A typical Go/No-Go decision is suggested to be implemented in the brain via the activation of the direct or indirect pathway in the basal ganglia. Medium spiny neurons (MSNs) in the striatum, receiving input from cortex and projecting to the direct and indirect pathways express D1 and D2 type dopamine receptors, respectively. Recently, it has become clear that the two types of MSNs markedly differ in their mutual and recurrent connectivities as well as feedforward inhibition from FSIs. Therefore, to understand striatal function in action selection, it is of key importance to identify the role of the distinct connectivities within and between the two types of MSNs on the balance of their activity. Here, we used both a reduced firing rate model and numerical simulations of a spiking network model of the striatum to analyze the dynamic balance of spiking activities in D1 and D2 MSNs. We show that the asymmetric connectivity of the two types of MSNs renders the striatum into a threshold device, indicating the state of cortical input rates and correlations by the relative activity rates of D1 and D2 MSNs. Next, we describe how this striatal threshold can be effectively modulated by the activity of fast spiking interneurons, by the dopamine level, and by the activity of the GPe via pallidostriatal backprojections. We show that multiple mechanisms exist in the basal ganglia for biasing striatal output in favour of either the `Go' or the `No-Go' pathway. This new understanding of striatal network dynamics provides novel insights into the putative role of the striatum in various behavioral deficits in patients with Parkinson' s disease, including increased reaction times, L-Dopa-induced dyskinesia, and deep brain stimulation-induced impulsivity.
The basal ganglia (BG) are a set of nuclei, located at the base of the forebrain, which play a crucial role in a variety of motor and cognitive functions. The striatum is the main input stage of the basal ganglia, receiving inputs from widely distributed areas in the cortex [1], and projecting to the BG output nuclei Globus Pallidus Interna (GPi) and Substantia Nigra (SNr) via the so-called direct and indirect pathways, respectively [2]. The integration of multi-modal sensory signals with motor and/or cognitive inputs in the striatum sets the stage for action selection. Therefore, to understand the computations performed by the basal ganglia, it is of key importance to characterize the dynamical properties of striatal network activity. Nearly 95% of the neurons in the striatum are inhibitory medium spiny neurons (MSNs). The remaining 5% are inhibitory interneurons, such as fast spiking interneurons (FSIs) and tonically active neurons (TANs) [3]. The MSNs are classified as D1 and D2 type neurons, depending on their dopamine receptor expression. Interestingly, D1 MSNs project to the GPi and SNr, forming the direct pathway, whereas D2 neurons project to Globus Pallidus Externa (GPe), forming the indirect pathway [2]. Consistent with basal ganglia anatomy and previous models [4–6], selective activation of D1 MSNs in the rat increases ambulation, whereas selective activation of D2 MSNs increases freezing behavior [7]. However, a complete shutdown of activity in either of the two neuron subpopulations might not occur in awake behaving animals [8]. In such a scenario, though, action selection could still be performed by a relative increase in the activity of one subpopulation compared to the other. Thus far, in computational models of the interactions between direct and indirect pathways [4–6,9], D1 and D2 MSNs have been considered as interchangeable inhibitory neuron subpopulations. In such single population models, the striatal output is controlled by the strength of cortico-striatal synaptic weights. The recurrent inhibition is not strong enough to support winner-take-all dynamics as earlier speculated [10], but may be sufficient to allow for a winner-less competition [11] and may enhance the saliency of the cortical input representation in the striatum [12]. This, however, is a highly simplistic view of the recurrent inhibition within the striatum and, as will be described below, is inconsistent with experimental data, especially given the recent findings on the recurrent connectivity in the striatum. Recent experiments have shown that D1 and D2 MSNs have quite different anatomical and electrophysiological properties [13]. Moreover, the striatal circuit also shows a highly specific connectivity in terms of the mutual inhibition between the MSN subpopulations [14,15] and the feedforward inhibition from FSIs [16]. Paired neuron recordings showed that D2 MSNs make more and stronger connections to D1 MSNs, than vice versa. Furthermore, FSIs preferentially innervate D1 MSNs as compared to D2 MSNs (Fig 1A). The computational role of this specific connectivity within the striatum is not clear and cannot be inferred from previous models, which assumed a single homogeneously connected MSN population in the striatum. Specifically, it is important to identify the effect of the distinct D1 and D2 connectivities on the competition between the direct and indirect pathways. Here we describe the effect of the heterogenous connectivity of D1 and D2 neurons on their mutual interactions using both a reduced firing rate model and numerical simulations of a spiking striatal network model. We show that the firing rates of both D1 and D2 MSNs change in a non-monotonic manner in response to cortical input rates and correlations. Interestingly, higher output rates in D1 than in D2 MSNs, and vice versa, were observed for separate, non-overlapping ranges of cortical input rate and correlation. Correlations in the input can further change the range of cortical inputs for which either D1 or D2 MSNs have the higher firing rate. Thus, we argue that the striatum acts as a threshold device for cortical input rates by changing the magnitude of the difference between firing rates of D1 and D2 MSNs, depending on the level of cortical input rates and correlations. While the main determinant of the striatal threshold is the asymmetric connectivity among the various striatal elements, the threshold is not fixed and can be dynamically adjusted by the dopamine level, by the connectivity and firing rate of fast spiking interneurons (FSI), and by the GPe activity. That is, changes in the striatal threshold could reflect changes in the operating point of the striatum, behavioral context, and learning and reward history. These novel insights concerning the interactions between direct and indirect pathways suggest putative mechanistic explanations for the role of striatum in cognitive deficits such as L-Dopa-induced Dyskinesia (LID), deep brain stimulation (DBS) -induced impulsivity and increased reaction times in Parkinson’s disease (PD) patients.
Experimental measurements of the mutual connectivity between striatal neurons show that D2 MSNs make more and stronger inhibitory connections on D1 MSNs than vice versa [14,15]. In Lhx6-GFP transgenic mice, FSIs preferentially target D1 MSNs as compared to D2 MSNs [16] (however cf. [15]). This leads to the following two inequalities: J12 > J21, J1F ≥ J2F, with J12 denoting the connection from D2 MSN to D1 MSN, J21 the obverse, J1F the connection from FSI to D1 MSN and J2F likewise to D2 MSN (cf. Table 1). These inequalities imply that if the two MSN subpopulations receive the same amount of excitatory input, D2 MSNs will always have a higher firing rate. We confirmed this by evaluating the fixed points of the linearized dynamics of the D1 and D2 MSNs in a mean field model (Eqs 25–27, Fig 1B—grey and black traces). In our spiking network simulations this corresponded to a lower mean firing rate of the D1 population compared to the D2 population. This bias might be considered functionally useful, because for similar input strengths (i. e. when the cortex does not impose any preference for either direct or indirect pathways), the indirect pathway will dominate the striatal network dynamics and the ‘No-Go’ would be the default state of the basal ganglia. Thus, in order for D1 MSN activity (λD1) to exceed D2 MSN activity (λD2), D1 MSNs must receive either stronger (JC1 > JC2), higher excitatory inputs (λctx_d1 > λctx_d2), or more excitatory synapses. Alternatively, D1 MSNs could be more excitable. Indeed, D1 MSNs have more primary dendrites and a larger dendritic arborization [13], thereby potentially receiving more inputs. Moreover, pyramidal tract neurons have stronger connections to D1 MSNs than to D2 MSNs, while intratelencephalic tract neurons innervate D1 and D2 MSNs equally. [1]. Despite this evidence, current data are not sufficient to determine the exact strength and rate of excitatory inputs to D1 and D2 MSNs. To estimate how much additional excitation would be required for D1 MSNs to have their firing rates exceed over those of D2 MSNs, we systematically varied the drive of cortical inputs to D1 and D2 MSNs and calculated the response firing rates of the two subpopulations, for the firing rate model (Fig 2). In the following we discuss two different scenarios for systematically varying the cortical excitatory drive to D1 and D2 MSNs. To obtain further insight into the activity balance of D1 and D2 MSNs, we considered a state of the striatum in which the cortico-striatal projections had been strengthened for a particular ‘Go’ task (JC1 > JC2, cf. the ‘multiplicative scenario’ for detail). This scenario is shown for the firing rate model in Fig 1B and for the spiking neural network in Fig 4). In this state, we estimated the difference between D1 and D2 MSN output rates as a function of cortical input rate. For a constant difference in synaptic strength to D1 and D2 MSNs (ΔJ = JC1 − JC2 = const), the difference between the output firing rates of D1 and D2 MSNs (ΔMSN) changes from positive to negative as the cortical input rate increases (Figs 1B and 4). That is, the response of the striatum to a range of cortical input rates can be divided into two regimes: a regime where D1 firing rate exceeds D2 firing rate and a regime where this situation is reversed. At the transition point λ C T X t r a n, the D1 and D2 firing rates are equal. This behavior can be interpreted as a striatal bias towards the ‘Go’ pathway below a certain cortical input rate (λ C T X < λ C T X t r a n), switching to a bias towards the ‘No-Go’ pathway for higher cortical input rates (λ C T X > λ C T X t r a n) (cf. Figs 4A and 4B and 1B). The cortical input rate (for a constant difference ΔCTX) at which ΔMSN changes its sign, switching the striatum from a putative ‘Go’ to ‘No-Go’ mode, can be referred to as decision transition threshold (DTT). That is, the striatum acts as a threshold device, indicating the change in the cortical input rates by the sign of the difference between the firing rates of D1 and D2 MSNs—higher D1 activity reflecting lower cortical input rate and higher D2 activity reflecting higher cortical input rate. Therefore, we propose that the asymmetric striatal connectivity allows it to ‘sense’ small changes in the cortical activity reaching the striatum and to modulate the balance between ‘Go’ and ‘No-Go’ states of the basal ganglia accordingly. Here we considered the scenario in which the sign of ΔMSN changes from positive to negative, i. e. going from ‘Go’ to ‘NoGo’ bias. It is possible to tune the strength of FSI inputs such that initially the striatum is biased towards the ‘NoGo’ pathway and a subsequent increase in the cortical input changes the bias to the ‘Go’ pathway (cf. Fig 3A and 3D). Previously, Lo and Wang [18] described the cortico-basal ganglia loop as a DTT for reaction time tasks. However, in their model the striatum was considered a passive unit, the threshold of which could be tuned by the strength of cortico-striatal synapses. In our model, for the first time, we describe the striatum as an active participant in deciding the level of DTT. It should however be noted, that our model explores the DTT under steady state conditions only. In this regime, the striatal network settles into stable fixed points (cf. Materials and Methods) and, therefore, the DTT is a stable network state as well. A DTT might also emerge as a transient state in the network dynamics in the presence of additional mechanisms like short term and/or long term plasticity of cortico-striatal and/or striato-striatal synapses and intrinsic plasticity of striatal neurons. Further exploration of such mechanisms is, however, beyond the scope of the present work. The presence of such dynamic DTT suggests several possible control mechanisms, that may set the bias towards either of the two pathways along the basal ganglia downstream nuclei, depending on the animal behavioral state, motivation and acquired learning. In the following sections, we describe three possible mechanisms that can modify the balance of D1 and D2 activity by modulating the level of the DTT, thereby rendering it as a dynamic variable. In a standard feedforward model of the basal ganglia, the GPi activity can indicate the higher activity of D1 (D2) MSNs by decreasing (increasing) its activity around the baseline. However, recent data shows that the basal ganglia network is more complicated than the simple feedforward model (cf. review by [19]). In future it would be important to show whether GPi can change its activity according to the D1 and D2 MSNs firing rate differences, in a more updated model of the basal ganglia. In the above, the DTT was demonstrated assuming that the firing rate of the cortical input and the difference between the input to the D1 and D2 MSNs (ΔCTX) remaines constant, but in more realistic conditions the input rate and ΔCTX could both be random variables. Therefore, we tested the viability of the concept of DTT when the mean firing rate of the cortical inputs to the D1 and D2 MSNs was chosen from a low-pass filtered noise. In addition, the extra input to the D1 MSNs was chosen from a low-pass filtered noise. To quantify the DTT we measured ΔMSN in pre-DTT and post-DTT regions (Supplementary S1 Fig). It is obvious that when ΔCTX is noisy the difference between the firing rate of the D1 and D2 MSNs around the DTT decreases, however ΔMSN in the pre-DTT and post-DTT remains large enough to be reliably measured (Supplementary S1 Fig). When the standard deviation is increased upto 50% of the mean value of the ΔCTX, both pre-DTT and post-DTT areas decreased only by 20% (Supplementary S1 Fig). Correlations in the spiking is another way of generating transient fluctuations in the input to the D1 and D2 MSNs and the effect of correlation induced fluctuations is described below (cf. subsection “Effect of cortical spiking activity correlations on the DTT”) In addition to the input rate fluctuations, the connectivity between the D1 and D2 MSNs could also be different from what we have previously used. Therefore, we tested the robustness of the DTT for variation in the chosen values of striatal network connectivity (e. g. J11, J12 etc.). There are up to eight network connectivity parameters (cortical input to the two MSNs populations, mutual and within MSNs connectivity and MSN and FSI connectivity) which could potentially affect the existence of the DTT. We systematically varied all the parameters (except the FSI connectivity to the MSNs) by ±100% around their mean values (as used throughout this manuscript). Visualisation of this high dimensional parameter space is difficult. Therefore, here we checked if the DTT exists when only one of the parameters is varied systematically by ±100% of its mean value, while other parameters can take any value between ±100% of their mean value. A DTT is said to exist if ΔMSN for increasing values of λctx and the given parameter combination changes sign from positive to negative values exactly once. This was detected algorithmically as well as by visual inspection. Indeed, the DTT is a robust to such changes in the network connectivity and for every parameter we tested here (cortical inputs to the MSNs and within and mutual connectivity of MSNs), we can find the DTT (cf. Supplementary S2A–S2F Fig). Only for very small values of the within connectivity of the D1 and D2 MSNs (J11 and J22), we could not find a solution for the DTT. In addition, we also checked the existence of the DTT for various pairs of the network connectivity parameters (e. g. J21 vs. J11). While there are some forbidden regions in which DTT does not exist, for a large variations in the striatal connectivity, the DTT can be observed (Supplementary S2 Fig). When the existence is visualised as a scatter diagram as a function of a pair of the network connectivity parameters, the mean of these clusters lie very close to the values we have used throughout this manuscript. Task related activity in the cortex is modulated both in firing rates and in spike correlations [20]. Here we investigated how spike correlations in the cortical inputs to the striatum may affect the balance between D1 and D2 MSNs. Injection of correlated inputs to a population of MSNs requires the separation of input correlations into two categories [12]: correlations arising due to convergence of cortical projections onto individual neurons, i. e correlations within the pool of pre-synaptic neurons seen by individual MSNs (in the following referred to as ‘within’-correlations—W) and correlations arising due to divergence of cortical projections, i. e. correlations between the pools as seen by different MSNs, the so-called shared input (in the following referred to as ‘between’ correlations—B) (cf. Materials and Methods and Fig 5A). Note that because both B and W correlations arise from convergent and divergent projections of the same pre-synaptic population, between correlations are always smaller than or equal to within correlations (B ≤ W [21,22], cf. Materials and Methods). To understand the effect of input correlations on the balance of D1 and D2 MSN activity, we independently varied both within and between input correlations while maintaining a constant input rate (MIP model in [23]). These simulations were only performed for the spiking neural network model since modelling correlations in a mean field model is non-trivial, especially when post-synaptic neurons are recurrently connected. (cf. Materials and Methods, Fig 5A). As expected, within-correlations affected the individual firing rates in a non-monotonic manner [23] for both D1 and D2 MSNs. For weak input correlations, as D1 MSNs form stronger synapses with cortical afferents, (JC1 > JC2; scenario II), D1 MSNs spike at higher rate than D2 MSNs (Fig 5B). For high values of W the MSNs operated in a so called ‘spike-wasting’ regime, because the average count of coincident spikes (or the input variance) exceeded the spike threshold [23] and therefore, W caused no difference in the output firing rates of D1 and D2 MSNs (Fig 5C). That is, the difference in the firing rates of D1 and D2 MSNs (ΔMSN) changed non-monotonically as a function of W, reaching its maximal values for W = Wopt ≠ 0 (Fig 6A and 6B). The effect of W on ΔMSN was further modulated by the between-correlations (B). For W < Wopt, increasing B resulted in a monotonic increase in ΔMSN. In this regime, B increased the correlation within D1 and D2 MSNs and, thereby, increased the effective inhibition of one population by the other, and vice versa. Because D1 MSNs received input with a slightly higher strength (JC1 > JC2; scenario II), B induced more synchrony among D1MSNs and, hence, they were better able to inhibit the D2 MSNs (Fig 5E). However, this held only over a smaller parameter regime, because by design B cannot exceed W (cf. Materials and Methods). For W ≥ Wopt, ΔMSN decreased monotonically with increasing B. In this regime, an increase in B enhanced the correlations within D1 and D2 MSNs, hence amounting to a ‘wasting of recurrent inhibition’ [12]. Increased correlations within D1 and D2 MSNs created strong but only short lasting inhibition leaving out longer lasting time windows without any recurrent inhibition (Fig 5D). In the absence of sufficiently long-lasting recurrent inhibition the difference ΔMSN monotonically decreased (Fig 6A and 6B). Previously, we have shown that shared inputs (B > 0) reduce the signal-to-noise ratio and the contrast enhancement in the striatum [12]. Our results here now show that the ‘between’-correlations could play an even more important role in initiating the action-selection process in the striatum. However, whether B correlations improve or impair action selection strongly depends on the ‘within’-correlations. (Fig 6). With an increase in the cortical input rate, the amount of inhibition experienced by a MSN in the striatal network also increases due to the increase in MSN firing rates. This requires more coincident excitatory inputs (i. e. higher W) to overcome the increased inhibition and to drive the output neurons above firing threshold. Thus, Wopt increases with an increase in input rates (compare Fig 6A and 6B). That is, an increase in cortical input rates broadens the dynamic range of the interaction between B and W by increasing Wopt (Fig 6C, also discussed in [23]). These results show that the striatum also acts as a threshold device for cortical input correlations, in the same way we have shown above for cortical input rates. To illustrate this, we plotted ΔMSN as a function of the cortical input rates and within correlations (W) for a fixed value of B (Fig 6C, cf. Supplementary S4 Fig). Here, we can define three regions in the space of input firing rates and input correlations for which ΔMSN is positive, negative and zero respectively (Fig 6C), reflecting how the striatum will react to the first and second order statistics of the cortical inputs by activating the direct, the indirect or none of the two pathways. While the direct and indirect pathways reflect the striatal preference for a ‘Go’ and ‘No-Go’ action, respectively, ΔMSN ≈ 0 could reflect a state of high conflict where it is useful to halt the decision making process until more information is available. MSNs express D1 and D2 types dopamine receptors forming the direct and indirect pathways of the BG, respectively. Dopamine affects striatal function by modulating the intrinsic excitability of the MSNs and synaptic weights [24] and synaptic plasticity [25,26] of the cortico-striatal projections. Because we are interested in how striatum processes cortical inputs, here we studied the effect of dopamine-induced modulation of the strength of cortico-striatal projections. Dopamine induces long term potentiation (LTP) in cortico-striatal synapses onto D1 MSNs and long term depression (LTD) in cortico-striatal synapses onto D2 MSNs [24]. This steady state effect of dopamine is often modelled by changing the weight of cortico-striatal synapses for D1 (JC1) and for D2 (JC2) [27]. Therefore, in our model we simulated the effect of dopamine depletion by decreasing JC1 and increasing JC2, whereas higher than normal dopamine was simulated by increasing JC1 and decreasing JC2. Thus, in a low dopamine state (e. g. in PD patients), D2 MSNs received much stronger cortical input and, therefore, exerted more inhibition on to the D1 MSNs. This shifted the DTT towards lower cortical input rates (Fig 7A) reducing the regime under which the direct pathway could dominate the indirect pathway. Consistent with experimental observations and previous models [4,5], our model suggests that dopamine depletion would (1) increase the firing rate of D2 MSNs, thereby (2) introducing a preference for ‘No-Go’ type actions. By contrast, in a high dopamine state (e. g. as in PD patients with L-Dopa treatment) D1 MSNs received much stronger cortical input and were able to maintain higher output firing rate than D2 MSNs for a wider range of cortical inputs. That is, high dopamine shifted the DTT towards higher cortical inputs (Fig 7A) and increased the regime under which the direct pathway could dominate the indirect pathway. Consistent with experimental observations, our model suggests that an increase in steady state dopamine levels would (1) increase the firing rate of D1 MSNs [28], thereby, (2) introducing a preference for ‘Go’ type actions or dyskinesia. In summary, therefore, synaptic effects of dopamine can be seen as a change in the DTT of the striatum. Approximately 1% of the striatal neuronal population is composed of parvalbumin expressing fast spiking interneurons (FSIs) [29]. Despite being only a small portion of the neuronal population in the striatum, they can strongly modulate MSNs activity due to their high degree of convergent and divergent connections to the MSNs. Moreover, they form synaptic connections near or at MSN’s somata as opposed to glutamergic synapses or local MSN axon collaterals, which usually connect to spines or dendritic shafts [30]. Furthermore, recent data suggests that FSIs preferentially inhibit direct pathway MSNs (D1) as compared to indirect pathway MSNs (D2) [16]. This implies that the activity of FSIs could alter the balance between the direct and indirect pathways and may even change the DTT. For instance, Fig 4A showed that for small ΔCTX, D1 MSNs fire at higher rate than D2 MSNs at low inputs, however an increase in FSI firing rate would inhibit D1 MSNs more than D2 MSNs and, hence, reduce the DTT. By contrast, a reduction in the firing rate of FSIs will have an opposite effect. Thus, FSIs could play a vital role in maintaining the default state of the striatal circuit as a ‘No-Go’ state, which could be changed to a ‘Go’ state by exciting D1 MSNs more than D2 MSNs. Alternatively, a depletion of FSIs (e. g. as seen in patients with Tourette syndrome), a reduction in their firing rate or connectivity should reverse this scenario and enforce a ‘Go’ state as the default striatum state leading to impulsivity-related behavioral symptoms. Besides cortical inputs, other factors such as back projections from the GPe could also modulate the effect of FSIs on MSNs by modulating their firing rates (Fig 7B). The effect of GPe on FSIs was modelled in our network model as a constant amount of inhibition on FSI firing rates. An increase in GPe activity (for instance due to an increase in STN activity) would effectively release D1 and D2 MSNs from the feedforward inhibition. However, the effect will be further amplified for D1 MSNs because these neurons receive more input from FSIs than D2 MSNs do. This can shift the bias from a ‘No-Go’ to ‘Go’ state for high cortical input rate (as shown in Fig 7B, dotted lines, for λCTX in the range (10–15 Hz) ). The same effect of FSI activity can be observed in the Fig 3D. The same effect is shown for mean field model in Supplementary S5 Fig. The temporal dynamics of such a modulation by GPe are shown in Supplementary S6 Fig for a mean field model. Thus, an increase in the activity of the FSIs would result in shifting the DTT to higher cortical rates. We argue that increased firing rates in STN might not only maintain a tight rein on GPi to prevent a premature response in a high conflict decision, as suggested by [5], but also assist in resolving the conflict in the striatum via pallido-striatal back projections. In the dopamine depleted condition when the DTT is very small, the effect of the GPe back projection might not be able to modulate the DTT any further (Fig 7C). Similarly, tonically active interneuron (TANs) could exploit the FSI to D1 and D2 MSNs connectivities by modulating the strength of FSI synapses onto the respective MSNs [31], thereby, changing the DTT indirectly. Our model is based on in vitro results from Taverna et al. [14] and Gittis et al. [16]. Taverna et al. [14] showed that D2 MSNs inhibit D1 MSNs more strongly in terms of numbers of projections and synaptic strengths as compared to vice versa, whereas Gittis et al. [16] showed that in the Lhx6-GFP transgenic mice, FSI has more projections to D1 than to D2, while the synaptic strengths of the projections to both MSN types are comparable. While it is unlikely that the preferential connectivity of FSIs to D1 MSNs is specific to the mouse strain (A. Gittis personal communication), another recent study by Planert et al. [15], while supporting the results of Taverna et al. [14], did not report any significant preference in the connectivity of FSIs to D1 MSNs. Thus, even though Planert et al. drew their conclusion based on only a small number of neurons, the apparent inconsistency of the data in the two studies requires a reevaluation of the connectivity of FSIs to MSNs. We, therefore, analysed the striatal dynamics for equal innervation of MSNs by FSIs.
Spike correlations in cortical input to the striatum influence the DTT in a complex fashion. To better understand the effect of input correlations we separately studied the role of correlations within and correlations between the pre-synaptic pools of the MSNs. Previously, we have shown that correlations between the pre-synaptic pools of MSNs (B) tend to reduce the signal-to-noise ratio of the striatal response to cortical inputs [12]. On the other hand correlations within the pre-synaptic pools of individual MSNs (W) affect the signal-to-noise ratio of the striatal response in a non-monotonic manner. Here, we found that W correlations also affect the difference between D1 and D2 MSNs activity (ΔMSN) in a non-monotonic fashion (Fig 6). Interestingly, when input firing rates result in equal output firing rates of D1 and D2 MSNs, a modulation in W could break the symmetry and increase the firing rate of either one of the two MSN subpopulations. The value of within-correlations (Wopt) for which ΔMSN is maximal depends on the input firing rates and the value of between-correlations (B) (Fig 6). The effect of between-correlations (or shared inputs) depends on the value of the within-correlations. In the regime W ≥ Wopt, an increase in shared input (B) decreases the value of ΔMSN. However, in the regime W ≤ Wopt, an increase in B increases the value of ΔMSN. Finally, for high values of input correlation ∣ΔMSN∣ is small and the two MSN populations have comparable firing rates, indicating the failure of the striatum to bias an action-selection process. Such an operating regime could halt the decision making process in high-conflict decision-making tasks. Indeed, we predict that in high conflict choice tasks, there should be an increase of correlation in the cortical activity reaching the striatum. The preferential connectivity of the FSIs to D1 MSNs gives the FSIs, an ability to control both the difference in the firing rate of the D1 and D2 MSNs and, hence, the DTT. For instance, an increase in FSI activity would reduce D1 MSNs activity and decrease the DTT whereas a decrease in FSI activity would tip the bias in favour of D1, and hence the ‘Go’ pathway. Such a decrease in FSI activity could be due to insufficient excitation from the cortex, or to high input from the pallido-striatal back backprojections. We suggest that selective modulation of the FSI activity, which could shift the DTT in the striatum from ‘No-Go’ to ‘Go’, could be a mechanism for decision making in a high-conflict situation. This functional concept may also help understand neuronal mechanisms underlying various behavioral phenomena. For instance, the arbitration of FSI via the STN-GPe pathway could explain longer reaction times, required to respond to a high-conflict decision. Similarly, increased impulsivity in PD patients with DBS could be explained by the fact that DBS increases GPe firing rates [33], which would decrease the activity of FSIs by pallido-striatal backprojections, resulting in lowering the DTT. Being the primary source of feedforward inhibition, FSIs play an important role in both the existence and the actual value of the DTT. For example, we find that a preferential inhibition of D1 MSNs is required for a DTT to be present for ‘multiplicative’ input. By contrast, an ‘additive’ scenario reveals a DTT, irrespective of the asymmetry of FSI projections onto MSNs. We showed that even in the ‘multiplicative’ input mode the striatum can exhibit a DTT, by clamping the FSIs rates, while increasing cortical input rates. We conclude that FSI activity plays a prominent role in determining the DTT in the striatum, underlining once more the functional importance of pallido-striatal backprojections to FSIs. At the synaptic level dopamine has been described to increase (decrease) the strength of cortico-striatal synapses onto D1 (D2) MSNs. This differential effect of dopamine provides a powerful mechanism to control the DTT and difference between the activities of the two neuronal subpopulations activities and, hence, the DTT. For instance, a dopamine induced increase in synaptic inputs to D1 MSNs shifts the DTT in the favour of D1 MSNs, and thereby increasing the cortical input regime for a ‘Go’ bias. This, in fact, could be a putative mechanism by which external administration of L-Dopa induces dyskinesia as a prominent side-effect. By contrast, a reduction in dopamine level reduces the regime for a ‘Go’ bias, while increasing the regime for a ‘No-Go’ bias. Thus, low dopamine results in higher firing rates in D2 MSNs, possibly manifesting in akinetic symptoms and inducing oscillations in the GPe-STN network [34]. Recent experimental data suggest that the activation of ‘Go’ and ‘No-Go’ pathways might not be exclusive in vivo awake behaving animals. At least at the population level, both D1 and D2 MSNs have been shown to be co-activated and neither of the two pathways were completely shutdown [8]. Consistent with these experimental findings, both D1 and D2 MSNs in our model are co-activated, however, the two populations differed in their firing rates. We note that here our goal is not to reproduce the experimental results of Cui et al. 2013 [8] which involved behaving mice performing self-paced lever pressing to retrieve the reward. Apart from the obvious simplicity of our network model, we think there might be an additional reason for this. In our model, we considered the striatum as unit representing a single action. Indeed, Cui et al. 2013 [8] suggest that a non-selective global deactivation of D2 MSNs in the striatum would abolish suppression of most unwanted motor programs and, hence, lead to hyperkinesia. It can be argued that our model represents such a global activation and deactivation of D1 and D2 in the striatum. The concept of a DTT, if extended to include multiple action channels, could be used to more closely address the results reported in [8]. Thus far, the striatum has been considered as a homogenous structure, with a weak and homogeneous inter-connectivity. Anatomical evidence, however, suggests that there is an inherent asymmetry in the striatal circuit in terms of mutual connections between D1 and D2 MSNs, as well as in feedforward projections from FSIs. In Fig 2, we showed that this asymmetry, for an appropriate activity regime, has the ability to reinforce the cortical bias (bias towards D1 at low cortical rates) or override it (bias towards D2 at high cortical rates). Taking cortical input correlations into consideration, these dynamics become even more complex, as is summarized in Fig 9. Low dopamine levels decrease the regime with D1 bias, while increasing the regime with D2 bias. Alternatively, an increase in dopamine levels increase the area for a D1 bias, while decreasing the area for a D2 bias. An increase in between-correlations in cortical input (B), however, pushes the contour towards the top left corner in Fig 9 (towards higher firing rates and lower values of within-correlation (W) ), thereby enlarging the area of conflict (RHC). We therefore suggest, that the asymmetry in the striatal circuit is useful to cover different regimes of D1 and D2 bias, accommodating characteristic features of cortical input, including rates, input correlations and local dopamine levels. Given the in vitro experimental conditions [14,15], the measurement of the connectivities within and between D1 and D2 MSNs could be biased. In more realistic in vivo conditions, the connectivities of D1 and D2 MSNs may turn out to be different. In particular, if D1 and D2 MSNs would not differ in their connectivities and membrane properties, there is no reason to consider the striatum as a two population network and the role of action selection would need to be performed in the downstream nuclei. Such a single population striatum could initiate this process by ‘winner-less competition’ [11] or by enhancing the contrast of the input [12]. If the D1 and D2 MSNs would only differ in their membrane properties, then our analysis of asymmetric connectivity between the two neuron subpopulations would still be valid, because the effect of different neuron transfer functions could be reduced to a difference in effective connection strengths. In the extreme case, when D2 MSNs would receive more inhibition than D1 MSNs, our analysis remains valid, but we would have to reverse the interpretation. Many disorders in the basal ganglia are related to the imbalance of its functional pathways. PD is thought to be a manifestation of dopamine depletion in the form of a domination of indirect and hyperdirect pathways, while weakening the direct pathway. Dyskinesia, on the other hand, is correlated with hyperactivity of the direct pathway and reduced activity of the indirect pathway [35]. The Tourette syndrome is correlated with decreased activity of FSIs [36]. This (dis) balance of striatal activity can be systematically modeled using the concept of the DTT. In fact, our model provides multiple neuronal mechanisms and a mechanistic understanding of several brain disorders by linking their specific pathophysiology to the DTT. Specifically, we explored three factors that may influence the DTT. In summary our results show that the striatum, with its asymmetric connectivity among and between D1 and D2 MSNs, can act as a threshold device, indicating the increase in cortical input firing rates and correlations by increasing the relative firing rates of D1 and D2 MSNs. The DTT (basically, a threshold on the difference between the two) is a dynamic variable which may represent behavioral state, learning history and motivation level of the animal. Various mechanisms e. g. feedforward inhibition, dopamine, GPe backprojections, tonically active neurons exist that can modulate the DTT and thereby, provide the striatum with a rich computational repertoire. We arrived at this functional description of the striatum as a DTT by considering the striatal network dynamics emerging from including low-level properties such as synaptic weights and connection probabilities. Moreover, we included several factors that can modulate the DTT level by affecting chemical imbalances and changes in neuronal properties. Taken together, we provide a high-level functional model of the striatum, which can be easily linked to low-level properties. Typically, the striatum is included in large-scale functional models [5], acting according to winner-take-all dynamics; an idea no longer supported by experiments [10]. Instead, we argue that our functional description of the striatum provides a more biologically realistic basis for large-scale functional models of basal ganglia function.
The mean population dynamics of the striatal circuit for scenario II were modelled using coupled non-linear differential equations: λ D 1 ˙ = - 0. 01 λ D 1 + S (J 11 λ D 1 + J 12 λ D 2 + J 1 F λ F S I + J C 1 λ C T X) (21) λ D 2 ˙ = - 0. 01 λ D 2 + S (J 21 λ D 1 + J 22 λ D 2 + J 2 F λ F S I + J C 2 λ C T X) (22) The values of parameters used in Eqs 21 and 22 are listed in the Table 1. The non-linear transfer function of the neuron S (z) has a sigmoidal function of the form: S (z) = z z 2 + 1 (23) The parameters J11, J12, J21, J22 are calculated by considering the effective input received by a neuron in the population. This can be quantified as: J x y = N y × ρ x y × I P S C x y × R x × τ x y (24) with x and y either 1 or 2. These parameters are listed in Table 2. This measure calculates the relative inhibitory strength of projections between D1 and D2: although the self connectivity of D1 is comparable to the number of projections it receives from D2 (ρ11 = 0. 26, ρ12 = 0. 27), since D2 makes stronger inhibitory connections than D1, J12 is larger than J11. The striatal network model was based on the spiking network model of the striatum as described in [12], except that the network connectivity was not considered to be homogeneous as in [12]. The simulations were carried out in NEST [43] with networks of 4000 MSNs (2000 D1,2000 D2) and 80 Fast Spiking Interneurons (FSI), consistent with the estimated ratio of MSN and FSI neurons [44,45]. The neuron model parameters for FSIs and MSNs are listed in Table 3. D1 MSNs, D2 MSNs and FSIs received independent excitatory Poisson inputs, mimicking the background cortico-striatal inputs. The cortical input parameters are summarized in Table 4. Connection probabilities for D1 MSNs, D2 MSNs and FSIs were taken from [14,16], also listed in Table 5. The leaky-integrate-and-fire (LIF) neuron model was used to simulate the neurons in the network with the subthreshold dynamics of the membrane potential Vx (t) described by: C x V ˙ x (t) + G x [ V x (t) - V r e s t x ] = I s y n (t) (28) I s y n (t) = I D 1 (t) + I D 2 (t) + I F S I (t) + I C t x (t) (29) where x ∈ D1, D2, FSI. In the above equations, Isyn (t) describes the total cortical excitatory, recurrent and feedforward inhibition to a neuron, Cx is the membrane capacitance, Gx is the leak conductance, and Vrest is the resting membrane potential. When the membrane potential of the neuron reached Vth, a spike was elicited and the membrane potential was reset to Vrest for a refractory duration (tref = 2 ms.) An alpha function was used to model the excitatory synaptic input received by D1, D2 and FSI: g e x c x (t) = { J e x c x t τ e x c e 1 - t τ e x c f o r t ≥ 0 0 f o r t < 0 (30) where x ∈ D1, D2, FSI. The rise times for the excitatory inputs τexc was set to be identical for all neurons. In addition to excitatory input from the cortex, D1 and D2 MSNs received (self and mutual) recurrent inhibition as well as feedforward inhibition from the FSIs. The corresponding inhibitory conductance changes were modelled as: g i n h x (t) = { J i n h x t τ i n h e 1 - t τ i n h f o r t ≥ 0 0 f o r t < 0 (31) where x ∈ D1, D2. The rise times τinh for all inhibitory synaptic conductance transients were set to have identical values. Assuming the synaptic strengths as fixed, the total excitatory conductance G e x c, i x (t) in a MSN i was given by G e x c, i x = ∑ m ∈ K i x ∑ n g e x c x (t - t m n C t x) (32) where x ∈ D1, D2. The sequence of spikes (n’s) impinging on a particular-synapse m are added by the inner sum, while the outer sum runs over all such excitatory synapses m in the set K i x projecting onto neuron i. The set t m n C t x represents the spike times of the excitatory neuron m. The total inhibitory conductance G i n h, i x (t) in a D1 or D2 MSN i was given by G i n h, i x = ∑ m ∈ K i F S I ∑ n g i n h F S I (t - t m n F S I - Δ F S I) + ∑ m ∈ K i D 1 ∑ n g i n h D 1 (t - t m n D 1 - Δ D 1) + ∑ m ∈ K i D 2 ∑ n g i n h D 2 (t - t m n D 2 - Δ D 2) (33) where x ∈ D1, D2 and K i F S I, K i D 1, K i D 2 refer to the pre-synaptic FSIs, D1 and D2 projecting onto neuron i, respectively. The transmission delays of ΔFSI and ΔD1, ΔD2 were fixed to 1ms and 2ms, respectively. The total synaptic current to a MSN i was I i x (t) = - G e x c, i x (t) [ V i x (t) - V e x c x ] - G i n h, i x (t) [ V i x (t) - V i n h x ] (34) where x ∈ D1, D2 and V e x c x, V i n h x denote the reversal potentials of excitatory and inhibitory synaptic currents, respectively. Similarly, for a FSI neuron i, the excitatory conductance was: G e x c, i F S I = ∑ m ∈ K i F S I ∑ n g e x c F S I (t - t m n C t x) (35) and the total synaptic current for the FSI neuron i was I i F S I (t) = - G e x c, i F S I (t) [ V i F S I (t) - V e x c F S I ] (36) The parameter values for both MSNs and FSIs in our network model are summarized in Table 3. To separately control the correlations within and between the pre-synaptic pools of the striatal neurons, we extended the multiple-interaction process (MIP) model of correlated ensemble of Poisson type spike trains [12,23]. The MIP model generates correlations by copying spikes from a spike train (the mother spike train) with a fixed probability (the copy probability, which determined the resulting correlation) to the individual spike trains. This process was implemented in the NEST simulator by introducing a synapse model that transmits spikes with a fixed probability: the lossy synapse. By making many convergent connections using the ‘lossy synapse’ we can mimic the random copying of spikes from the mother spike train to the children process. This way we controlled the correlations within (W) the pre-synaptic pool of a neuron. To introduce correlations between (B) the pre-synaptic pools of two neurons we created a two-layered MIP (Fig 10). We started with a spike train M with a firing rate Rin. In the first layer of MIP we generated N spike trains (C1, C2, ⋯, CN) with a fixed pair-wise correlation (B′) and firing rate (Rin B′). In the second layer, each of these spike trains acted as a mother spike train to generate pairwise correlations in the pre-synaptic pools of individual striatal neurons. Each spike train Ci was used to generate correlated spike trains (Ci0, Ci1, ⋯, Cim) that acted as the pre-synaptic pool of each striatal neuron with a pairwise correlation W and firing rate Rin B′W (Fig 10). The effective correlation between the pre-synaptic pools of two neurons is then defined as B = B′W. Thus, in this model we can control the correlations between and within the pre-synaptic pools by changing the copy probabilities in the first and second layer. B refers to the average correlation between the spiking activities of neurons in the pre-synaptic pools of two striatal neurons. The spike trains in each pre-synaptic pool are themselves correlated with a correlation coefficient W. For such pooled random variables, Bedenbaugh and Gerstein [22] derived the following relationship: ρ 1,2 = B W + 1 n (1 - W) + 𝓞 1 n where ρ1,2 is the correlation coefficient between the two pools of the pre-synaptic neurons, n is the size of each pre-synaptic pool. Because 0 ≤ ρ1,2 ≤ 1, it follows that B ≤ W. Intuitively we can understand this relationship between B and W in the following way: Imagine two pools of identical spike trains, but with individual spikes trains uncorrelated to each other (i. e. B′ = 1 and W = 0). In this case, the average correlations between the two pools will be small because each spike train has only copy in the other pool, while being uncorrelated with all others. However, when W > 0, more such pairs with non-zero correlation will occur, increasing the value of B. Because, B ≤ W, the difference between the firing rates of the two MSN neuronal population in Fig 6 is estimated for the lower triangular region. This relationship is explicitly shown also in Fig 10B, where the measured input correlation between the pools (B) is shown to be less than W. Our model considers the striatum as a single action unit with ‘Go’ and ‘No-go’ counterparts and, hence, with a single DTT. But ideally, there are multiple channels of competing motor programs which can be envisioned as mutually competing DTT’s. Thus, future work will include the extension of this model to incorporate multiple action channels. Secondly, the effect of input correlations is yet not formulated into the analytical framework. This is a non-trivial problem, due to the non-linear transfer of input correlations into output firing rates, specifically if the output neurons are recurrently interconnected. Thirdly, our model explores the striatal dynamics at steady states, since the model showed stable fixed points. However, it is possible that in the presence of additional phenomena, such as plasticity of cortico-striatal or striato-striatal projections or intrinsic plasticity of the striatal neurons etc. , the model may show interesting transient dynamics. Recent work proposed the existence of a ‘decision threshold’ in a different context [50]. They considered, the ‘decision threshold’ as a function of the RT (reaction time) distribution of the rats in a decision task pertaining to the transient dynamics of the basal ganglia. A future extension to this work can explore the effect of DTT due to the asymmetry in the striatal connectivity on the distance to the ‘decision threshold’ in [50], i. e. the effect of DTT on the RTs of a decision. And lastly, though our model abides by many experimental observations, it contradicts one experimental result, to the best of our knowledge. In [51], it was observed that FSIs increased their activity around a choice execution. In our model, a choice execution will correspond to the activation of the D1 pathway, which requires reduced activity in the FSIs, in contrast to the recordings in [51]. We think this contradiction is a consequence of our model of the striatum considering only one channel in the ‘Go’ and ‘No-Go’ pathways.
The basal ganglia (BG) play a crucial role in a variety of cognitive and motor functions. BG dysfunction leads to brain disorders such as Parkinson’s disease. At the main input stage of the BG, the striatum, two competing pathways originate. Neurons projecting on these pathways either express D1 or D2 type dopamine receptors. Because activity of D1 or D2 neurons facilitate go or no-go type decision, it is important to study the balance of the D1 and D2 neuron activity. Contrary to the common assumption thus far, recent data shows an asymmetry in striatal circuit with D1 receiving higher inhibition from D2 and fast-spiking neurons. Here, we studied the functional implications of the asymmetric connectivity between D1 and D2 neurons. Our analysis and simulations show that the asymmetric connectivity between these neurons gives rise to a decision transition threshold (DTT), as a consequence D1 (D2) neurons have higher firing rate at lower (higher) average cortical firing rates. Importantly, DTT can be modulated by input correlations, local connectivity, feedforward inhibition and dopamine. Our results suggest that abnormal changes in the DTT could be a plausible mechanism underlying the cognitive and motor deficits associated with brain diseases involving BG malfunction.
Existence and Control of Go/No-Go Decision Transition Threshold in the Striatum