Local, calcium- and reward-based synaptic learning rule that enhances dendritic nonlinearities can solve the nonlinear feature binding problem

Classically, single neurons in the nervous system have been thought to operate as simple linear integrators such that the nonlinearity of dendrites can be neglected (McCulloch and Pitts, 1943). Based on this simplification, powerful artificial neural systems have been created that outperform humans on multiple tasks (Silver et al., 2018). However, in recent decades, it has been shown that active dendritic properties shape neuronal output and that dendrites display nonlinear integration of input signals (Antic et al., 2010). These dendritic nonlinearities enable a neuron to perform sophisticated computations (Tran-Van-Minh et al., 2015; Gidon et al., 2020), expanding its computational power beyond what is available with the somatic voltage threshold and making it similar to a multilayer artificial neural network (Poirazi et al., 2003).

A dendritic nonlinearity common among projection neurons in several brain areas is the NMDA-dependent plateau potential (Oikonomou et al., 2014). Plateau potentials are regenerative, all-or-none, supralinear voltage elevations triggered by spatiotemporally clustered glutamatergic input (Schiller et al., 2000; Polsky et al., 2004; Losonczy and Magee, 2006; Major et al., 2008; Larkum et al., 2009; Lavzin et al., 2012; Xu et al., 2012). Such plateaus require that nearby spines are coactivated, but the spatial requirement is somewhat loose as even single dendritic branches have been proposed to act as computational units (Branco and Häusser, 2010; Losonczy and Magee, 2006). Nevertheless, multiple so-called hotspots, preferentially responsive to different input values or features, are known to form with close dendritic proximity (Jia et al., 2010; Chen et al., 2011; Varga et al., 2011). Such functional synaptic clusters are present in multiple species, developmental stages, and brain regions (Kleindienst et al., 2011; Takahashi et al., 2012; Winnubst et al., 2015; Wilson et al., 2016; Iacaruso et al., 2017; Scholl et al., 2017; Niculescu et al., 2018; Kerlin et al., 2019; Ju et al., 2020). Hence, multiple features are commonly clustered in a single dendritic branch, indicating that this could be the neural substrate where combinations of simple features into more complex items occur.

Combinations of features in dendritic branches further provide single neurons with the possibility to solve linearly non-separable tasks, such as the nonlinear feature binding problem (NFBP) (Tran-Van-Minh et al., 2015; Gidon et al., 2020). In its most basic form, the NFBP involves discriminating between two groups of feature combinations. This problem is nonlinear because the neuron must learn to respond only to specific feature combinations, even though all features contribute equally in terms of synaptic input. A commonly used example involves two different shapes combined with two different colors, resulting in four total combinations. Out of these, the neuron should respond only to two specific feature combinations (exemplified in Figure 1A and B).

Figure 1

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As a task, the NFBP is relevant to brain regions which perform integration of multimodal input signals, or signals representing different features of the same modality (Roskies, 1999). It is usually illustrated with examples from the visual system, as in Figure 1A; Roskies, 1999; von der Malsburg, 1999; Tran-Van-Minh et al., 2015. A region that integrates multimodal inputs, such as sensory information and motor-related signals, is the input nucleus of the basal ganglia, the striatum (Reig and Silberberg, 2014; Johansson and Silberberg, 2020), and this system will be used in the present modeling study. Here, however, we will continue to illustrate the NFBP with the more intuitive features borrowed from the visual field, although for the dorsal striatum these features would rather map onto different sensory- and motor-related features. Plateau potentials and some clustering of input have been demonstrated in striatal projection neurons (SPNs) (Plotkin et al., 2011; Oikonomou et al., 2014; Du et al., 2017; Hwang et al., 2022; Day et al., 2024; Sanabria et al., 2024).

In addition to integrating converging input from the cortex and the thalamus, the striatum is densely innervated by midbrain dopaminergic neurons which carry information about rewarding stimuli (Schultz, 2007; Matsuda et al., 2009; Surmeier et al., 2010). As such, the striatum is thought to be an important site of reward learning, associating actions with outcomes based on neuromodulatory cues. In this classical framework, peaks in dopamine signify rewarding outcomes and pauses in dopamine represent the omission of expected rewards (Schultz et al., 1997). Dopamine signals further control the synaptic plasticity of corticostriatal synapses on the SPNs (Figure 1C). In direct pathway SPNs (dSPNs) expressing the D1 receptor, a dopamine peak together with significant calcium influx through NMDA receptors triggers synaptic strengthening (long-term potentiation [LTP]). Conversely, when little or no dopamine is bound to the D1 receptors, as during a dopamine pause, and there is significant calcium influx through L-type calcium channels, synaptic weakening occurs (long-term depression [LTD], see Figure 1D; Shen et al., 2008; Fino et al., 2010; Plotkin et al., 2013).

The ability to undergo LTP or LTD is itself regulated (Huang et al., 1992), a concept termed metaplasticity (Abraham and Bear, 1996). Metaplasticity refers to changes in synaptic plasticity driven by prior synaptic activity (Frey et al., 1995) or by neuromodulators (Moody et al., 1999), effectively making plasticity itself adaptable. Metaplasticity can further regulate synaptic physiology to shape future plasticity without directly altering synaptic efficacy, acting as a homeostatic mechanism to keep synapses within an optimal dynamic range (Abraham, 2008). Previous theoretical studies have demonstrated the essential role of metaplasticity in maintaining stability in synaptic weight distributions (Bienenstock et al., 1982; Fusi et al., 2005; Clopath et al., 2010; Benna and Fusi, 2016; Zenke and Gerstner, 2017).

If dopamine peaks are associated with the relevant feature combinations in the NFBP and dopamine pauses with the irrelevant ones, they trigger LTP in synapses representing the relevant feature combinations and LTD in those representing irrelevant combinations. If, after learning, the relevant feature combinations have strong enough synapses so they can evoke plateau potentials, while the irrelevant feature combinations have weak enough synapses so they don’t evoke plateaus, the outcome of this learning process should be a synaptic arrangement that could solve the NFBP (Figure 1B; Tran-Van-Minh et al., 2015). In line with this, it has been demonstrated that the NFBP can be solved in abstract neuron models where the soma and dendrites are represented by single electrical compartments and where neuronal firing and plateau potentials are phenomenologically represented by instantaneous firing rate functions (Legenstein and Maass, 2011; Schiess et al., 2016). Good performance on the NFBP has also been demonstrated with biologically detailed models (Bicknell and Häusser, 2021). This solution used a multicompartmental model of a single pyramidal neuron, including both excitatory and inhibitory synapses and supralinear NMDA depolarizations. Synapses representing different features were randomly dispersed throughout the dendrites, and a phenomenological precalculated learning rule – dependent on somatic spike timing and high local dendritic voltage – was used to optimize the strength of the synapses. The solution did, however, depend on a form of supervised learning as somatic current injections were used to raise the spiking probability of the relevant feature combinations.

In this study, we ask whether – and under what conditions – the theoretical solution to the NFBP can be achieved in a biophysically detailed model of an SPN using only local, biologically-grounded learning rules. We frame this paper around two questions: First, can a single dSPN equipped with only calcium- and dopamine-dependent excitatory plasticity solve the NFBP when the relevant features are pre-clustered on one dendritic branch? Second, if that mechanism is insufficient – as with randomly distributed or very distal inputs – does adding inhibitory plasticity restore plateau-based nonlinear computation and spiking?

To answer these questions, we adopt an approach that relies on the following key mechanisms:

A local learning rule: We develop a learning rule driven by local calcium dynamics in the synapse and by reward signals from the neuromodulator dopamine. This plasticity rule is based on the known synaptic machinery for triggering LTP or LTD at the corticostriatal synapse onto dSPNs (Shen et al., 2008). Importantly, the rule does not rely on supervised learning paradigms, and no separate training and testing phase is required.

Robust dendritic nonlinearities: According to Tran-Van-Minh et al., 2015, sufficient supralinear integration is needed to ensure that, e.g., two inputs (one feature combination in the NFBP, Figure 1A) on the same dendrite generate greater somatic depolarization than if those inputs were distributed across different dendrites. To accomplish this, we generate dendritic plateau potentials using the approach of Trpevski et al., 2023.

Metaplasticity: Our simulations demonstrate that metaplasticity is necessary for synaptic weights to remain stable and within physiologically realistic ranges, regardless of their initial values.

We first demonstrate the effectiveness of the proposed learning rule under the assumption of pre-existing clustered synapses for each individual feature, as suggested by Tran-Van-Minh et al., 2015. These clustered synapses are trained to a degree where they can reliably evoke robust plateau potentials for the relevant feature combinations required to solve the NFBP, while synapses representing irrelevant features are weakened (illustrated in Figure 1B).

We then extend the analysis by applying the learning rule to more randomly distributed synapses, which initially exhibit minimal local supralinear integration. However, when incorporating the assumption that branch-specific plasticity mechanisms are at play, supralinear integration emerges within distinct dendritic branches. This suggests that branch-specific plasticity could play a critical role in enabling single neurons to solve nonlinear problems. Furthermore, we explore an activity-dependent rule for GABAergic plasticity and demonstrate its potential importance in shaping dendritic nonlinearities. This mechanism may thus further enhance computational capabilities by refining and stabilizing the integration of inputs across dendritic branches.

Although brain systems like the striatum, which integrate multimodal inputs, somehow solve nonlinear problems at the network or systems level, it remains unclear whether individual neurons in the brain regularly solve the NFBP. Our investigation suggests, however, that single SPNs possess the computational capacity to address linearly non-separable tasks. This is achieved by leveraging the organism’s performance feedback, represented by dopamine peaks (success) and dopamine pauses (failure), in combination with their ability to generate dendritic plateau potentials. Since the mechanisms used in the rule are general to the brain, this capability may also extend to other projection neurons capable of producing dendritic plateaus, such as pyramidal neurons. However, the specific feedback mechanisms, represented here by dopamine, would need to be associated with alternative neuromodulatory signals depending on the type of neuron and synapse involved.

In this article, we studied whether single neurons can solve linearly non-separable computational tasks, represented by the NFBP, by using a biophysically detailed multicompartment dSPN model. Based on the synaptic machinery of corticostriatal synapses onto dSPNs, we propose a learning rule that uses local synaptic calcium concentration and dopamine feedback signals: rewards for relevant stimuli and omitted rewards for irrelevant stimuli. Assuming first that single features in the NFBP are represented by clustered synapses, we show that the learning rule can solve the NFBP by strengthening (or stabilizing) synaptic clusters for relevant stimuli and weakening clusters for the irrelevant stimuli. The feature combinations for the relevant stimuli, stored in strengthened synaptic clusters, trigger supralinear dendritic responses in the form of plateau potentials, which is an important ingredient for the solution of the NFBP, as plateaus significantly increase the likelihood of neuronal spiking in SPNs in a robust way (Du et al., 2017).

The location of the synaptic clusters along the dendrites influenced the performance on the NFBP. In our model, the region for optimal performance was around 150 micrometers away from the soma, at about the same distance as where the somatic depolarization and induced spike probability, following activation of clustered synaptic input, was largest in the model by Lindroos and Hellgren Kotaleski, 2021. Clusters placed further away produce smaller somatic depolarizations, due to dendritic filtering (Major et al., 2008), and as a consequence, do not control the likelihood of somatic spiking as decisively. As the supralinear dendritic response is necessary for discriminating the relevant from the irrelevant stimuli, the performance with distally placed clusters decreases somewhat.

Further, we relaxed the assumption of pre-clustered synapses by using randomly distributed synapses for each feature. In this scenario, only one relevant stimulus is sometimes learned in a dendritic branch by the randomly distributed synapses. In the random setup, the glutamate spillover models were updated to include the synapses in the whole dendritic branch, building on the notion that the single branch acts as a single computational unit (Branco and Häusser, 2010; Losonczy and Magee, 2006). The realism of this spillover assumption remains open to debate. In actual dendritic branches, however, the diffusion of signaling molecules likely plays a crucial role in both synaptic plasticity at existing synapses and for locally induced structural plasticity (Nishiyama and Yasuda, 2015; Chater et al., 2024). These processes may both enhance branch-specific supralinearities in qualitatively similar ways as when using spillover, but the mechanisms are not explored in the current model.

By using a phenomenological inhibitory plasticity rule based on the Bienenstock-Cooper-Munro (BCM) formalism (Bienenstock et al., 1982), we also show that inhibitory synapses can significantly improve performance on the NFBP. This is in line with earlier theoretical studies where negative synaptic weights were required to solve the NFBP (Schiess et al., 2016). In our setup with pre-existing synaptic clusters, inhibitory synapses made learning faster and increased performance by inhibiting supralinear NMDA responses for the irrelevant stimuli. This was specifically true in distal dendrites where the input impedance is higher (Branco et al., 2010). The threshold for plateau potential initiation is also lower in distal dendrites compared to proximal (Losonczy and Magee, 2006), which likely will further extend the influence of inhibition in this region (Doron et al., 2017; Du et al., 2017). Similarly, in the scenario with distributed synapses, inhibition enables one of the relevant stimuli to be reliably encoded in the dendritic branch by strengthening the inhibitory synapses for features different from those of the encoded stimulus. Together, it therefore seems like inhibition not only has a role in learning (Chen et al., 2015; Cichon and Gan, 2015), but also improves the ability of the neuron to discriminate between stimuli with shared features.

In calcium-dependent plasticity models, a ‘no man’s land’ has sometimes been proposed as a region where synaptic weights remain largely unchanged within a specific calcium range, reducing sensitivity to fluctuations and improving stability (Lisman, 2001; Moldwin et al., 2024). For our inhibitory rule, which builds on a BCM-like mechanism, introduction of such a mechanism in future work could make the system less sensitive to fluctuations in calcium concentration.

Although our learning rule infrequently solves the NFBP when used with only randomly distributed synapses, it can always learn to perform a linearly separable task, such as learning to respond to only one relevant stimulus (e.g. red strawberry). Moreover, the learning rule is general enough so that in addition to the feature-specific inputs related to the task, it can handle feature-unspecific inputs that might or might not be related to the NFBP. Finally, the learning rule is always ‘on’, continuously updating synapses with each stimulus presentation, which is a more realistic mechanism compared to using separate training and testing phases as in the field of machine learning. The synaptic weights automatically stabilize in the model when the performance improves, with metaplasticity playing a crucial role in this process. That is, rewards are then seen very regularly as the neuron has learned to spike for the relevant stimuli, while omitted rewards rarely occur as the neuron stays silent when the irrelevant stimuli are provided.

When formulating the learning rule in this article, our goal was to base it on what is known regarding the synaptic machinery in corticostriatal synapses. This implied that the learning rule is based on the local calcium activity and on dopamine signals. Feedback from the dopamine system can also be viewed as an innate, evolutionarily encoded ‘supervisor’, which instructs neurons which feature combinations are beneficial and which ones should be avoided. However, in our case, we do not use additional excitation to promote somatic spiking for only the relevant feature combinations, and in that sense, the learning rule does not require a supervised learning paradigm. However, since the SPNs rest at very hyperpolarized membrane potential, our setup includes distributed excitatory inputs which are feature-unspecific in order to make sure that the neuron spikes for all stimuli, especially at the beginning of training. These additional inputs are on average weakened as learning progresses (as they are activated for all stimuli and thus often receive negative feedback). That general or noisy inputs are reduced during learning is in line with the observed reduction of execution variability during motor learning as a novice becomes an expert (Kawai et al., 2015). Specifically, the underlying neuronal representation of corticostriatal synapses undergoes a similar change during learning (Santos et al., 2015).

Since each synapse has its own calcium response, it is important for the learning rule to be able to follow individual synaptic activities. The LTP plasticity kernel in our model is for this reason itself plastic (metaplasticity), meaning that it changes its calcium dependence as a function of the reward history (dopamine peaks and pauses). This setup helps the model separate clustered synaptic input from the feature-unspecific input at the beginning of training, as only clustered synapses will see enough calcium to fall within the LTP plastic range (LTP kernel).

We further use an asymmetric metaplasticity rule, where negative feedback causes a larger shift of the LTP kernel than a positive. This was necessary in order to prevent LTP in synapses that should ultimately undergo LTD. Hence, similarly to the classical loss-aversion tendency described in economic decision theory (Kahneman and Tversky, 1979), the model, through its requirement for asymmetric metaplasticity, predicts that negative feedback has a bigger impact on changing a well-learned behavior at the single-cell level than positive feedback. Dopamine signaling has also been linked as a neural substrate to the decision-making theory mentioned above (Stauffer et al., 2016).

It is not known whether single neurons solve the NFBP or other linearly non-separable tasks. However, many brain nuclei receive convergent inputs from numerous other brain nuclei, acting as integratory hubs (van den Heuvel and Sporns, 2013). Since feature binding evidently occurs in the brain, and functional clusters for single features such as visual stimulus orientation, receptive fields, color, or sound intensity exist on single neurons (Chen et al., 2011; Wilson et al., 2016; Iacaruso et al., 2017; Scholl et al., 2017; Ju et al., 2020), it is possible that the NFBP is a relevant task for neurons to solve. How brain regions with synaptic machinery different from that of striatal dSPN might solve the NFBP remains an open question, and reliance on other neuromodulatory signals may be part of the answer. For example, in the striatum, the indirect pathway SPNs (iSPN) have analogous synaptic machinery to the one in dSPNs, requiring calcium influx from the same sources for LTP and LTD, but are differently responsive to dopamine (Shen et al., 2008). In iSPNs, a dopamine pause, together with a peak in adenosine, is required to trigger LTP, whereas a dopamine peak without peaks in adenosine rather promotes LTD (Shen et al., 2008; Nair et al., 2015). Therefore, we expect that an analogously formulated learning rule will also solve the NFBP in iSPNs, activating them for irrelevant feature combinations to, e.g., suppress movement, and suppressing their activity for relevant feature combinations to facilitate movement. In addition, LTP in dSPNs is significantly facilitated by co-regulation of other neuromodulatory systems, such as when there is a coincident acetylcholine pause with the dopamine peak, which we have not explicitly included in the model (Nair et al., 2015; Bruce et al., 2019; Reynolds et al., 2022).

In this paper, we introduce a local, calcium- and reward-based synaptic learning rule, constrained by experimental findings, to investigate learning in SPNs. The learning rule operates based on the changes in local calcium concentration resulting from synaptic activation patterns that evoke dendritic nonlinearities, such as plateau potentials. Such events affecting the postsynaptic calcium concentration can enable learning of the NFBP. The learning rule is embedded in a biophysically detailed model of a dSPN built and simulated in the NEURON software, v8.2 (Carnevale and Hines, 2006). Here, we will focus on the setup of the learning rule and only give a short summary of the neuron and synapse models and emphasize changes compared to previously published versions. For a detailed description of the neuron model setup, see Lindroos et al., 2018; Lindroos and Hellgren Kotaleski, 2021. The modeling of plateau potentials is explained in Trpevski et al., 2023. See also the section Code and data availability below.

Learning rule

Excitatory synaptic plasticity

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The learning rule is grounded in experimental evidence, indicating that striatal LTP relies on NMDA receptor activation and the presence of dopamine, whereas LTD depends on the activation of L-type calcium channels (Ca_v1.3) and mGluR receptors in the absence of dopamine (Shen et al., 2008; Fino et al., 2010; Plotkin et al., 2013; Yagishita et al., 2014; Fisher et al., 2017; Shindou et al., 2019). Under basal dopamine levels, no significant synaptic plasticity is assumed to occur. This rule outlines a reward-based learning mechanism where a dopamine peak enhances synaptic weight via an LTP plasticity kernel, while a dopamine pause reduces synaptic weight through an LTD kernel. Thus, dopamine functions as a switch, determining whether LTP or LTD pathways are activated (as illustrated in Figure 1D). Notably, synaptic weights affect both AMPA and NMDA receptors, consistent with findings that the NMDA-to-AMPA ratio remains unchanged following LTP (Watt et al., 2004). Additionally, the previously described spillover mechanism is linked to synaptic weights, representing an attempt to associate plasticity with increased glutamate spillover resulting from the retraction of astrocytic processes from the spine (Henneberger et al., 2020). The excitatory synaptic plasticity rule encompasses key components such as LTP, LTD, and metaplasticity, each of which will be elaborated upon in the subsequent sections.

LTP

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The LTP process is initiated by elevated dopamine levels, following calcium influx through NMDA receptors. Synaptic strength is adjusted using the LTP kernel, a bell-shaped function where the maximum increase occurs when peak NMDA calcium levels are at the kernel’s midpoint; deviations above or below this midpoint result in smaller increases (Figure 7A, top panel). The left side of the LTP kernel represents the lower calcium threshold necessary for LTP induction, while the right side indicates an upper calcium threshold beyond which LTP does not occur. This framework, in conjunction with metaplasticity, establishes an upper boundary for synaptic strength, ensuring that synaptic weights do not exceed a certain limit (Zenke and Gerstner, 2017; Zenke et al., 2017). Since each synapse experiences unique calcium levels (due to different synaptic conductance, local input resistance, etc.), adjusting the calcium dependence of the LTP kernel allows for individualized tuning of synaptic weights (see Figure 7A and C for illustrations).

Figure 7

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The update of the synaptic weights follows a rule formulated using the derivative of the sigmoid function (Equation 2). The sigmoid function (Equation 1) transforms the calcium concentration into a normalized range between 0 and 1:

(1) ${\displaystyle {\displaystyle \sigma (Ca,\theta ,\beta )=\frac{1}{1+e^{-\beta (Ca-\theta )}}}}$

where θ represents the midpoint of the transition, representing the calcium concentration at which the function increases most rapidly, and β determines the steepness of this transition.

The derivative of the sigmoid function, given by:

(2) ${\displaystyle {\displaystyle {\sigma }^{\prime }(Ca,\theta ,\beta )=\beta \cdot \sigma (Ca,\theta ,\beta )\cdot \left(1-\sigma (Ca,\theta ,\beta )\right)}}$

produces a bell-shaped curve, which forms the LTP kernel. This kernel peaks at the midpoint θ, where synaptic modification is maximized and decays symmetrically on each side. The parameter β here controls the width of the kernel, thereby regulating the sensitivity of synaptic weight changes to calcium fluctuations.

Based on this general description of the mathematical formulas underlying the LTP kernel, the LTP learning rule for the synaptic weights is formulated as:

(3) ${\displaystyle {\displaystyle \mathrm{\Delta }{w}_{\text{ltp}}={\eta }_{\text{ltp}}\cdot {\sigma }^{\prime }(C{a}_{\text{NMDA}},{\theta }_{\text{ltp}},{\beta }_{\text{ltp}})}}$

where $\mathrm{\Delta }{w}_{\text{ltp}}$ represents the change in synaptic weight, ${\eta }_{\text{ltp}}$ is the learning rate, and $[Ca{]}_{\text{NMDA}}$ denotes the calcium concentration mediated by NMDA receptors. The parameters ${\theta }_{\text{ltp}}$ and ${\beta }_{\text{ltp}}$ define the midpoint and slope of the sigmoid curve specific to LTP. The parameter values used in these equations are provided in Table 1.

Table 1

Parameter	Description	Value
${\eta }_{\mathrm{l}\mathrm{t}\mathrm{p}}$	Learning rate for [Ca]NMDA-dependent plasticity	1.5×10−5 µS mM−1 ms−1
${\theta }_{\mathrm{l}\mathrm{t}\mathrm{p}}$	Midpoint of LTP kernel (initial value)	0.02 mM
${\beta }_{\mathrm{l}\mathrm{t}\mathrm{p}}$	Controls width of LTP kernel	1.0×103 mM−1
${\eta }_{\mathrm{l}\mathrm{t}\mathrm{d}}$	Learning rate for [Ca]L-type-dependent plasticity	3×10–3 ms−1 mM−1
${\theta }_{\mathrm{l}\mathrm{t}\mathrm{d}}$	Constant LTD threshold	7×10–5 mM
${\beta }_{\mathrm{l}\mathrm{t}\mathrm{d}}$	Controls steepness of LTD threshold	1.0×10 5 mM−1
${\eta }_{\mathrm{s},\mathrm{l}\mathrm{t}\mathrm{p}}$	Rate for shifting 𝜃ltp during dopamine peaks	1×10–7 mM2 ms–1
${\eta }_{\mathrm{s},\mathrm{l}\mathrm{t}\mathrm{d}}$	Rate for shifting 𝜃ltp during dopamine pauses	4×10–7 mM2 ms−1
${\beta }_{\mathrm{m}\mathrm{p}}$	Controls width of metaplasticity kernel	334 mM–1

This manuscript is a computational study. No new experimental data were generated. All code used for simulations, analyses, and figure generation (including figure supplements) is publicly available at https://github.com/zahradd/dSPN-learning-rule (copy archived at Khodadadi, 2025). This repository includes the simulations underlying the figures in the manuscript, along with a README file providing detailed instructions for environment setup and result reproduction. Additionally, model components adapted from Lindroos and Hellgren Kotaleski, 2021 and Trpevski et al., 2023, are available on ModelDB (accession numbers: 266775 and 2017143).

1. 1. Abraham WC
   2. Bear MF

   (1996) Metaplasticity: the plasticity of synaptic plasticity

   Trends in Neurosciences 19:126–130.

   https://doi.org/10.1016/s0166-2236(96)80018-x

   • PubMed
   • Google Scholar
2. 1. Abraham WC

   (2008) Metaplasticity: tuning synapses and networks for plasticity

   Nature Reviews. Neuroscience 9:387.

   https://doi.org/10.1038/nrn2356

   • PubMed
   • Google Scholar
3. 1. Antic SD
   2. Zhou WL
   3. Moore AR
   4. Short SM
   5. Ikonomu KD

   (2010) The decade of the dendritic NMDA spike

   Journal of Neuroscience Research 88:2991–3001.

   https://doi.org/10.1002/jnr.22444

   • PubMed
   • Google Scholar
4. 1. Benna MK
   2. Fusi S

   (2016) Computational principles of synaptic memory consolidation

   Nature Neuroscience 19:1697–1706.

   https://doi.org/10.1038/nn.4401

   • PubMed
   • Google Scholar
5. 1. Bicknell BA
   2. Häusser M

   (2021) A synaptic learning rule for exploiting nonlinear dendritic computation

   Neuron 109:4001–4017.

   https://doi.org/10.1016/j.neuron.2021.09.044

   • PubMed
   • Google Scholar
6. 1. Bienenstock EL
   2. Cooper LN
   3. Munro PW

   (1982) Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex

   The Journal of Neuroscience 2:32–48.

   https://doi.org/10.1523/JNEUROSCI.02-01-00032.1982

   • PubMed
   • Google Scholar
7. 1. Branco T
   2. Clark BA
   3. Häusser M

   (2010) Dendritic discrimination of temporal input sequences in cortical neurons

   Science 329:1671–1675.

   https://doi.org/10.1126/science.1189664

   • PubMed
   • Google Scholar
8. 1. Branco T
   2. Häusser M

   (2010) The single dendritic branch as a fundamental functional unit in the nervous system

   Current Opinion in Neurobiology 20:494–502.

   https://doi.org/10.1016/j.conb.2010.07.009

   • PubMed
   • Google Scholar
9. 1. Bruce NJ
   2. Narzi D
   3. Trpevski D
   4. van Keulen SC
   5. Nair AG
   6. Röthlisberger U
   7. Wade RC
   8. Carloni P
   9. Hellgren Kotaleski J

   (2019) Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals

   PLOS Computational Biology 15:e1007382.

   https://doi.org/10.1371/journal.pcbi.1007382

   • PubMed
   • Google Scholar
10. Book

    1. Carnevale NT
    2. Hines ML

    (2006) The NEURON Book

    Cambridge University Press.

    https://doi.org/10.1017/CBO9780511541612

    • Google Scholar
11. 1. Carter AG
    2. Sabatini BL

    (2004) State-dependent calcium signaling in dendritic spines of striatal medium spiny neurons

    Neuron 44:483–493.

    https://doi.org/10.1016/j.neuron.2004.10.013

    • PubMed
    • Google Scholar
12. 1. Chapman CA
    2. Nuwer JL
    3. Jacob TC

    (2022) The Yin and Yang of GABAergic and glutamatergic synaptic plasticity: opposites in balance by crosstalking mechanisms

    Frontiers in Synaptic Neuroscience 14:911020.

    https://doi.org/10.3389/fnsyn.2022.911020

    • PubMed
    • Google Scholar
13. 1. Chater TE
    2. Eggl MF
    3. Goda Y
    4. Tchumatchenko T

    (2024) Competitive processes shape multi-synapse plasticity along dendritic segments

    Nature Communications 15:7572.

    https://doi.org/10.1038/s41467-024-51919-0

    • PubMed
    • Google Scholar
14. 1. Chen X
    2. Leischner U
    3. Rochefort NL
    4. Nelken I
    5. Konnerth A

    (2011) Functional mapping of single spines in cortical neurons in vivo

    Nature 475:501–505.

    https://doi.org/10.1038/nature10193

    • PubMed
    • Google Scholar
15. 1. Chen SX
    2. Kim AN
    3. Peters AJ
    4. Komiyama T

    (2015) Subtype-specific plasticity of inhibitory circuits in motor cortex during motor learning

    Nature Neuroscience 18:1109–1115.

    https://doi.org/10.1038/nn.4049

    • PubMed
    • Google Scholar
16. 1. Cichon J
    2. Gan WB

    (2015) Branch-specific dendritic Ca(2+) spikes cause persistent synaptic plasticity

    Nature 520:180–185.

    https://doi.org/10.1038/nature14251

    • PubMed
    • Google Scholar
17. 1. Clopath C
    2. Büsing L
    3. Vasilaki E
    4. Gerstner W

    (2010) Connectivity reflects coding: a model of voltage-based STDP with homeostasis

    Nature Neuroscience 13:344–352.

    https://doi.org/10.1038/nn.2479

    • PubMed
    • Google Scholar
18. 1. Day M
    2. Belal M
    3. Surmeier WC
    4. Melendez A
    5. Wokosin D
    6. Tkatch T
    7. Clarke VRJ
    8. Surmeier DJ

    (2024) GABAergic regulation of striatal spiny projection neurons depends upon their activity state

    PLOS Biology 22:e3002483.

    https://doi.org/10.1371/journal.pbio.3002483

    • PubMed
    • Google Scholar
19. 1. Destexhe A
    2. Mainen ZF
    3. Sejnowski TJ

    (1994) An Efficient method for computing synaptic conductances based on a kinetic model of receptor binding

    Neural Computation 6:14–18.

    https://doi.org/10.1162/neco.1994.6.1.14

    • Google Scholar
20. 1. Dorman DB
    2. Jędrzejewska-Szmek J
    3. Blackwell KT

    (2018) Inhibition enhances spatially-specific calcium encoding of synaptic input patterns in a biologically constrained model

    eLife 7:e38588.

    https://doi.org/10.7554/eLife.38588

    • PubMed
    • Google Scholar
21. 1. Doron M
    2. Chindemi G
    3. Muller E
    4. Markram H
    5. Segev I

    (2017) Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons

    Cell Reports 21:1550–1561.

    https://doi.org/10.1016/j.celrep.2017.10.035

    • PubMed
    • Google Scholar
22. 1. Du K
    2. Wu YW
    3. Lindroos R
    4. Liu Y
    5. Rózsa B
    6. Katona G
    7. Ding JB
    8. Kotaleski JH

    (2017) Cell-type-specific inhibition of the dendritic plateau potential in striatal spiny projection neurons

    PNAS 114:E7612–E7621.

    https://doi.org/10.1073/pnas.1704893114

    • PubMed
    • Google Scholar
23. 1. Fino E
    2. Paille V
    3. Cui Y
    4. Morera-Herreras T
    5. Deniau JM
    6. Venance L

    (2010) Distinct coincidence detectors govern the corticostriatal spike timing-dependent plasticity

    The Journal of Physiology 588:3045–3062.

    https://doi.org/10.1113/jphysiol.2010.188466

    • PubMed
    • Google Scholar
24. 1. Fisher SD
    2. Robertson PB
    3. Black MJ
    4. Redgrave P
    5. Sagar MA
    6. Abraham WC
    7. Reynolds JNJ

    (2017) Reinforcement determines the timing dependence of corticostriatal synaptic plasticity in vivo

    Nature Communications 8:334.

    https://doi.org/10.1038/s41467-017-00394-x

    • PubMed
    • Google Scholar
25. 1. Frey U
    2. Schollmeier K
    3. Reymann KG
    4. Seidenbecher T

    (1995) Asymptotic hippocampal long-term potentiation in rats does not preclude additional potentiation at later phases

    Neuroscience 67:799–807.

    https://doi.org/10.1016/0306-4522(95)00117-2

    • PubMed
    • Google Scholar
26. 1. Fusi S
    2. Drew PJ
    3. Abbott LF

    (2005) Cascade models of synaptically stored memories

    Neuron 45:599–611.

    https://doi.org/10.1016/j.neuron.2005.02.001

    • PubMed
    • Google Scholar
27. 1. Gandolfi D
    2. Bigiani A
    3. Porro CA
    4. Mapelli J

    (2020) Inhibitory plasticity: from molecules to computation and beyond

    International Journal of Molecular Sciences 21:1805.

    https://doi.org/10.3390/ijms21051805

    • PubMed
    • Google Scholar
28. 1. Gao PP
    2. Graham JW
    3. Zhou WL
    4. Jang J
    5. Angulo S
    6. Dura-Bernal S
    7. Hines M
    8. Lytton WW
    9. Antic SD

    (2021) Local glutamate-mediated dendritic plateau potentials change the state of the cortical pyramidal neuron

    Journal of Neurophysiology 125:23–42.

    https://doi.org/10.1152/jn.00734.2019

    • PubMed
    • Google Scholar
29. 1. Gidon A
    2. Zolnik TA
    3. Fidzinski P
    4. Bolduan F
    5. Papoutsi A
    6. Poirazi P
    7. Holtkamp M
    8. Vida I
    9. Larkum ME

    (2020) Dendritic action potentials and computation in human layer 2/3 cortical neurons

    Science 367:83–87.

    https://doi.org/10.1126/science.aax6239

    • PubMed
    • Google Scholar
30. 1. Grienberger C
    2. Milstein AD
    3. Bittner KC
    4. Romani S
    5. Magee JC

    (2017) Inhibitory suppression of heterogeneously tuned excitation enhances spatial coding in CA1 place cells

    Nature Neuroscience 20:417–426.

    https://doi.org/10.1038/nn.4486

    • PubMed
    • Google Scholar
31. 1. Henneberger C
    2. Bard L
    3. Panatier A
    4. Reynolds JP
    5. Kopach O
    6. Medvedev NI
    7. Minge D
    8. Herde MK
    9. Anders S
    10. Kraev I
    11. Heller JP
    12. Rama S
    13. Zheng K
    14. Jensen TP
    15. Sanchez-Romero I
    16. Jackson CJ
    17. Janovjak H
    18. Ottersen OP
    19. Nagelhus EA
    20. Oliet SHR
    21. Stewart MG
    22. Nägerl UV
    23. Rusakov DA

    (2020) LTP induction boosts glutamate spillover by driving withdrawal of Perisynaptic Astroglia

    Neuron 108:919–936.

    https://doi.org/10.1016/j.neuron.2020.08.030

    • PubMed
    • Google Scholar
32. 1. Higley MJ
    2. Sabatini BL

    (2010) Competitive regulation of synaptic Ca2+ influx by D2 dopamine and A2A adenosine receptors

    Nature Neuroscience 13:958–966.

    https://doi.org/10.1038/nn.2592

    • PubMed
    • Google Scholar
33. 1. Hjorth JJJ
    2. Kozlov A
    3. Carannante I
    4. Frost Nylén J
    5. Lindroos R
    6. Johansson Y
    7. Tokarska A
    8. Dorst MC
    9. Suryanarayana SM
    10. Silberberg G
    11. Hellgren Kotaleski J
    12. Grillner S

    (2020) The microcircuits of striatum in silico

    PNAS 117:9554–9565.

    https://doi.org/10.1073/pnas.2000671117

    • PubMed
    • Google Scholar
34. 1. Huang YY
    2. Colino A
    3. Selig DK
    4. Malenka RC

    (1992) The influence of prior synaptic activity on the induction of long-term potentiation

    Science 255:730–733.

    https://doi.org/10.1126/science.1346729

    • PubMed
    • Google Scholar
35. 1. Hulme SR
    2. Connelly WM

    (2014) L-type calcium channel-dependent inhibitory plasticity in the thalamus

    Journal of Neurophysiology 112:2037–2039.

    https://doi.org/10.1152/jn.00918.2013

    • PubMed
    • Google Scholar
36. 1. Hwang FJ
    2. Roth RH
    3. Wu YW
    4. Sun Y
    5. Kwon DK
    6. Liu Y
    7. Ding JB

    (2022) Motor learning selectively strengthens cortical and striatal synapses of motor engram neurons

    Neuron 110:2790–2801.

    https://doi.org/10.1016/j.neuron.2022.06.006

    • PubMed
    • Google Scholar
37. 1. Iacaruso MF
    2. Gasler IT
    3. Hofer SB

    (2017) Synaptic organization of visual space in primary visual cortex

    Nature 547:449–452.

    https://doi.org/10.1038/nature23019

    • PubMed
    • Google Scholar
38. 1. Jia H
    2. Rochefort NL
    3. Chen X
    4. Konnerth A

    (2010) Dendritic organization of sensory input to cortical neurons in vivo

    Nature 464:1307–1312.

    https://doi.org/10.1038/nature08947

    • PubMed
    • Google Scholar
39. 1. Johansson Y
    2. Silberberg G

    (2020) The functional organization of cortical and thalamic inputs onto five types of striatal neurons is determined by source and target cell identities

    Cell Reports 30:1178–1194.

    https://doi.org/10.1016/j.celrep.2019.12.095

    • PubMed
    • Google Scholar
40. 1. Ju N
    2. Li Y
    3. Liu F
    4. Jiang H
    5. Macknik SL
    6. Martinez-Conde S
    7. Tang S

    (2020) Spatiotemporal functional organization of excitatory synaptic inputs onto macaque V1 neurons

    Nature Communications 11:697.

    https://doi.org/10.1038/s41467-020-14501-y

    • PubMed
    • Google Scholar
41. 1. Kahneman D
    2. Tversky A

    (1979) Prospect theory: an analysis of decision under risk

    Econometrica 47:1914185.

    https://doi.org/10.2307/1914185

    • Google Scholar
42. 1. Kawai R
    2. Markman T
    3. Poddar R
    4. Ko R
    5. Fantana AL
    6. Dhawale AK
    7. Kampff AR
    8. Ölveczky BP

    (2015) Motor cortex is required for learning but not for executing a motor skill

    Neuron 86:800–812.

    https://doi.org/10.1016/j.neuron.2015.03.024

    • PubMed
    • Google Scholar
43. 1. Kerlin A
    2. Mohar B
    3. Flickinger D
    4. MacLennan BJ
    5. Dean MB
    6. Davis C
    7. Spruston N
    8. Svoboda K

    (2019) Functional clustering of dendritic activity during decision-making

    eLife 8:e46966.

    https://doi.org/10.7554/eLife.46966

    • PubMed
    • Google Scholar
44. Software

    1. Khodadadi Z

    (2025) DSPN-learning-rule, version swh:1:rev:ff1f34ee3bbdbb806a712458e863df0aa0d651b0

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:a139bcb7e590035f7fe48534dd7de7982476062c;origin=https://github.com/zahradd/dSPN-learning-rule;visit=swh:1:snp:2f13da70f205d0bb072a3b03e26e315a4037ee83;anchor=swh:1:rev:ff1f34ee3bbdbb806a712458e863df0aa0d651b0
45. 1. Kleindienst T
    2. Winnubst J
    3. Roth-Alpermann C
    4. Bonhoeffer T
    5. Lohmann C

    (2011) Activity-dependent clustering of functional synaptic inputs on developing hippocampal dendrites

    Neuron 72:1012–1024.

    https://doi.org/10.1016/j.neuron.2011.10.015

    • PubMed
    • Google Scholar
46. 1. Kurotani T
    2. Yamada K
    3. Yoshimura Y
    4. Crair MC
    5. Komatsu Y

    (2008) State-dependent bidirectional modification of somatic inhibition in neocortical pyramidal cells

    Neuron 57:905–916.

    https://doi.org/10.1016/j.neuron.2008.01.030

    • PubMed
    • Google Scholar
47. 1. Larkum ME
    2. Nevian T
    3. Sandler M
    4. Polsky A
    5. Schiller J

    (2009) Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle

    Science 325:756–760.

    https://doi.org/10.1126/science.1171958

    • PubMed
    • Google Scholar
48. 1. Lavzin M
    2. Rapoport S
    3. Polsky A
    4. Garion L
    5. Schiller J

    (2012) Nonlinear dendritic processing determines angular tuning of barrel cortex neurons in vivo

    Nature 490:397–401.

    https://doi.org/10.1038/nature11451

    • PubMed
    • Google Scholar
49. 1. Legenstein R
    2. Maass W

    (2011) Branch-specific plasticity enables self-organization of nonlinear computation in single neurons

    The Journal of Neuroscience 31:10787–10802.

    https://doi.org/10.1523/JNEUROSCI.5684-10.2011

    • PubMed
    • Google Scholar
50. 1. Lindroos R
    2. Dorst MC
    3. Du K
    4. Filipović M
    5. Keller D
    6. Ketzef M
    7. Kozlov AK
    8. Kumar A
    9. Lindahl M
    10. Nair AG
    11. Pérez-Fernández J
    12. Grillner S
    13. Silberberg G
    14. Hellgren Kotaleski J

    (2018) Basal Ganglia neuromodulation over multiple temporal and structural scales-simulations of direct pathway MSNs investigate the fast onset of dopaminergic effects and predict the role of Kv4.2

    Frontiers in Neural Circuits 12:3.

    https://doi.org/10.3389/fncir.2018.00003

    • PubMed
    • Google Scholar
51. 1. Lindroos R
    2. Hellgren Kotaleski J

    (2021) Predicting complex spikes in striatal projection neurons of the direct pathway following neuromodulation by acetylcholine and dopamine

    The European Journal of Neuroscience 53:2117–2134.

    https://doi.org/10.1111/ejn.14891

    • PubMed
    • Google Scholar
52. 1. Lisman JE

    (2001) Three Ca2+ levels affect plasticity differently: the LTP zone, the LTD zone and no man’s land

    The Journal of Physiology 532:285.

    https://doi.org/10.1111/j.1469-7793.2001.0285f.x

    • PubMed
    • Google Scholar
53. 1. Losonczy A
    2. Magee JC

    (2006) Integrative properties of radial oblique dendrites in hippocampal CA1 pyramidal neurons

    Neuron 50:291–307.

    https://doi.org/10.1016/j.neuron.2006.03.016

    • PubMed
    • Google Scholar
54. 1. Lovett-Barron M
    2. Turi GF
    3. Kaifosh P
    4. Lee PH
    5. Bolze F
    6. Sun XH
    7. Nicoud JF
    8. Zemelman BV
    9. Sternson SM
    10. Losonczy A

    (2012) Regulation of neuronal input transformations by tunable dendritic inhibition

    Nature Neuroscience 15:423–430.

    https://doi.org/10.1038/nn.3024

    • PubMed
    • Google Scholar
55. 1. Major G
    2. Polsky A
    3. Denk W
    4. Schiller J
    5. Tank DW

    (2008) Spatiotemporally graded NMDA spike/plateau potentials in basal dendrites of neocortical pyramidal neurons

    Journal of Neurophysiology 99:2584–2601.

    https://doi.org/10.1152/jn.00011.2008

    • PubMed
    • Google Scholar
56. 1. Matsuda W
    2. Furuta T
    3. Nakamura KC
    4. Hioki H
    5. Fujiyama F
    6. Arai R
    7. Kaneko T

    (2009) Single nigrostriatal dopaminergic neurons form widely spread and highly dense axonal arborizations in the neostriatum

    The Journal of Neuroscience 29:444–453.

    https://doi.org/10.1523/JNEUROSCI.4029-08.2009

    • PubMed
    • Google Scholar
57. 1. McCulloch WS
    2. Pitts W

    (1943) A logical calculus of the ideas immanent in nervous activity

    The Bulletin of Mathematical Biophysics 5:115–133.

    https://doi.org/10.1007/BF02478259

    • Google Scholar
58. 1. Milstein AD
    2. Li Y
    3. Bittner SR
    4. Grinvald A
    5. Spruston N

    (2015) Inhibitory gating of input comparison in the caudate nucleus

    Nature Neuroscience 18:1591–1600.

    https://doi.org/10.1016/j.neuron.2015.08.025

    • PubMed
    • Google Scholar
59. Preprint

    1. Moldwin T
    2. Azran LS
    3. Segev I

    (2024) A generalized framework for the calcium control hypothesis describes weight-dependent synaptic plasticity

    bioRxiv.

    https://doi.org/10.1101/2023.07.13.548837

    • Google Scholar
60. 1. Moody TD
    2. Carlisle HJ
    3. O’Dell TJ

    (1999) A nitric oxide-independent and β-adrenergic receptor-sensitive form of metaplasticity limits θ-frequency stimulation-induced LTP in the hippocampal CA1 region

    Learning & Memory 6:619–633.

    https://doi.org/10.1101/lm.6.6.619

    • Google Scholar
61. 1. Nair AG
    2. Gutierrez-Arenas O
    3. Eriksson O
    4. Vincent P
    5. Hellgren Kotaleski J

    (2015) Sensing positive versus negative reward signals through Adenylyl Cyclase-Coupled GPCRs in direct and indirect pathway striatal medium spiny neurons

    The Journal of Neuroscience 35:14017–14030.

    https://doi.org/10.1523/JNEUROSCI.0730-15.2015

    • PubMed
    • Google Scholar
62. 1. Niculescu D
    2. Michaelsen-Preusse K
    3. Güner Ü
    4. van Dorland R
    5. Wierenga CJ
    6. Lohmann C

    (2018) A BDNF-mediated push-pull plasticity mechanism for synaptic clustering

    Cell Reports 24:2063–2074.

    https://doi.org/10.1016/j.celrep.2018.07.073

    • PubMed
    • Google Scholar
63. 1. Nishiyama J
    2. Yasuda R

    (2015) Biochemical computation for spine structural plasticity

    Neuron 87:63–75.

    https://doi.org/10.1016/j.neuron.2015.05.043

    • PubMed
    • Google Scholar
64. 1. Oikonomou KD
    2. Singh MB
    3. Sterjanaj EV
    4. Antic SD

    (2014) Spiny neurons of amygdala, striatum, and cortex use dendritic plateau potentials to detect network UP states

    Frontiers in Cellular Neuroscience 8:292.

    https://doi.org/10.3389/fncel.2014.00292

    • PubMed
    • Google Scholar
65. 1. Plotkin JL
    2. Day M
    3. Surmeier DJ

    (2011) Synaptically driven state transitions in distal dendrites of striatal spiny neurons

    Nature Neuroscience 14:881–888.

    https://doi.org/10.1038/nn.2848

    • PubMed
    • Google Scholar
66. 1. Plotkin JL
    2. Shen W
    3. Rafalovich I
    4. Sebel LE
    5. Day M
    6. Chan CS
    7. Surmeier DJ

    (2013) Regulation of dendritic calcium release in striatal spiny projection neurons

    Journal of Neurophysiology 110:2325–2336.

    https://doi.org/10.1152/jn.00422.2013

    • PubMed
    • Google Scholar
67. 1. Poirazi P
    2. Brannon T
    3. Mel BW

    (2003) Pyramidal neuron as two-layer neural network

    Neuron 37:989–999.

    https://doi.org/10.1016/s0896-6273(03)00149-1

    • PubMed
    • Google Scholar
68. 1. Polsky A
    2. Mel BW
    3. Schiller J

    (2004) Computational subunits in thin dendrites of pyramidal cells

    Nature Neuroscience 7:621–627.

    https://doi.org/10.1038/nn1253

    • PubMed
    • Google Scholar
69. 1. Ravasenga T
    2. Ruben M
    3. Regio V
    4. Polenghi A
    5. Petrini EM
    6. Barberis A

    (2022) Spatial regulation of coordinated excitatory and inhibitory synaptic plasticity at dendritic synapses

    Cell Reports 38:110347.

    https://doi.org/10.1016/j.celrep.2022.110347

    • PubMed
    • Google Scholar
70. 1. Reig R
    2. Silberberg G

    (2014) Multisensory integration in the mouse striatum

    Neuron 83:1200–1212.

    https://doi.org/10.1016/j.neuron.2014.07.033

    • PubMed
    • Google Scholar
71. 1. Reynolds JNJ
    2. Avvisati R
    3. Dodson PD
    4. Fisher SD
    5. Oswald MJ
    6. Wickens JR
    7. Zhang YF

    (2022) Coincidence of cholinergic pauses, dopaminergic activation and depolarisation of spiny projection neurons drives synaptic plasticity in the striatum

    Nature Communications 13:1296.

    https://doi.org/10.1038/s41467-022-28950-0

    • PubMed
    • Google Scholar
72. 1. Roskies AL

    (1999) The binding problem

    Neuron 24:7–9.

    https://doi.org/10.1016/s0896-6273(00)80817-x

    • PubMed
    • Google Scholar
73. 1. Sanabria BD
    2. Baskar SS
    3. Yonk AJ
    4. Linares-Garcia I
    5. Abraira VE
    6. Lee CR
    7. Margolis DJ

    (2024) Cell-type specific connectivity of whisker-related sensory and motor cortical input to dorsal striatum

    eNeuro 11:ENEURO.0503-23.2023.

    https://doi.org/10.1523/ENEURO.0503-23.2023

    • PubMed
    • Google Scholar
74. 1. Santos FJ
    2. Oliveira RF
    3. Jin X
    4. Costa RM

    (2015) Corticostriatal dynamics encode the refinement of specific behavioral variability during skill learning

    eLife 4:e09423.

    https://doi.org/10.7554/eLife.09423

    • PubMed
    • Google Scholar
75. 1. Schiess M
    2. Urbanczik R
    3. Senn W

    (2016) Somato-dendritic synaptic plasticity and error-backpropagation in active dendrites

    PLOS Computational Biology 12:e1004638.

    https://doi.org/10.1371/journal.pcbi.1004638

    • PubMed
    • Google Scholar
76. 1. Schiller J
    2. Major G
    3. Koester HJ
    4. Schiller Y

    (2000) NMDA spikes in basal dendrites of cortical pyramidal neurons

    Nature 404:285–289.

    https://doi.org/10.1038/35005094

    • PubMed
    • Google Scholar
77. 1. Scholl B
    2. Wilson DE
    3. Fitzpatrick D

    (2017) Local order within global disorder: synaptic architecture of visual space

    Neuron 96:1127–1138.

    https://doi.org/10.1016/j.neuron.2017.10.017

    • PubMed
    • Google Scholar
78. 1. Schultz W
    2. Dayan P
    3. Montague PR

    (1997) A neural substrate of prediction and reward

    Science 275:1593–1599.

    https://doi.org/10.1126/science.275.5306.1593

    • PubMed
    • Google Scholar
79. 1. Schultz W

    (2007) Multiple dopamine functions at different time courses

    Annual Review of Neuroscience 30:259–288.

    https://doi.org/10.1146/annurev.neuro.28.061604.135722

    • PubMed
    • Google Scholar
80. 1. Shen W
    2. Flajolet M
    3. Greengard P
    4. Surmeier DJ

    (2008) Dichotomous dopaminergic control of striatal synaptic plasticity

    Science 321:848–851.

    https://doi.org/10.1126/science.1160575

    • PubMed
    • Google Scholar
81. 1. Shindou T
    2. Ochi-Shindou M
    3. Wickens JR

    (2011) A Ca(2+) threshold for induction of spike-timing-dependent depression in the mouse striatum

    The Journal of Neuroscience 31:13015–13022.

    https://doi.org/10.1523/JNEUROSCI.3206-11.2011

    • PubMed
    • Google Scholar
82. 1. Shindou T
    2. Shindou M
    3. Watanabe S
    4. Wickens J

    (2019) A silent eligibility trace enables dopamine-dependent synaptic plasticity for reinforcement learning in the mouse striatum

    The European Journal of Neuroscience 49:726–736.

    https://doi.org/10.1111/ejn.13921

    • PubMed
    • Google Scholar
83. 1. Silver D
    2. Hubert T
    3. Schrittwieser J
    4. Antonoglou I
    5. Lai M
    6. Guez A
    7. Lanctot M
    8. Sifre L
    9. Kumaran D
    10. Graepel T
    11. Lillicrap T
    12. Simonyan K
    13. Hassabis D

    (2018) A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

    Science 362:1140–1144.

    https://doi.org/10.1126/science.aar6404

    • PubMed
    • Google Scholar
84. 1. Stauffer WR
    2. Lak A
    3. Kobayashi S
    4. Schultz W

    (2016) Components and characteristics of the dopamine reward utility signal

    The Journal of Comparative Neurology 524:1699–1711.

    https://doi.org/10.1002/cne.23880

    • PubMed
    • Google Scholar
85. 1. Surmeier DJ
    2. Shen W
    3. Day M
    4. Gertler T
    5. Chan S
    6. Tian X
    7. Plotkin JL

    (2010) The role of dopamine in modulating the structure and function of striatal circuits

    Progress in Brain Research 183:149–167.

    https://doi.org/10.1016/S0079-6123(10)83008-0

    • PubMed
    • Google Scholar
86. 1. Takahashi N
    2. Kitamura K
    3. Matsuo N
    4. Mayford M
    5. Kano M
    6. Matsuki N
    7. Ikegaya Y

    (2012) Locally synchronized synaptic inputs

    Science 335:353–356.

    https://doi.org/10.1126/science.1210362

    • PubMed
    • Google Scholar
87. 1. Tran-Van-Minh A
    2. Cazé RD
    3. Abrahamsson T
    4. Cathala L
    5. Gutkin BS
    6. DiGregorio DA

    (2015) Contribution of sublinear and supralinear dendritic integration to neuronal computations

    Frontiers in Cellular Neuroscience 9:67.

    https://doi.org/10.3389/fncel.2015.00067

    • PubMed
    • Google Scholar
88. 1. Trpevski D
    2. Khodadadi Z
    3. Carannante I
    4. Hellgren Kotaleski J

    (2023) Glutamate spillover drives robust all-or-none dendritic plateau potentials-an in silico investigation using models of striatal projection neurons

    Frontiers in Cellular Neuroscience 17:1196182.

    https://doi.org/10.3389/fncel.2023.1196182

    • PubMed
    • Google Scholar
89. 1. Udakis M
    2. Pedrosa V
    3. Chamberlain SEL
    4. Clopath C
    5. Mellor JR

    (2020) Interneuron-specific plasticity at parvalbumin and somatostatin inhibitory synapses onto CA1 pyramidal neurons shapes hippocampal output

    Nature Communications 11:4395.

    https://doi.org/10.1038/s41467-020-18074-8

    • PubMed
    • Google Scholar
90. 1. van den Heuvel MP
    2. Sporns O

    (2013) Network hubs in the human brain

    Trends in Cognitive Sciences 17:683–696.

    https://doi.org/10.1016/j.tics.2013.09.012

    • PubMed
    • Google Scholar
91. 1. Varga Z
    2. Jia H
    3. Sakmann B
    4. Konnerth A

    (2011) Dendritic coding of multiple sensory inputs in single cortical neurons in vivo

    PNAS 108:15420–15425.

    https://doi.org/10.1073/pnas.1112355108

    • PubMed
    • Google Scholar
92. 1. von der Malsburg C

    (1999) The what and why of binding: the modeler’s perspective

    Neuron 24:95–104.

    https://doi.org/10.1016/s0896-6273(00)80825-9

    • PubMed
    • Google Scholar
93. 1. Watt AJ
    2. Sjöström PJ
    3. Häusser M
    4. Nelson SB
    5. Turrigiano GG

    (2004) A proportional but slower NMDA potentiation follows AMPA potentiation in LTP

    Nature Neuroscience 7:518–524.

    https://doi.org/10.1038/nn1220

    • PubMed
    • Google Scholar
94. 1. Wilson CJ
    2. Groves PM
    3. Kitai ST
    4. Linder JC

    (1983) Three-dimensional structure of dendritic spines in the rat neostriatum

    The Journal of Neuroscience 3:383–388.

    https://doi.org/10.1523/JNEUROSCI.03-02-00383.1983

    • PubMed
    • Google Scholar
95. 1. Wilson DE
    2. Whitney DE
    3. Scholl B
    4. Fitzpatrick D

    (2016) Orientation selectivity and the functional clustering of synaptic inputs in primary visual cortex

    Nature Neuroscience 19:1003–1009.

    https://doi.org/10.1038/nn.4323

    • PubMed
    • Google Scholar
96. 1. Winnubst J
    2. Cheyne JE
    3. Niculescu D
    4. Lohmann C

    (2015) Spontaneous activity drives local synaptic plasticity in vivo

    Neuron 87:399–410.

    https://doi.org/10.1016/j.neuron.2015.06.029

    • PubMed
    • Google Scholar
97. 1. Wolf JA
    2. Moyer JT
    3. Lazarewicz MT
    4. Contreras D
    5. Benoit-Marand M
    6. O’Donnell P
    7. Finkel LH

    (2005) NMDA/AMPA ratio impacts state transitions and entrainment to oscillations in a computational model of the nucleus accumbens medium spiny projection neuron

    The Journal of Neuroscience 25:9080–9095.

    https://doi.org/10.1523/JNEUROSCI.2220-05.2005

    • PubMed
    • Google Scholar
98. 1. Xu N
    2. Harnett MT
    3. Williams SR
    4. Huber D
    5. O’Connor DH
    6. Svoboda K
    7. Magee JC

    (2012) Nonlinear dendritic integration of sensory and motor input during an active sensing task

    Nature 492:247–251.

    https://doi.org/10.1038/nature11601

    • PubMed
    • Google Scholar
99. 1. Yagishita S
    2. Hayashi-Takagi A
    3. Ellis-Davies GCR
    4. Urakubo H
    5. Ishii S
    6. Kasai H

    (2014) A critical time window for dopamine actions on the structural plasticity of dendritic spines

    Science 345:1616–1620.

    https://doi.org/10.1126/science.1255514

    • PubMed
    • Google Scholar
100. 1. Zenke F
     2. Gerstner W

     (2017) Hebbian plasticity requires compensatory processes on multiple timescales

     Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 372:20160259.

     https://doi.org/10.1098/rstb.2016.0259

     • PubMed
     • Google Scholar
101. 1. Zenke F
     2. Gerstner W
     3. Ganguli S

     (2017) The temporal paradox of Hebbian learning and homeostatic plasticity

     Current Opinion in Neurobiology 43:166–176.

     https://doi.org/10.1016/j.conb.2017.03.015

     • PubMed
     • Google Scholar

Author details

1. Zahra Khodadadi

   Science for Life Laboratory, Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   zahra.khodadadi@scilifelab.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6124-949X

2. Daniel Trpevski

   Science for Life Laboratory, Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing, Conceptualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9068-6744

3. Robert Lindroos

   Science for Life Laboratory, Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Jeanette Hellgren Kotaleski

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9134-3601

4. Jeanette Hellgren Kotaleski

   1. Science for Life Laboratory, Department of Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
   2. Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   Contributed equally with

   Robert Lindroos

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0550-0739

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