated synapses, and that LTD and LTP are inversely related (1-4). Similar results have been obtained at excitatory synapses throughout the brain.A considerable body of evidence indicates that the important variable is actually the amount of integrated postsynaptic N-methyl-D-aspartate (NMDA) receptor (NMDAR) activation during conditioning (1, 2, 4-6). Modest NMDAR activation induces LTD, whereas strong activation produces LTP. Because of their voltage dependence, the contribution of NMDARs to synaptic transmission during conditioning stimulation varies with the level of postsynaptic depolarization. Thus, it is possible to induce LTD or LTP with a constant stimulation frequency by clamping the postsynaptic membrane potential at different values (approximately Ϫ50 mV for LTD and Ϫ20 mV for LTP).Recently, it has been demonstrated that synaptic modification also can depend on the precise timing of pre-and postsynaptic action potentials (7-11). If a presynaptic action potential occurs in a window of several tens of milliseconds before a back-propagating postsynaptic action potential, LTP is induced. In contrast, if a presynaptic action potential occurs after the postsynaptic spike, LTD is induced.Ideally, one would like to develop a unified description of bidirectional synaptic plasticity that can account for all routes of induction. One approach is to look beyond the various induction protocols to the critical role of calcium influx through NMDARs. One attractive idea is that modest increases in postsynaptic calcium trigger LTD, whereas large increases trigger . This hypothesis is consistent with the classical rate-based induction protocols if it is assumed that high-frequency stimulation triggers a larger rise in postsynaptic calcium than does low-frequency stimulation. Indeed, recent...

The receptive fields of visual cortical neurons are bidirectionally modified by sensory deprivation and experience, but the synaptic basis for these changes is unknown. Here we demonstrate bidirectional, experience-dependent regulation of the composition and function of synaptic NMDA receptors (NMDARs) in visual cortex layer 2/3 pyramidal cells of young rats. Visual experience decreases the proportion of NR2B-only receptors, shortens the duration of NMDAR-mediated synaptic currents, and reduces summation of synaptic NMDAR currents during bursts of high-frequency stimulation. Visual deprivation exerts an opposite effect. Although the effects of experience and deprivation are reversible, the rates of synaptic modification vary. Experience can induce a detectable change in synaptic transmission within hours, while deprivation-induced changes take days. We suggest that experience-dependent changes in NMDAR composition and function regulate the development of receptive field organization in visual cortex.

In reward-based learning, synaptic modifications depend on a brief stimulus and a temporally delayed reward, which poses the question of how synaptic activity patterns associate with a delayed reward. A theoretical solution to this so-called “distal reward problem” has been the notion of activity-generated ‘synaptic eligibility traces’, silent and transient synaptic tags that can be converted into long-term changes in synaptic strength by reward-linked neuromodulators. Here we report the first experimental demonstration of eligibility traces in cortical synapses. We demonstrate the Hebbian induction of distinct traces for LTP and LTD and their subsequent timing-dependent transformation into lasting changes by specific monoaminergic receptors anchored to postsynaptic proteins. Notably, the temporal properties of these transient traces allow stable learning in a recurrent neural network that accurately predicts the timing of the reward, further validating the induction/transformation of eligibility traces for LTP and LTD as a plausible synaptic substrate for reward-based learning.

PKMζ is a persistently active PKC isoform proposed to maintain late-LTP and long-term memory. But late-LTP and memory are maintained without PKMζ in PKMζ-null mice. Two hypotheses can account for these findings. First, PKMζ is unimportant for LTP or memory. Second, PKMζ is essential for late-LTP and long-term memory in wild-type mice, and PKMζ-null mice recruit compensatory mechanisms. We find that whereas PKMζ persistently increases in LTP maintenance in wild-type mice, PKCι/λ, a gene-product closely related to PKMζ, persistently increases in LTP maintenance in PKMζ-null mice. Using a pharmacogenetic approach, we find PKMζ-antisense in hippocampus blocks late-LTP and spatial long-term memory in wild-type mice, but not in PKMζ-null mice without the target mRNA. Conversely, a PKCι/λ-antagonist disrupts late-LTP and spatial memory in PKMζ-null mice but not in wild-type mice. Thus, whereas PKMζ is essential for wild-type LTP and long-term memory, persistent PKCι/λ activation compensates for PKMζ loss in PKMζ-null mice.DOI: http://dx.doi.org/10.7554/eLife.14846.001

Brief monocular deprivation during early postnatal development can lead to a depression of synaptic transmission that renders visual cortical neurons unresponsive to subsequent visual stimulation through the deprived eye. The Bienenstock-Cooper-Munro (BCM) theory proposes that homosynaptic mechanisms of long-term depression (LTD) account for the deprivation effects. Homosynaptic depression, by definition, occurs only at active synapses. Thus, in contrast to the commonly held view that the synaptic depression caused by monocular deprivation is simply a result of retinal inactivity, this theoretical framework indicates that the synaptic depression may actually be driven by the residual activity in the visually deprived retina. Here we examine the validity of this idea by comparing the consequences of brief monocular deprivation by lid suture with those of monocular inactivation by intra-ocular treatment with tetrodotoxin. Lid suture leaves the retina spontaneously active, whereas tetrodotoxin eliminates all activity. In agreement with the BCM theory, our results show that monocular lid suture causes a significantly greater depression of deprived-eye responses in kitten visual cortex than does treatment with tetrodotoxin. These findings have important implications for mechanisms of experience-dependent plasticity in the neocortex.

Spike timing dependent plasticity (STDP) is a phenomenon in which the precise timing of spikes affects the sign and magnitude of changes in synaptic strength. STDP is often interpreted as the comprehensive learning rule for a synapse – the “first law” of synaptic plasticity. This interpretation is made explicit in theoretical models in which the total plasticity produced by complex spike patterns results from a superposition of the effects of all spike pairs. Although such models are appealing for their simplicity, they can fail dramatically. For example, the measured single-spike learning rule between hippocampal CA3 and CA1 pyramidal neurons does not predict the existence of long-term potentiation one of the best-known forms of synaptic plasticity. Layers of complexity have been added to the basic STDP model to repair predictive failures, but they have been outstripped by experimental data. We propose an alternate first law: neural activity triggers changes in key biochemical intermediates, which act as a more direct trigger of plasticity mechanisms. One particularly successful model uses intracellular calcium as the intermediate and can account for many observed properties of bidirectional plasticity. In this formulation, STDP is not itself the basis for explaining other forms of plasticity, but is instead a consequence of changes in the biochemical intermediate, calcium. Eventually a mechanism-based framework for learning rules should include other messengers, discrete change at individual synapses, spread of plasticity among neighboring synapses, and priming of hidden processes that change a synapse's susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and powerefficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.iono-neuromorphic modeling | rate-based synaptic plasticity | silicon neuron | subthreshold microelectronics | VLSI circuit L earning and memory are emergent animal behaviors governed by activity-dependent neuronal adaptation rules in response to changing environments. A putative neuronal mechanism of learning and memory is Hebbian synaptic plasticity (1)-the adaptive modification of excitatory synaptic strength following paired activation of the pre-and postsynaptic neurons. Two classic paradigms for the induction of Hebbian synaptic plasticity in the mammalian hippocampus and neocortex are rate-based plasticity (2-4) [herein referred to as spike-rate-dependent plasticity (SRDP)] and spike-timing-dependent plasticity (STDP) (5-7). The SRDP induction protocols control presynaptic firing rate in order to vary the sign and magnitude of synaptic plasticity (8): a high-frequency (20-100 Hz) train of presynaptic pulses results in long-term potentiation (LTP) of the synaptic strength, whereas a low-frequency (1-5 Hz) train results in long-term depression (LTD). These protocols are consistent with the theoretical learning rule (BCM rule) proposed by Bienenstock, Cooper, and Munro (9), in which the sign and magnitude of synaptic plasticity are controlled solely by postsynaptic activity as determined by presynaptic firing rate: low postsynaptic activity weakens synaptic efficacy and high postsynaptic activity strengthens it. By contrast, the STDP induction protocol stipulates that precise timing of preand postsynaptic activities determines the direction and strength of synaptic pl...

The ability to represent time is an essential component of cognition but its neural basis is unknown. Although extensively studied both behaviorally and electrophysiologically, a general theoretical framework describing the elementary neural mechanisms used by the brain to learn temporal representations is lacking. It is commonly believed that the underlying cellular mechanisms reside in high order cortical regions but recent studies show sustained neural activity in primary sensory cortices that can represent the timing of expected reward. Here, we show that local cortical networks can learn temporal representations through a simple framework predicated on reward dependent expression of synaptic plasticity. We assert that temporal representations are stored in the lateral synaptic connections between neurons and demonstrate that reward-modulated plasticity is sufficient to learn these representations. We implement our model numerically to explain reward-time learning in the primary visual cortex (V1), demonstrate experimental support, and suggest additional experimentally verifiable predictions. reinforcment learning ͉ visual cortex O ur brains process time with such instinctual ease that the difficulty of defining what time is, in a neural sense, seems paradoxical. There is a rich literature in experimental neuroscience describing the temporal dynamics of both cellular and system-level neuronal processes and many insightful psychophysical studies have revealed perceptual correlates of time. Despite this, and the clear importance of accurate temporal processing at all levels of behavior, we still know little about how time is represented or used by the brain (1). Temporal processing is classically understood as a higher order function, and although there is some disagreement (2, 3), it is often argued that dedicated structures or regions in the brain are responsible for representing time (4). Because different mechanisms are likely responsible for computing timing at different time scales (1,5,6), and because there is evidence for modality specific temporal mechanisms (7), an alternative possibility is that timing processes develop locally within different brain regions.Recent evidence indicates that temporal representations are expressed in primary sensory cortices (8-10) and that rewardbased reinforcement can affect the form of stimulus driven activity in the primary somatosensory cortex (11-13). In particular, Shuler and Bear (9) showed that neurons in rat primary visual cortex can develop persistent activity, evoked by brief visual stimuli, that robustly represents the temporal interval between a visual stimulus and paired reward (Fig. 1). A mechanistic framework capable of describing how a neural substrate can learn the observed temporal representations does not exist.Here, we explain how these temporal signals can be encoded in recurrent excitatory synaptic connections and how a local network can learn specific temporal instantiations through reward modulated plasticity. Although our model is potentially ...