Harel Z. Shouval

A unified model of NMDA receptor-dependent bidirectional synaptic plasticity

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 receptor-dependent synaptic plasticity.

Visual Experience and Deprivation Bidirectionally Modify the Composition and Function of NMDA Receptors in Visual Cortex

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.

Monocular deprivation induces homosynaptic long-term depression in visual cortex

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.

A biophysical model of bidirectional synaptic plasticity: Dependence on AMPA and NMDA receptors

In many regions of the brain, including the mammalian cortex, the magnitude and direction of activity-dependent changes in synaptic strength depend on the frequency of presynaptic stimulation (synaptic plasticity), as well as the history of activity at those synapses (metaplasticity). We present a model of a molecular mechanism of bidirectional synaptic plasticity based on the observation that long-term synaptic potentiation (LTP) and long-term synaptic depression (LTD) correlate with the phosphorylation/dephosphorylation of sites on the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor subunit protein GluR1. The primary assumption of the model, for which there is wide experimental support, is that postsynaptic calcium concentration and consequent activation of calcium-dependent protein kinases and phosphatases are the triggers for the induction of LTP/LTD. As calcium influx through the n-methyl-d-aspartate (NMDA) receptor plays a fundamental role in the induction of LTP/LTD, changes in the properties of NMDA receptor-mediated calcium influx will dramatically affect activity-dependent synaptic plasticity (metaplasticity). We demonstrate that experimentally observed metaplasticity can be accounted for by activity-dependent regulation of NMDA receptor subunit composition and function. Our model produces a frequency-dependent LTP/LTD curve with a sliding synaptic modification threshold similar to what has been proposed theoretically by Bienenstock, Cooper, and Munro and observed experimentally.

Spike timing dependent plasticity: a consequence of more fundamental learning rules.

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 synapses susceptibility to future change. Mechanism-based models provide a rich framework for the computational representation of synaptic plasticity.

Converging evidence for a simplified biophysical model of synaptic plasticity

Abstract. Different mechanisms that could form the molecular basis for bi-directional synaptic plasticity have been identified experimentally and corresponding biophysical models can be constructed. However, such models are complex and therefore it is hard to deduce their consequences to compare them to existing abstract models of synaptic plasticity. In this paper we examine two such models: a phenomenological one inspired by the phenomena of AMPA receptor insertion, and a more complex biophysical model based on the phenomena of AMPA receptor phosphorylation. We show that under certain approximations both these models can be mapped on to an equivalent, calcium-dependent, differential equation. Intracellular calcium concentration varies locally in each postsynaptic compartment, thus the plasticity rule we extract is a single-synapse rule. We convert this single synapse plasticity equation to a multi-synapse rule by incorporating a model of the NMDA receptor. Finally we suggest a mathematical embodiment of metaplasticity, which is consistent with observations on NMDA receptor properties and dependence on cellular activity. These results, in combination with some of our previous results, produce converging evidence for the calcium control hypothesis including a dependence of synaptic plasticity on the level of intercellular calcium as well as on the temporal pattern of calcium transients.

A biophysically-based neuromorphic model of spike rate- and timing-dependent 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 power-efficient 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.

Compensation for PKMζ in long-term potentiation and spatial long-term memory in mutant mice

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

Receptive Field Formation in Natural Scene Environments: Comparison of Single Cell Learning Rules

We study several statistically and biologically motivated learning rules using the same visual environment: one made up of natural scenes and the same single-cell neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. Included in these rules are kurtosis and skewness maximization, the quadratic form of the Bienenstock-Cooper-Munro (BCM) learning rule, and single-cell independent component analysis. Using a structure removal method, we demonstrate that receptive fields developed using these rules depend on a small portion of the distribution. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when the distribution contains kurtotic directions, although the BCM modification equations are computationally simpler.

Learning reward timing in cortex through reward dependent expression of synaptic plasticity

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.