Models that contain the Model Concept : STDP

(Spike Timing Dependent Plasticity: the relative timing of spikes in pre- and post-synaptic neurons can induce long term potentiation or depression at their shared synapse(s) and neighboring synapses.) Re-display model names without descriptions

	Models	Description
1.	A simple model of neuromodulatory state-dependent synaptic plasticity (Pedrosa and Clopath, 2016)
		The model is used to illustrate the role of neuromodulators in cortical plasticity. The model consists of a feedforward network with 1 postsynaptic neuron with plastic synaptic weights. These weights are updated through a spike-timing-dependent plasticity rule. "First, we explore the ability of neuromodulators to gate plasticity by reshaping the learning window for spike-timing-dependent plasticity. Using a simple computational model, we implement four different learning rules and demonstrate their effects on receptive field plasticity. We then compare the neuromodulatory effects of upregulating learning rate versus the effects of upregulating neuronal activity. "
2.	A single-cell spiking model for the origin of grid-cell patterns (D'Albis & Kempter 2017)
		A single-cell spiking model explaining the formation of grid-cell pattern in a feed-forward network. Patterns emerge via spatially-tuned feedforward inputs, synaptic plasticity, and spike-rate adaptation.
3.	A synapse model for developing somatosensory cortex (Manninen et al 2020)
		We developed a model for an L4-L2/3 synapse in somatosensory cortex to study the role of astrocytes in modulation of t-LTD. Our model includes the one-compartmental presynaptic L4 spiny stellate cell, two-compartmental (soma and dendrite) postsynaptic L2/3 pyramidal cell, and one-compartmental fine astrocyte process.
4.	Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018)
		"Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike- Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules."
5.	Adaptive robotic control driven by a versatile spiking cerebellar network (Casellato et al. 2014)
		" ... We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. ..."
6.	An allosteric kinetics of NMDARs in STDP (Urakubo et al. 2008)
		"... We developed a detailed biophysical model of STDP and found that the model required spike timing-dependent distinct suppression of NMDARs by Ca2+-calmodulin. This led us to predict an allosteric kinetics of NMDARs: a slow and rapid suppression of NMDARs by Ca2+-calmodulin with prespiking -> postspiking and postspiking -> prespiking, respectively. We found that the allosteric kinetics, but not the conventional kinetics, is consistent with specific features of amplitudes and peak time of NMDAR-mediated EPSPs in experiments. ..." See paper for more and details.
7.	Biophysical and phenomenological models of spike-timing dependent plasticity (Badoual et al. 2006)
		"Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. ... A simplified phenomenological model is also derived..."
8.	CA1 pyr cell: phenomenological NMDAR-based model of synaptic plasticity (Dainauskas et al 2023)
		This Python code implements a phenomenological NMDA receptor-based voltage-dependent model of synaptic plasticity for CA3-CA1 synapse and shows weight changes of a synapse placed on a two-compartmental model of a hippocampal CA1 pyramidal neuron for spike-timing-dependent synaptic plasticity (STDP) and frequency-dependent synaptic plasticity stimulation protocols. The developed model predicts altered learning rules in synapses formed on the apical dendrites of the detailed compartmental model of CA1 pyramidal neuron in the presence of the GluN2B-NMDA receptor hypofunction.
9.	CA1 pyramidal neuron: synaptic plasticity during theta cycles (Saudargiene et al. 2015)
		This NEURON code implements a microcircuit of CA1 pyramidal neuron and consists of a detailed model of CA1 pyramidal cell and four types of inhibitory interneurons (basket, bistratified, axoaxonic and oriens lacunosum-moleculare cells). Synaptic plasticity during theta cycles at a synapse in a single spine on the stratum radiatum dendrite of the CA1 pyramidal cell is modeled using a phenomenological model of synaptic plasticity (Graupner and Brunel, PNAS 109(20):3991-3996, 2012). The code is adapted from the Poirazi CA1 pyramidal cell (ModelDB accession number 20212) and the Cutsuridis microcircuit model (ModelDB accession number 123815)
10.	Calcium influx during striatal upstates (Evans et al. 2013)
		"... To investigate the mechanisms that underlie the relationship between calcium and AP timing, we have developed a realistic biophysical model of a medium spiny neuron (MSN). ... Using this model, we found that either the slow inactivation of dendritic sodium channels (NaSI) or the calcium inactivation of voltage-gated calcium channels (CDI) can cause high calcium corresponding to early APs and lower calcium corresponding to later APs. We found that only CDI can account for the experimental observation that sensitivity to AP timing is dependent on NMDA receptors. Additional simulations demonstrated a mechanism by which MSNs can dynamically modulate their sensitivity to AP timing and show that sensitivity to specifically timed pre- and postsynaptic pairings (as in spike timing-dependent plasticity protocols) is altered by the timing of the pairing within the upstate. …"
11.	Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013)
		We construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials. The model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up and down states.
12.	Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
		The paper investigates the property of the electrosensory lateral line lobe (ELL) of the brain of weakly electric fish to cancel predictable stimuli. Electroreceptors on the skin encode all signals in their firing activity, but superficial pyramidal (SP) cells in the ELL that receive this feedforward input do not respond to constant sinusoidal signals. This cancellation putatively occurs using a network of feedback delay lines and burst-induced synaptic plasticity between the delay lines and the SP cell that learns to cancel the redundant input. Biologically, the delay lines are parallel fibres from cerebellar-like granule cells in the eminentia granularis posterior. A model of this network (e.g. electroreceptors, SP cells, delay lines and burst-induced plasticity) was constructed to test whether the current knowledge of how the network operates is sufficient to cancel redundant stimuli.
13.	Computing with neural synchrony (Brette 2012)
		"... In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. ..."
14.	Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)
		We developed a 3-layer sensorimotor cortical network of consisting of 704 spiking model-neurons, including excitatory, fast-spiking and low-threshold spiking interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a virtual musculoskeletal human arm, with realistic anatomical and biomechanical properties, to reach a target. Virtual arm position was used to simultaneously control a robot arm via a network interface.
15.	Cortico-striatal plasticity in medium spiny neurons (Gurney et al 2015)
		In the associated paper (Gurney et al, PLoS Biology, 2015) we presented a computational framework that addresses several issues in cortico-striatal plasticity including spike timing, reward timing, dopamine level, and dopamine receptor type. Thus, we derived a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then showed this model produces the predicted activity changes necessary for learning and extinction in an operant task. Moreover, we showed the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. The model was validated in a wider setting of action selection in basal ganglia, showing how it could account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. The code supplied here allows reproduction of the proposed process of learning in medium spiny neurons, giving the results of Figure 7 of the paper.
16.	Diffusive homeostasis in a spiking network model (Sweeney et al. 2015)
		In this paper we propose a new mechanism, diffusive homeostasis, in which neural excitability is modulated by nitric oxide, a gas which can flow freely across cell membranes. Our model simulates the activity-dependent synthesis and diffusion of nitric oxide in a recurrent network model of integrate-and-fire neurons. The concentration of nitric oxide is then used as homeostatic readout which modulates the firing threshold of each neuron.
17.	Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)
		Several recent results suggest that boosting the CREB pathway improves hippocampal-dependent memory in healthy rodents and restores this type of memory in an AD mouse model. However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and LTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach to treat AD.
18.	Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)
		"We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. ..."
19.	Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)
		This NEURON code implements a small network model (100 pyramidal cells and 4 types of inhibitory interneuron) of storage and recall of patterns in the CA1 region of the mammalian hippocampus. Patterns of PC activity are stored either by a predefined weight matrix generated by Hebbian learning, or by STDP at CA3 Schaffer collateral AMPA synapses.
20.	Endocannabinoid dynamics gate spike-timing dependent depression and potentiation (Cui et al 2016)
		The endocannabinoid (eCB) system is considered involved in synaptic depression. Recent reports have also linked eCBs to synaptic potentiation. However it is not known how eCB signaling may support such bidirectionality. To question the mechanisms of this phenomena in spike-timing dependent plasticity (STDP) at corticostriatal synapses, we combined electrophysiology experiments with biophysical modeling. We demonstrate that STDP is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Therefore, just like neurotransmitters glutamate or GABA, eCB form a bidirectional system.
21.	Fast convergence of cerebellar learning (Luque et al. 2015)
		The cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly understood. The classical long-term synaptic plasticity between parallel fibers (PFs) and Purkinje cells (PCs), which is driven by the inferior olive (IO), can only account for limited aspects of learning. Recently, the role of additional forms of plasticity in the granular layer, molecular layer and deep cerebellar nuclei (DCN) has been considered. In particular, learning at DCN synapses allows for generalization, but convergence to a stable state requires hundreds of repetitions. In this paper we have explored the putative role of the IO-DCN connection by endowing it with adaptable weights and exploring its implications in a closed-loop robotic manipulation task. Our results show that IO-DCN plasticity accelerates convergence of learning by up to two orders of magnitude without conflicting with the generalization properties conferred by DCN plasticity. Thus, this model suggests that multiple distributed learning mechanisms provide a key for explaining the complex properties of procedural learning and open up new experimental questions for synaptic plasticity in the cerebellar network.
22.	Feedforward network undergoing Up-state-mediated plasticity (Gonzalez-Rueda et al. 2018)
		Using whole-cell recordings and optogenetic stimulation of presynaptic input in anaesthetized mice, we show that synaptic plasticity rules are gated by cortical dynamics. Up states are biased towards depression such that presynaptic stimulation alone leads to synaptic depression, while connections contributing to postsynaptic spiking are protected against this synaptic weakening. We find that this novel activity-dependent and input-specific downscaling mechanism has two important computational advantages: 1) improved signal-to-noise ratio, and 2) preservation of previously stored information. Thus, these synaptic plasticity rules provide an attractive mechanism for SWS-related synaptic downscaling and circuit refinement. We simulate a feedforward network of neurons undergoing Up-state-mediated plasticity. Under this plasticity rule, presynaptic spikes alone lead to synaptic depression, whereas those followed by postsynaptic spikes within 10 ms are not changed.
23.	First-Spike-Based Visual Categorization Using Reward-Modulated STDP (Mozafari et al. 2018)
		"...Here, for the first time, we show that (Reinforcement Learning) RL can be used efficiently to train a spiking neural network (SNN) to perform object recognition in natural images without using an external classifier. We used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. In the highest layers, each neuron was assigned to an object category, and it was assumed that the stimulus category was the category of the first neuron to fire. ..."
24.	FNS spiking neural simulator; LIFL neuron model, event-driven simulation (Susi et al 2021)
		FNS is an event-driven Spiking Neural Network simulator, oriented to data-driven simulations. FNS combines spiking/synaptic level description with the event-driven approach, allowing the user to define heterogeneous modules and multi-scale connectivity with delayed connections and plastic synapses, providing fast simulations at the same time. A novel parallelization strategy is also implemented in order to further speed up simulations. FNS is based on the Leaky-Integrate and Fire with Latency (LIFL) spiking neuron model, that combines some realistic neurocomputational features to low computational complexity. FNS is written in Java, distributed as open source and protected by the GPL license.
25.	Formation of synfire chains (Jun and Jin 2007)
		"Temporally precise sequences of neuronal spikes that span hundreds of milliseconds are observed in many brain areas, including songbird premotor nucleus, cat visual cortex, and primary motor cortex. Synfire chains—networks in which groups of neurons are connected via excitatory synapses into a unidirectional chain—are thought to underlie the generation of such sequences. It is unknown, however, how synfire chains can form in local neural circuits, especially for long chains. Here, we show through computer simulation that long synfire chains can develop through spike-time dependent synaptic plasticity and axon remodeling—the pruning of prolific weak connections that follows the emergence of a finite number of strong connections. ..."
26.	Hebbian STDP for modelling the emergence of disparity selectivity (Chauhan et al 2018)
		This code shows how Hebbian learning mediated by STDP mechanisms could explain the emergence of disparity selectivity in the early visual system. This upload is a snapshot of the code at the time of acceptance of the paper. For a link to a soon-to-come git repository, consult the author's website: www.tusharchauhan.com/research/ . The datasets used in the paper are not provided due to size, but download links and expected directory-structures are. The user can (and is strongly encouraged to) experiment with their own dataset. Let me know if you find something interesting! Finally, I am very keen on a redesign/restructure/adaptation of the code to more applied problems in AI and robotics (or any other field where a spiking non-linear approach makes sense). If you have a serious proposal, don't hesitate to contact me [research AT tusharchauhan DOT com ].
27.	Heterosynaptic Spike-Timing-Dependent Plasticity (Hiratani & Fukai 2017)
		"The balance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such a balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. In this study, we developed computational models of heterosynaptic spike-timing-dependent plasticity (STDP) to show that the excitatory/inhibitory balance in dendritic branches is robustly achieved through heterosynaptic interactions between excitatory and inhibitory synapses. The model reproduces key features of experimental heterosynaptic STDP well, and provides analytical insights. ..."
28.	Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011)
		"Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. ... Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry."
29.	Learning spatial transformations through STDP (Davison, Frégnac 2006)
		A common problem in tasks involving the integration of spatial information from multiple senses, or in sensorimotor coordination, is that different modalities represent space in different frames of reference. Coordinate transformations between different reference frames are therefore required. One way to achieve this relies on the encoding of spatial information using population codes. The set of network responses to stimuli in different locations (tuning curves) constitute a basis set of functions which can be combined linearly through weighted synaptic connections in order to approximate non-linear transformations of the input variables. The question then arises how the appropriate synaptic connectivity is obtained. This model shows that a network of spiking neurons can learn the coordinate transformation from one frame of reference to another, with connectivity that develops continuously in an unsupervised manner, based only on the correlations available in the environment, and with a biologically-realistic plasticity mechanism (spike timing-dependent plasticity).
30.	Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007)
		"... How does the brain know what firing patterns of what neurons are responsible for the reward if 1) the patterns are no longer there when the reward arrives and 2) all neurons and synapses are active during the waiting period to the reward? Here, we show how the conundrum is resolved by a model network of cortical spiking neurons with spike-timing-dependent plasticity (STDP) modulated by dopamine (DA). Although STDP is triggered by nearly coincident firing patterns on a millisecond timescale, slow kinetics of subsequent synaptic plasticity is sensitive to changes in the extracellular DA concentration during the critical period of a few seconds. ... This study emphasizes the importance of precise firing patterns in brain dynamics and suggests how a global diffusive reinforcement signal in the form of extracellular DA can selectively influence the right synapses at the right time." See paper for more and details.
31.	Memory savings through unified pre- and postsynaptic STDP (Costa et al 2015)
		Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. In these simulations we demonstrate that learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.
32.	Microsaccades and synchrony coding in the retina (Masquelier et al. 2016)
		We show that microsaccades (MS) enable efficient synchrony-based coding among the primate retinal ganglion cells (RGC). We find that each MS causes certain RGCs to fire synchronously, namely those whose receptive fields contain contrast edges after the MS. The emitted synchronous spike volley thus rapidly transmits the most salient edges of the stimulus. We demonstrate that the readout could be done rapidly by simple coincidence-detector neurons, and that the required connectivity could emerge spontaneously with spike timing-dependent plasticity.
33.	Mirror Neuron (Antunes et al 2017)
		Modeling Mirror Neurons Through Spike-Timing Dependent Plasticity. This script reproduces Figure 3B.
34.	Modeling dendritic spikes and plasticity (Bono and Clopath 2017)
		Biophysical model and reduced neuron model with voltage-dependent plasticity.
35.	Modeling dentate granule cells heterosynaptic plasticity using STDP-BCM rule (Jedlicka et al. 2015)
		... Here we study how key components of learning mechanisms in the brain, namely spike timing-dependent plasticity and metaplasticity, interact with spontaneous activity in the input pathways of the neuron. Using biologically realistic simulations we show that ongoing background activity is a key determinant of the degree of long-term potentiation and long-term depression of synaptic transmission between nerve cells in the hippocampus of freely moving animals. This work helps better understand the computational rules which drive synaptic plasticity in vivo. ...
36.	Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)
		"We implemented a model of the motor system with the following components: dorsal premotor cortex (PMd), primary motor cortex (M1), spinal cord and musculoskeletal arm (Figure 1). PMd modulated M1 to select the target to reach, M1 excited the descending spinal cord neurons that drove the arm muscles, and received arm proprioceptive feedback (information about the arm position) via the ascending spinal cord neurons. The large-scale model of M1 consisted of 6,208 spiking Izhikevich model neurons [37] of four types: regular-firing and bursting pyramidal neurons, and fast-spiking and low-threshold-spiking interneurons. These were distributed across cortical layers 2/3, 5A, 5B and 6, with cell properties, proportions, locations, connectivity, weights and delays drawn primarily from mammalian experimental data [38], [39], and described in detail in previous work [29]. The network included 486,491 connections, with synapses modeling properties of four different receptors ..."
37.	NMDA subunit effects on Calcium and STDP (Evans et al. 2012)
		Effect of NMDA subunit on spike timing dependent plasticity.
38.	Online learning model of olfactory bulb external plexiform layer network (Imam & Cleland 2020)
		This model illustrates the rapid online learning of odor representations, and their recognition despite high levels of interference (other competing odorants), in a model of the olfactory bulb external plexiform layer (EPL) network. The computational principles embedded in this model are based on the those developed in the biophysical models of Li and Cleland (2013, 2017). This is a standard Python version of a model written for Intel's Loihi neuromorphic hardware platform (The Loihi code is available at https://github.com/intel-nrc-ecosystem/models/tree/master/official/epl).
39.	Opposing roles for Na+/Ca2+ exchange and Ca2+-activated K+ currents during STDP (O`Halloran 2020)
		"Sodium Calcium exchanger (NCX) proteins utilize the electrochemical gradient of Na+ to generate Ca2+ efflux (forward mode) or influx (reverse mode). In mammals, there are three unique NCX encoding genes-NCX1, NCX2, and NCX3, that comprise the SLC8A family, and mRNA from all three exchangers is expressed in hippocampal pyramidal cells. Furthermore, mutant ncx2-/- and ncx3-/- mice have each been shown to exhibit altered long-term potentiation (LTP) in the hippocampal CA1 region due to delayed Ca2+ clearance after depolarization that alters synaptic transmission. In addition to the role of NCX at the synapse of hippocampal subfields required for LTP, the three NCX isoforms have also been shown to localize to the dendrite of hippocampal pyramidal cells. In the case of NCX1, it has been shown to localize throughout the basal and apical dendrite of CA1 neurons where it helps compartmentalize Ca2+ between dendritic shafts and spines. Given the role for NCX and calcium in synaptic plasticity, the capacity of NCX splice-forms to influence backpropagating action potentials has clear consequences for the induction of spike-timing dependent synaptic plasticity (STDP). To explore this, we examined the effect of NCX localization, density, and allosteric activation on forward and back propagating signals and, next employed a STDP paradigm to monitor the effect of NCX on plasticity using back propagating action potentials paired with EPSPs. From our simulation studies we identified a role for the sodium calcium exchange current in normalizing STDP, and demonstrate that NCX is required at the postsynaptic site for this response. We also screened other mechanisms in our model and identified a role for the Ca2+ activated K+ current at the postsynapse in producing STDP responses. Together, our data reveal opposing roles for the Na+/Ca2+ exchanger current and the Ca2+ activated K+ current in setting STDP."
40.	Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
		We simulate a LIF neuron equipped with STDP. A pattern repeats in its inputs. The LIF progressively becomes selective to the repeating pattern, in an optimal manner.
41.	Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
		The model demonstrates how a common oscillatory drive for a group of neurons formats and reliabilizes their spike times - through an activation-to-phase conversion - so that repeating activation patterns can be easily detected and learned by a downstream neuron equipped with STDP, and then recognized in just one oscillation cycle.
42.	Polychronization: Computation With Spikes (Izhikevich 2005)
		"We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spiketiming- dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. ... To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. ..."
43.	Pyramidal neuron conductances state and STDP (Delgado et al. 2010)
		Neocortical neurons in vivo process each of their individual inputs in the context of ongoing synaptic background activity, produced by the thousands of presynaptic partners a typical neuron has. That background activity affects multiple aspects of neuronal and network function. However, its effect on the induction of spike-timing dependent plasticity (STDP) is not clear. Using the present biophysically-detailed computational model, it is not only able to replicate the conductance-dependent shunting of dendritic potentials (Delgado et al,2010), but show that synaptic background can truncate calcium dynamics within dendritic spines, in a way that affects potentiation more strongly than depression. This program uses a simplified layer 2/3 pyramidal neuron constructed in NEURON. It was similar to the model of Traub et al., J Neurophysiol. (2003), and consisted of a soma, an apical shaft, distal dendrites, five basal dendrites, an axon, and a single spine. The spine’s location was variable along the apical shaft (initial 50 μm) and apical. The axon contained an axon hillock region, an initial segment, segments with myelin, and nodes of Ranvier, in order to have realistic action potential generation. For more information about the model see supplemental material, Delgado et al 2010.
44.	Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
		Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones.
45.	Reproducing infra-slow oscillations with dopaminergic modulation (Kobayashi et al 2017)
		" ... In this paper, to reproduce ISO (Infra-Slow Oscillations) in neural networks, we show that dopaminergic modulation of STDP is essential. More specifically, we discovered a close relationship between two dopaminergic effects: modulation of the STDP function and generation of ISO. We therefore, numerically investigated the relationship in detail and proposed a possible mechanism by which ISO is generated."
46.	Reward modulated STDP (Legenstein et al. 2008)
		"... This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. ... In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics."
47.	Self-influencing synaptic plasticity (Tamosiunaite et al. 2007)
		"... Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. ..."
48.	Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)
		"... We developed a model of sensory and motor neocortex consisting of 704 spiking model-neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a 2-joint virtual arm to reach to a fixed target. ... "
49.	Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
		"... In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. ... Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. ... After training with natural images, the neurons display heightened sensitivity to specific colors. ..."
50.	Spike-timing dependent inhibitory plasticity for gating bAPs (Wilmes et al 2017)
		"Inhibition is known to influence the forward-directed flow of information within neurons. However, also regulation of backward-directed signals, such as backpropagating action potentials (bAPs), can enrich the functional repertoire of local circuits. Inhibitory control of bAP spread, for example, can provide a switch for the plasticity of excitatory synapses. Although such a mechanism is possible, it requires a precise timing of inhibition to annihilate bAPs without impairment of forward-directed excitatory information flow. Here, we propose a specific learning rule for inhibitory synapses to automatically generate the correct timing to gate bAPs in pyramidal cells when embedded in a local circuit of feedforward inhibition. Based on computational modeling of multi-compartmental neurons with physiological properties, we demonstrate that a learning rule with anti-Hebbian shape can establish the required temporal precision. ..."
51.	Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)
		"... The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. ..."
52.	Stability of complex spike timing-dependent plasticity in cerebellar learning (Roberts 2007)
		"Dynamics of spike-timing dependent synaptic plasticity are analyzed for excitatory and inhibitory synapses onto cerebellar Purkinje cells. The purpose of this study is to place theoretical constraints on candidate synaptic learning rules that determine the changes in synaptic efficacy due to pairing complex spikes with presynaptic spikes in parallel fibers and inhibitory interneurons. ..."
53.	STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
		The model demonstrates that a neuron equipped with STDP robustly detects repeating rate patterns among its afferents, from which the spikes are generated on the fly using inhomogenous Poisson sampling, provided those rates have narrow temporal peaks (10-20ms) - a condition met by many experimental Post-Stimulus Time Histograms (PSTH).
54.	STDP and BDNF in CA1 spines (Solinas et al. 2019)
		Storing memory traces in the brain is essential for learning and memory formation. Memory traces are created by joint electrical activity in neurons that are interconnected by synapses and allow transferring electrical activity from a sending (presynaptic) to a receiving (postsynaptic) neuron. During learning, neurons that are co-active can tune synapses to become more effective. This process is called synaptic plasticity or long-term potentiation (LTP). Timing-dependent LTP (t-LTP) is a physiologically relevant type of synaptic plasticity that results from repeated sequential firing of action potentials (APs) in pre- and postsynaptic neurons. T-LTP is observed during learning in vivo and is a cellular correlate of memory formation. T-LTP can be elicited by different rhythms of synaptic activity that recruit distinct synaptic growth processes underlying t-LTP. The protein brain-derived neurotrophic factor (BDNF) is released at synapses and mediates synaptic growth in response to specific rhythms of t-LTP stimulation, while other rhythms mediate BDNF-independent t-LTP. Here, we developed a realistic computational model that accounts for our previously published experimental results of BDNF-independent 1:1 t-LTP (pairing of 1 presynaptic with 1 postsynaptic AP) and BDNF-dependent 1:4 t-LTP (pairing of 1 presynaptic with 4 postsynaptic APs). The model explains the magnitude and time course of both t-LTP forms and allows predicting t-LTP properties that result from altered BDNF turnover. Since BDNF levels are decreased in demented patients, understanding the function of BDNF in memory processes is of utmost importance to counteract Alzheimer’s disease.
55.	STDP and NMDAR Subunits (Gerkin et al. 2007)
		The paper argues for competing roles of NR2A- and NR2B-containing NMDARs in spike-timing-dependent plasticity. This simple dynamical model recapitulates the results of STDP experiments involving selective blockers of NR2A- and NR2B-containing NMDARs, for which the stimuli are pre- and postsynaptic spikes in varying combinations. Experiments were done using paired recordings from glutamatergic neurons in rat hippocampal cultures. This model focuses on the dynamics of the putative potentiation and depression modules themselves, and their interaction For detailed dynamics involving NMDARs and Ca2+ transients, see Rubin et al., J. Neurophys., 2005.
56.	STDP depends on dendritic synapse location (Letzkus et al. 2006)
		This model was published in Letzkus, Kampa & Stuart (2006) J Neurosci 26(41):10420-9. The simulation creates several plots showing voltage and NMDA current and conductance changes at different apical dendritic locations in layer 5 pyramidal neurons during STDP induction protocols. Created by B. Kampa (2006).
57.	STDP promotes synchrony of inhibitory networks in the presence of heterogeneity (Talathi et al 2008)
		"Recently Haas et al. (J Neurophysiol 96: 3305–3313, 2006), observed a novel form of spike timing dependent plasticity (iSTDP) in GABAergic synaptic couplings in layer II of the entorhinal cortex. Depending on the relative timings of the presynaptic input at time tpre and the postsynaptic excitation at time tpost, the synapse is strengthened (delta_t = t(post) - t(pre) > 0) or weakened (delta_t < 0). The temporal dynamic range of the observed STDP rule was found to lie in the higher gamma frequency band (> or = 40 Hz), a frequency range important for several vital neuronal tasks. In this paper we study the function of this novel form of iSTDP in the synchronization of the inhibitory neuronal network. In particular we consider a network of two unidirectionally coupled interneurons (UCI) and two mutually coupled interneurons (MCI), in the presence of heterogeneity in the intrinsic firing rates of each coupled neuron. ..."
58.	Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)
		Learning in the brain requires complementary mechanisms: potentiation and activity-dependent homeostatic scaling. We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy. We discuss some of the issues that arise when considering synaptic scaling in such a model, and show that scaling regulates activity whilst allowing learning to remain unaltered.
59.	Theta phase precession in a model CA3 place cell (Baker and Olds 2007)
		"... The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. Spike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell’s place field. ... Through selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity."
60.	Voltage-based STDP synapse (Clopath et al. 2010)
		Implementation of the STDP rule by Clopath et al., Nat. Neurosci. 13(3):344-352,2010 STDP mechanism added to the AlphaSynapse in NEURON.

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