Modulation of striatal single units by expected reward: a spiny neuron model displaying dopamine-induced bistability

A computational network dynamical modeling for abnormal oscillation and deep brain stimulation control of obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is associated with multi-nodal abnormalities in brain networks, characterized by recurrent intrusive thoughts (obsessions) and repetitive behaviours or mental acts (compulsions), which might manifest as pathological low-frequency oscillations in the frontal EEG and low-frequency bursting firing patterns in the subthalamus nucleus (STN). Abnormalities in the cortical-striatal-thalamic-cortical (CSTC) loop, including dysregulation of serotonin, dopamine, and glutamate systems, are considered to contribute to certain types of OCD. Here, we extend a biophysical computational model to investigate the effect of orbitofronto-subcortical loop abnormalities on network oscillations. Particularly, the OCD lesion process is simulated by the loss of connectivity from striatal parvalbumin interneurons (PV) to medium spiny neurons (MSNs), excessive activation to the hyperdirect pathway, and high dopamine concentrations. By calculating low-frequency oscillation power in the STN, STN burst index, and average firing rates levels of the cortex and thalamus, we demonstrate that the model can explain the pathology of glutamatergic and dopamine system dysregulation, the effects of pathway imbalance, and neuropsychiatric treatment in OCD. In addition, results indicate the abnormal brain rhythms caused by the dysregulation of orbitofronto-subcortical loop may serve as a biomarker of OCD. Our studies can help to understand the cause of OCD, thereby facilitating the diagnosis of OCD and the development of new therapeutics.

Production of adaptive movement patterns via an insect inspired spiking neural network central pattern generator

Navigation in ever-changing environments requires effective motor behaviors. Many insects have developed adaptive movement patterns which increase their success in achieving navigational goals. A conserved brain area in the insect brain, the Lateral Accessory Lobe, is involved in generating small scale search movements which increase the efficacy of sensory sampling. When the reliability of an essential navigational stimulus is low, searching movements are initiated whereas if the stimulus reliability is high, a targeted steering response is elicited. Thus, the network mediates an adaptive switching between motor patterns. We developed Spiking Neural Network models to explore how an insect inspired architecture could generate adaptive movements in relation to changing sensory inputs. The models are able to generate a variety of adaptive movement patterns, the majority of which are of the zig-zagging kind, as seen in a variety of insects. Furthermore, these networks are robust to noise. Because a large spread of network parameters lead to the correct movement dynamics, we conclude that the investigated network architecture is inherently well-suited to generating adaptive movement patterns.

Transient Response of Basal Ganglia Network in Healthy and Low-Dopamine State

The basal ganglia (BG) are crucial for a variety of motor and cognitive functions. Changes induced by persistent low-dopamine (e.g., in Parkinson’s disease; PD) result in aberrant changes in steady-state population activity (β band oscillations) and the transient response of the BG. Typically, a brief cortical stimulation results in a triphasic response in the substantia nigra pars reticulata (SNr; an output of the BG). The properties of the triphasic responses are shaped by dopamine levels. While mechanisms underlying aberrant steady state activity are well studied, it is still unclear which BG interactions are crucial for the aberrant transient responses in the BG. Moreover, it is also unclear whether mechanisms underlying the aberrant changes in steady-state activity and transient response are the same. Here, we used numerical simulations of a network model of BG to identify the key factors that determine the shape of the transient responses. We show that an aberrant transient response of the SNr in the low-dopamine state involves changes in the direct pathway and the recurrent interactions within the globus pallidus externa (GPe) and between GPe and subthalamic nucleus (STN). However, the connections from D2-type spiny projection neurons (D2-SPN) to GPe are most crucial in shaping the transient response and by restoring them to their healthy level, we could restore the shape of transient response even in low-dopamine state. Finally, we show that the changes in BG that result in aberrant transient response are also sufficient to generate pathologic oscillatory activity in the steady state.

Dystonia and dopamine: From phenomenology to pathophysiology

A line of evidence suggests that the pathophysiology of dystonia involves the striatum, whose activity is modulated among other neurotransmitters, by the dopaminergic system. However, the link between dystonia and dopamine appears complex and remains unclear. Here, we propose a physiological approach to investigate the clinical and experimental data supporting a role of the dopaminergic system in the pathophysiology of dystonic syndromes. Because dystonia is a disorder of motor routines, we first focus on the role of dopamine and striatum in procedural learning. Second, we consider the phenomenology of dystonia from every angle in order to search for features giving food for thought regarding the pathophysiology of the disorder. Then, for each dystonic phenotype, we review, when available, the experimental and imaging data supporting a connection with the dopaminergic system. Finally, we propose a putative model in which the different phenotypes could be explained by changes in the balance between the direct and indirect striato-pallidal pathways, a process critically controlled by the level of dopamine within the striatum. Search strategy and selection criteria References for this article were identified through searches in PubMed with the search terms « dystonia », « dopamine", « striatum », « basal ganglia », « imaging data », « animal model », « procedural learning », « pathophysiology », and « plasticity » from 1998 until 2018. Articles were also identified through searches of the authors' own files. Only selected papers published in English were reviewed. The final reference list was generated on the basis of originality and relevance to the broad scope of this review.

Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex

Despite increasing evidence suggesting the cerebellum works in concert with the cortex and basal ganglia, the nature of the reciprocal interactions between these three brain regions remains unclear. This consensus paper gathers diverse recent views on a variety of important roles played by the cerebellum within the cerebello-basal ganglia-thalamo-cortical system across a range of motor and cognitive functions. The paper includes theoretical and empirical contributions, which cover the following topics: recent evidence supporting the dynamical interplay between cerebellum, basal ganglia, and cortical areas in humans and other animals; theoretical neuroscience perspectives and empirical evidence on the reciprocal influences between cerebellum, basal ganglia, and cortex in learning and control processes; and data suggesting possible roles of the cerebellum in basal ganglia movement disorders. Although starting from different backgrounds and dealing with different topics, all the contributors agree that viewing the cerebellum, basal ganglia, and cortex as an integrated system enables us to understand the function of these areas in radically different ways. In addition, there is unanimous consensus between the authors that future experimental and computational work is needed to understand the function of cerebellar-basal ganglia circuitry in both motor and non-motor functions. The paper reports the most advanced perspectives on the role of the cerebellum within the cerebello-basal ganglia-thalamo-cortical system and illustrates other elements of consensus as well as disagreements and open questions in the field.

Untangling Basal Ganglia Network Dynamics and Function: Role of Dopamine Depletion and Inhibition Investigated in a Spiking Network Model

The basal ganglia are a crucial brain system for behavioral selection, and their function is disturbed in Parkinson's disease (PD), where neurons exhibit inappropriate synchronization and oscillations. We present a spiking neural model of basal ganglia including plausible details on synaptic dynamics, connectivity patterns, neuron behavior, and dopamine effects. Recordings of neuronal activity in the subthalamic nucleus and Type A (TA; arkypallidal) and Type I (TI; prototypical) neurons in globus pallidus externa were used to validate the model. Simulation experiments predict that both local inhibition in striatum and the existence of an indirect pathway are important for basal ganglia to function properly over a large range of cortical drives. The dopamine depletion-induced increase of AMPA efficacy in corticostriatal synapses to medium spiny neurons (MSNs) with dopamine receptor D2 synapses (CTX-MSN D2) and the reduction of MSN lateral connectivity (MSN-MSN) were found to contribute significantly to the enhanced synchrony and oscillations seen in PD. Additionally, reversing the dopamine depletion-induced changes to CTX-MSN D1, CTX-MSN D2, TA-MSN, and MSN-MSN couplings could improve or restore basal ganglia action selection ability. In summary, we found multiple changes of parameters for synaptic efficacy and neural excitability that could improve action selection ability and at the same time reduce oscillations. Identification of such targets could potentially generate ideas for treatments of PD and increase our understanding of the relation between network dynamics and network function.

Altered striatal rhythmic activity in cylindromatosis knock-out mice due to enhanced GABAergic inhibition

Despite the highest expression in striatum, physiological function of cylindromatosis (CYLD), a deubiquitinating enzyme, remains unexplored. We found, in the present study, that the duration of spontaneous up-states in the striatum is shorter and membrane potential fluctuation preceding action potential and firing rate are increased in Cyld(-/-) mice. Excess striatal GABAergic inhibition likely plays the major role in this alteration as supported by the findings: (1) the levels of striatal GABAA and GABAB receptors in Cyld(-/-) mice are increased, (2) pharmacological blockade of GABAB receptors rescues the shortened up-state phenotype, and (3) pharmacological blockade of GABAA receptors rescues the power of beta frequency oscillations. Our results indicate that CYLD alters striatal network function by regulating the protein expression levels of GABA receptors.

Presynaptic Inhibition in the Striatum of the Basal Ganglia Improves Pattern Classification and Thus Promotes Superior Goal Selection

This review article takes a multidisciplinary approach to understand how presynaptic inhibition in the striatum of the basal ganglia (BG) contributes to pattern classification and the selection of goals that control behavior. It is a difficult problem both because it is multidimensional and because it is has complex system dynamics. We focus on the striatum because, as the main site for input to the BG, it gets to decide what goals are important to consider.

Involvement of Potassium and Cation Channels in Hippocampal Abnormalities of Embryonic Ts65Dn and Tc1 Trisomic Mice

Down syndrome (DS) mouse models exhibit cognitive deficits, and are used for studying the neuronal basis of DS pathology. To understand the differences in the physiology of DS model neurons, we used dissociated neuronal cultures from the hippocampi of Ts65Dn and Tc1 DS mice. Imaging of [Ca²⁺]i and whole cell patch clamp recordings were used to analyze network activity and single neuron properties, respectively. We found a decrease of ~ 30% in both fast (A-type) and slow (delayed rectifier) outward potassium currents. Depolarization of Ts65Dn and Tc1 cells produced fewer spikes than diploid cells. Their network bursts were smaller and slower than diploids, displaying a 40% reduction in Δf / f₀ of the calcium signals, and a 30% reduction in propagation velocity. Additionally, Ts65Dn and Tc1 neurons exhibited changes in the action potential shape compared to diploid neurons, with an increase in the amplitude of the action potential, a lower threshold for spiking, and a sharp decrease of about 65% in the after-hyperpolarization amplitude.

Numerical simulations reproduced the DS measured phenotype by variations in the conductance of the delayed rectifier and A-type, but necessitated also changes in inward rectifying and M-type potassium channels and in the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels. We therefore conducted whole cell patch clamp measurements of M-type potassium currents, which showed a ~ 90% decrease in Ts65Dn neurons, while HCN measurements displayed an increase of ~ 65% in Ts65Dn cells. Quantitative real-time PCR analysis indicates overexpression of 40% of KCNJ15, an inward rectifying potassium channel, contributing to the increased inhibition. We thus find that changes in several types of potassium channels dominate the observed DS model phenotype.

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Down syndrome model neurons display altered action potential shape, are less excitable and have decreased potassium currents.

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The data is accurately described by a numerical simulation that changes conductance of four potassium and the HCN currents.

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Measurements of the currents related to these four channels, and RT-pcr for a fifth (KCNJ15), confirm the numerical model.

In Down syndrome (DS) cognitive function is impaired, leading us to use cultured hippocampal neuronal networks to investigate the cellular basis of its pathology. DS mouse model neurons are less excitable, produce fewer spikes and generate less network activity. Alterations in several types of potassium currents were detected in these neurons. Numerical simulation of a DS neuron successfully reproduced the experimental results. Beyond extending our understanding of neuronal function and pathology, the alterations in channel conductance can now be targeted with specific drugs so as to point to new directions in therapy of cognitive disabilities of DS.

A computer-based quantitative systems pharmacology model of negative symptoms in schizophrenia: exploring glycine modulation of excitation-inhibition balance

Although many antipsychotics can reasonably control positive symptoms in schizophrenia, patients' return to society is often hindered by negative symptoms and cognitive deficits. As an alternative to animal rodent models that are often not very predictive for the clinical situation, we developed a new computer-based mechanistic modeling approach. This Quantitative Systems Pharmacology approach combines preclinical basic neurophysiology of a biophysically realistic neuronal ventromedial cortical-ventral striatal network identified from human imaging studies that are associated with negative symptoms. Calibration of a few biological coupling parameters using a retrospective clinical database of 34 drug-dose combinations resulted in correlation coefficients greater than 0.60, while a robust quantitative prediction of a number of independent trials was observed. We then simulated the effect of glycine modulation on the anticipated clinical outcomes. The quantitative biochemistry of glycine interaction with the different NMDA-NR₂ subunits, neurodevelopmental trajectory of the NMDA-NR_2B in the human schizophrenia pathology, their specific localization on excitatory vs. inhibitory interneurons and the electrogenic nature of the glycine transporter resulted in an inverse U-shape dose-response with an optimum in the low micromolar glycine concentration. Quantitative systems pharmacology based computer modeling of complex humanized brain circuits is a powerful alternative approach to explain the non-monotonic dose-response observed in past clinical trial outcomes with sarcosine, D-cycloserine, glycine, or D-serine or with glycine transporter inhibitors. In general it can be helpful to better understand the human neurophysiology of negative symptoms, especially with targets that show non-monotonic dose-responses.

Contralateral disconnection of the rat prelimbic cortex and dorsomedial striatum impairs cue-guided behavioral switching

Switches in reward outcomes or reward-predictive cues are two fundamental ways in which information is used to flexibly shift response patterns. The rat prelimbic cortex and dorsomedial striatum support behavioral flexibility based on a change in outcomes. The present experiments investigated whether these two brain regions are necessary for conditional discrimination performance in which a switch in reward-predictive cues occurs every three to six trials. The GABA agonists baclofen and muscimol infused into the prelimbic cortex significantly impaired performance leading rats to adopt an inappropriate turn strategy. The NMDA receptor antagonist D-AP5 infused into the dorsomedial striatum or prelimbic cortex and dorsomedial striatum contralateral disconnection impaired performance due to a rat failing to switch a response choice for an entire trial block in about two out of 13 test blocks. In an additional study, contralateral disconnection did not affect nonswitch discrimination performance. The results suggest that the prelimbic cortex and dorsomedial striatum are necessary to support cue-guided behavioral switching. The prelimbic cortex may be critical for generating alternative response patterns while the dorsomedial striatum supports the selection of an appropriate response when cue information must be used to flexibly switch response patterns.

Timing and expectation of reward: a neuro-computational model of the afferents to the ventral tegmental area

Neural activity in dopaminergic areas such as the ventral tegmental area is influenced by timing processes, in particular by the temporal expectation of rewards during Pavlovian conditioning. Receipt of a reward at the expected time allows to compute reward-prediction errors which can drive learning in motor or cognitive structures. Reciprocally, dopamine plays an important role in the timing of external events. Several models of the dopaminergic system exist, but the substrate of temporal learning is rather unclear. In this article, we propose a neuro-computational model of the afferent network to the ventral tegmental area, including the lateral hypothalamus, the pedunculopontine nucleus, the amygdala, the ventromedial prefrontal cortex, the ventral basal ganglia (including the nucleus accumbens and the ventral pallidum), as well as the lateral habenula and the rostromedial tegmental nucleus. Based on a plausible connectivity and realistic learning rules, this neuro-computational model reproduces several experimental observations, such as the progressive cancelation of dopaminergic bursts at reward delivery, the appearance of bursts at the onset of reward-predicting cues or the influence of reward magnitude on activity in the amygdala and ventral tegmental area. While associative learning occurs primarily in the amygdala, learning of the temporal relationship between the cue and the associated reward is implemented as a dopamine-modulated coincidence detection mechanism in the nucleus accumbens.

Endocannabinoid-dependent modulation of phasic dopamine signaling encodes external and internal reward-predictive cues

The mesolimbic dopamine (DA) system plays an integral role in incentive motivation and reward seeking and a growing body of evidence identifies signal transduction at cannabinoid receptors as a critical modulator of this system. Indeed, administration of exogenous cannabinoids results in burst firing of DA neurons of the ventral tegmental area and increases extracellular DA in the nucleus accumbens (NAcc). Implementation of fast-scan cyclic voltammetry (FSCV) confirms the ability of cannabinoids to augment DA within the NAcc on a subsecond timescale. The use of FSCV along with newly developed highly selective pharmacological compounds advances our understanding of how cannabinoids influence DA transmission and highlights a role for endocannabinoid-modulated subsecond DAergic activation in the incentive motivational properties of not only external, but also internal reward-predictive cues. For example, our laboratory has recently demonstrated that in mice responding under a fixed-interval (FI) schedule for food reinforcement, fluctuations in NAcc DA signal the principal cue predictive of reinforcer availability - time. That is, as the interval progresses, NAcc DA levels decline leading to accelerated food seeking and the resulting characteristic FI scallop pattern of responding. Importantly, administration of WIN 55,212-2, a synthetic cannabinoid agonist, or JZL184, an indirect cannabinoid agonist, increases DA levels during the interval and disrupts this pattern of responding. Along with a wealth of other reports, these results illustrate the role of cannabinoid receptor activation in the regulation of DA transmission and the control of temporally guided reward seeking. The current review will explore the striatal beat frequency model of interval timing as it pertains to cannabinoid signaling and propose a neurocircuitry through which this system modulates interoceptive time cues.

A model-based prediction of the calcium responses in the striatal synaptic spines depending on the timing of cortical and dopaminergic inputs and post-synaptic spikes

The dopamine-dependent plasticity of the cortico-striatal synapses is considered as the cellular mechanism crucial for reinforcement learning. The dopaminergic inputs and the calcium responses affect the synaptic plasticity by way of the signaling cascades within the synaptic spines. The calcium concentration within synaptic spines, however, is dependent on multiple factors including the calcium influx through ionotropic glutamate receptors, the intracellular calcium release by activation of metabotropic glutamate receptors, and the opening of calcium channels by EPSPs and back-propagating action potentials. Furthermore, dopamine is known to modulate the efficacies of NMDA receptors, some of the calcium channels, and sodium and potassium channels that affect the back propagation of action potentials. Here we construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials. The model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up- and down-states. We investigated the calcium responses by systematically varying the timings of the glutamatergic and dopaminergic inputs relative to the action potential and found that the calcium response and the subsequent synaptic potentiation is maximal when the dopamine input precedes glutamate input and action potential. The prediction is not consistent with the hypothesis that the dopamine input provides the reward prediction error for reinforcement learning. The finding suggests that there is an unknown learning mechanisms at the network level or an unknown cellular mechanism for calcium dynamics and signaling cascades.

A strategy for developing new treatment paradigms for neuropsychiatric and neurocognitive symptoms in Alzheimer's disease

Successful disease modifying drug development for Alzheimer's disease (AD) has hit a roadblock with the recent failures of amyloid-based therapies, highlighting the translational disconnect between preclinical animal models and clinical outcome. Although disease modifying therapies are the Holy Grail to pursue, symptomatic therapies addressing cognitive and neuropsychiatric aspects of the disease are also extremely important for the quality of life of patients and caregivers. Despite the fact that neuropsychiatric problems in Alzheimer patients are the major driver for costs associated with institutionalization, no good preclinical animal models with predictive validity have been documented. We propose a combination of quantitative systems pharmacology (QSP), phenotypic screening and preclinical animal models as a novel strategy for addressing the bottleneck in both cognitive and neuropsychiatric drug discovery and development for AD. Preclinical animal models such as transgene rats documenting changes in neurotransmitters with tau and amyloid pathology will provide key information that together with human imaging, pathology and clinical data will inform the virtual patient model. In this way QSP modeling can partially overcome the translational disconnect and reduce the attrition of drug programs in the clinical setting. This approach is different from target driven drug discovery as it aims to restore emergent properties of the networks and therefore likely will identify multitarget drugs. We review examples on how this hybrid humanized QSP approach has been helpful in predicting clinical outcomes in schizophrenia treatment and cognitive impairment in AD and expand on how this strategy could be applied to neuropsychiatric symptoms in dementia. We believe such an innovative approach when used carefully could change the Research and Development paradigm for symptomatic treatment in AD.

NMDA and GABAB (KIR) conductances: the "perfect couple" for bistability

Networks that produce persistent firing in response to novel input patterns are thought to be important in working memory and other information storage functions. One possible mechanism for maintaining persistent firing is dendritic voltage bistability in which the depolarized state depends on the voltage dependence of the NMDA conductance at recurrent synapses. In previous models, the hyperpolarized state is dependent on voltage-independent conductances, including GABA(A). The interplay of these conductances leads to bistability, but its robustness is limited by the fact that the conductance ratio must be within a narrow range. The GABA(B) component of inhibitory transmission was not considered in previous analyses. Here, we show that the voltage dependence of the inwardly rectifying potassium (KIR) conductance activated by GABA(B) receptors adds substantial robustness to network simulations of bistability and the persistent firing that it underlies. The hyperpolarized state is robust because, at hyperpolarized potentials, the GABA(B)/KIR conductance is high and the NMDA conductance is low; the depolarized state is robust because, at depolarized potentials, the NMDA conductance is high and the GABA(B)/KIR conductance is low. Our results suggest that this complementary voltage dependence of GABA(B)/KIR and NMDA conductances makes them a "perfect couple" for producing voltage bistability.

Quantitative systems pharmacology as an extension of PK/PD modeling in CNS research and development

Quantitative systems pharmacology (QSP) is a recent addition to the modeling and simulation toolbox for drug discovery and development and is based upon mathematical modeling of biophysical realistic biological processes in the disease area of interest. The combination of preclinical neurophysiology information with clinical data on pathology, imaging and clinical scales makes it a real translational tool. We will discuss the specific characteristics of QSP and where it differs from PK/PD modeling, such as the ability to provide support in target validation, clinical candidate selection and multi-target MedChem projects. In clinical development the approach can provide additional and unique evaluation of the effect of comedications, genotypes and disease states (patient populations) even before the initiation of actual trials. A powerful property is the ability to perform failure analysis. By giving examples from the CNS R&D field in schizophrenia and Alzheimer's disease, we will illustrate how this approach can make a difference for CNS R&D projects.

A model of the neural substrates for exploratory dynamics in basal ganglia

We present a model of basal ganglia (BG) that departs from the classical Go/NoGo picture of the function of its key pathways-the Direct and Indirect Pathways (DP and IP). Between the Go and NoGo regimes, we posit a third Explore regime, which denotes random exploration of action alternatives. Striatal dopamine (DA) is assumed to switch between DP and IP activation. The IP is modeled as a loop of the subthalamic nucleus (STN) and the Globus Pallidus externa (GPe). Simulations reveal that while the model displays Go and NoGo regimes for extreme values of DA, at intermediate values of DA, it exhibits exploratory behavior, which originates from the chaotic activity of the STN-GPe loop. We describe a series of BG models based on Go/Explore/NoGo approach, to explain the role of BG in three cases: (1) a simple action selection task, (2) reaching, and (3) willed action.

Learning intrinsic excitability in medium spiny neurons

We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.

Blinded prospective evaluation of computer-based mechanistic schizophrenia disease model for predicting drug response

The tremendous advances in understanding the neurobiological circuits involved in schizophrenia have not translated into more effective treatments. An alternative strategy is to use a recently published ‘Quantitative Systems Pharmacology’ computer-based mechanistic disease model of cortical/subcortical and striatal circuits based upon preclinical physiology, human pathology and pharmacology. The physiology of 27 relevant dopamine, serotonin, acetylcholine, norepinephrine, gamma-aminobutyric acid (GABA) and glutamate-mediated targets is calibrated using retrospective clinical data on 24 different antipsychotics. The model was challenged to predict quantitatively the clinical outcome in a blinded fashion of two experimental antipsychotic drugs; JNJ37822681, a highly selective low-affinity dopamine D₂ antagonist and ocaperidone, a very high affinity dopamine D₂ antagonist, using only pharmacology and human positron emission tomography (PET) imaging data. The model correctly predicted the lower performance of JNJ37822681 on the positive and negative syndrome scale (PANSS) total score and the higher extra-pyramidal symptom (EPS) liability compared to olanzapine and the relative performance of ocaperidone against olanzapine, but did not predict the absolute PANSS total score outcome and EPS liability for ocaperidone, possibly due to placebo responses and EPS assessment methods. Because of its virtual nature, this modeling approach can support central nervous system research and development by accounting for unique human drug properties, such as human metabolites, exposure, genotypes and off-target effects and can be a helpful tool for drug discovery and development.

Learning to select actions with spiking neurons in the Basal Ganglia

We expand our existing spiking neuron model of decision making in the cortex and basal ganglia to include local learning on the synaptic connections between the cortex and striatum, modulated by a dopaminergic reward signal. We then compare this model to animal data in the bandit task, which is used to test rodent learning in conditions involving forced choice under rewards. Our results indicate a good match in terms of both behavioral learning results and spike patterns in the ventral striatum. The model successfully generalizes to learning the utilities of multiple actions, and can learn to choose different actions in different states. The purpose of our model is to provide both high-level behavioral predictions and low-level spike timing predictions while respecting known neurophysiology and neuroanatomy.

Significance of input correlations in striatal function

The striatum is the main input station of the basal ganglia and is strongly associated with motor and cognitive functions. Anatomical evidence suggests that individual striatal neurons are unlikely to share their inputs from the cortex. Using a biologically realistic large-scale network model of striatum and cortico-striatal projections, we provide a functional interpretation of the special anatomical structure of these projections. Specifically, we show that weak pairwise correlation within the pool of inputs to individual striatal neurons enhances the saliency of signal representation in the striatum. By contrast, correlations among the input pools of different striatal neurons render the signal representation less distinct from background activity. We suggest that for the network architecture of the striatum, there is a preferred cortico-striatal input configuration for optimal signal representation. It is further enhanced by the low-rate asynchronous background activity in striatum, supported by the balance between feedforward and feedback inhibitions in the striatal network. Thus, an appropriate combination of rates and correlations in the striatal input sets the stage for action selection presumably implemented in the basal ganglia.

Modulation of synaptic potentials and cell excitability by dendritic KIR and KAs channels in nucleus accumbens medium spiny neurons: a computational study

The nucleus accumbens (NAc), a critical structure of the brain reward circuit, is implicated in normal goal-directed behaviour and learning as well as pathological conditions like schizophrenia and addiction. Its major cellular substrates, the medium spiny (MS) neurons, possess a wide variety of dendritic active conductances that may modulate the excitatory post synaptic potentials (EPSPs) and cell excitability. We examine this issue using a biophysically detailed 189-compartment stylized model of the NAc MS neuron, incorporating all the known active conductances. We find that, of all the active channels, inward rectifying K+ (KIR) channels play the primary role in modulating the resting membrane potential (RMP) and EPSPs in the down-state of the neuron. Reduction in the conductance of KIR channels evokes facilitatory effects on EPSPs accompanied by rises in local input resistance and membrane time constant. At depolarized membrane potentials closer to up-state levels, the slowly inactivating A-type potassium channel (KAs) conductance also plays a strong role in determining synaptic potential parameters and cell excitability. We discuss the implications of our results for the regulation of accumbal MS neuron biophysics and synaptic integration by intrinsic factors and extrinsic agents such as dopamine.

Differential effects of paced and unpaced responding on delayed serial order recall in schizophrenia

Working memory for temporal order is a component of working memory that is especially dependent on striatal systems, but has not been extensively studied in schizophrenia. This study was designed to characterize serial order reproduction by adapting a spatial serial order task developed for nonhuman primate studies, while controlling for working memory load and whether responses were initiated freely (unpaced) or in an externally paced format. Clinically stable schizophrenia patients (n=27) and psychiatrically healthy individuals (n=25) were comparable on demographic variables and performance on standardized tests of immediate serial order recall (Digit Span, Spatial Span). No group differences were observed for serial order recall when read sequence reproduction was unpaced. However, schizophrenia patients exhibited significant impairments when responding was paced, regardless of sequence length or retention delay. Intact performance by schizophrenia patients during the unpaced condition indicates that prefrontal storage and striatal output systems are sufficiently intact to learn novel response sequences and hold them in working memory to perform serial order tasks. However, retention for newly learned response sequences was disrupted in schizophrenia patients by paced responding, when read-out of each element in the response sequence was externally controlled. The disruption of memory for serial order in paced read-out condition indicates a deficit in frontostriatal interaction characterized by an inability to update working memory stores and deconstruct 'chunked' information.

A model study on the circuit mechanism underlying decision-making in Drosophila

Previous elegant experiments in a flight simulator showed that conditioned Drosophila is able to make a clear-cut decision to avoid potential danger. When confronted with conflicting visual cues, the relative saliency of two competing cues is found to be a sensory ruler for flies to judge which cue should be used for decision-making. Further genetic manipulations and immunohistological analysis revealed that the dopamine system and mushroom bodies are indispensable for such a clear-cut or nonlinear decision. The neural circuit mechanism, however, is far from being clear. In this paper, we adopt a computational modeling approach to investigate how different brain areas and the dopamine system work together to drive a fly to make a decision. By developing a systems-level neural network, a two-pathway circuit is proposed. Besides a direct pathway from a feature binding area to the motor center, another connects two areas via the mushroom body, a target of dopamine release. A raised dopamine level is hypothesized to be induced by complex choice tasks and to enhance lateral inhibition and steepen the units' response gain in the mushroom body. Simulations show that training helps to assign values to formerly neutral features. For a circuit model with a blocked mushroom body, the direct pathway passes all alternatives to the motor center without changing original values, giving rise to a simple choice characterized by a linear choice curve. With respect to an intact circuit, enhanced lateral inhibition dependent on dopamine critically promotes competition between alternatives, turning the linear- into nonlinear choice behavior. Results account well for experimental data, supporting the reasonableness of model working hypotheses. Several testable predictions are made for future studies.

Multiple firing patterns in deep dorsal horn neurons of the spinal cord: computational analysis of mechanisms and functional implications

Deep dorsal horn relay neurons (dDHNs) of the spinal cord are known to exhibit multiple firing patterns under the control of local metabotropic neuromodulation: tonic firing, plateau potential, and spontaneous oscillations. This work investigates the role of interactions between voltage-gated channels and the occurrence of different firing patterns and then correlates these two phenomena with their functional role in sensory information processing. We designed a conductance-based model using the NEURON software package, which successfully reproduced the classical features of plateau in dDHNs, including a wind-up of the neuronal response after repetitive stimulation. This modeling approach allowed us to systematically test the impact of conductance interactions on the firing patterns. We found that the expression of multiple firing patterns can be reproduced by changes in the balance between two currents (L-type calcium and potassium inward rectifier conductances). By investigating a possible generalization of the firing state switch, we found that the switch can also occur by varying the balance of any hyperpolarizing and depolarizing conductances. This result extends the control of the firing switch to neuromodulators or to network effects such as synaptic inhibition. We observed that the switch between the different firing patterns occurs as a continuous function in the model, revealing a particular intermediate state called the accelerating mode. To characterize the functional effect of a firing switch on information transfer, we used correlation analysis between a model of peripheral nociceptive afference and the dDHN model. The simulation results indicate that the accelerating mode was the optimal firing state for information transfer.

Different corticostriatal integration in spiny projection neurons from direct and indirect pathways

The striatum is the principal input structure of the basal ganglia. Major glutamatergic afferents to the striatum come from the cerebral cortex and make monosynaptic contacts with medium spiny projection neurons (MSNs) and interneurons. Also: glutamatergic afferents to the striatum come from the thalamus. Despite differences in axonal projections, dopamine (DA) receptors expression and differences in excitability between MSNs from “direct” and “indirect” basal ganglia pathways, these neuronal classes have been thought as electrophysiologically very similar. Based on work with bacterial artificial chromosome (BAC) transgenic mice, here it is shown that corticostriatal responses in D₁- and D₂-receptor expressing MSNs (D₁- and D₂-MSNs) are radically different so as to establish an electrophysiological footprint that readily differentiates between them. Experiments in BAC mice allowed us to predict, with high probability (P > 0.9), in rats or non-BAC mice, whether a recorded neuron, from rat or mouse, was going to be substance P or enkephalin (ENK) immunoreactive. Responses are more prolonged and evoke more action potentials in D₁-MSNs, while they are briefer and exhibit intrinsic autoregenerative responses in D₂-MSNs. A main cause for these differences was the interaction of intrinsic properties with the inhibitory contribution in each response. Inhibition always depressed corticostriatal depolarization in D₂-MSNs, while it helped in sustaining prolonged depolarizations in D₁-MSNs, in spite of depressing early discharge. Corticostriatal responses changed dramatically after striatal DA depletion in 6-hydroxy-dopamine (6-OHDA) lesioned animals: a response reduction was seen in substance P (SP)+ MSNs whereas an enhanced response was seen in ENK+ MSNs. The end result was that differences in the responses were greatly diminished after DA depletion.

Rethinking Situatedness

Common coding proposes a shared representation in the brain between perception, execution, and imagination of movement. This neural-level representation is considered to support a “direct activation” of action by perception/imagination of movement at the behavioral level. We examine how this two-tier model, where a representation at the neural level supports “direct activation” at the behavioral level, relates to the notion of situatedness—the real-time access and use of environment structure for cognition. Reviewing four leading environment-oriented approaches to cognition (ecological psychology, situated action, distributed cognition, and ecological rationality), we show that the access and use of the environment structures proposed by three of these approaches require a mechanism that supports both representations and direct access. We argue that the two-tier common coding model provides such an integrated mechanism, and, further, it extends and refines the notion of direct perception.

Convergent evidence for abnormal striatal synaptic plasticity in dystonia

Dystonia is a functionally disabling movement disorder characterized by abnormal movements and postures. Although substantial recent progress has been made in identifying genetic factors, the pathophysiology of the disease remains a mystery. A provocative suggestion gaining broader acceptance is that some aspect of neural plasticity may be abnormal. There is also evidence that, at least in some forms of dystonia, sensorimotor "use" may be a contributing factor. Most empirical evidence of abnormal plasticity in dystonia comes from measures of sensorimotor cortical organization and physiology. However, the basal ganglia also play a critical role in sensorimotor function. Furthermore, the basal ganglia are prominently implicated in traditional models of dystonia, are the primary targets of stereotactic neurosurgical interventions, and provide a neural substrate for sensorimotor learning influenced by neuromodulators. Our working hypothesis is that abnormal plasticity in the basal ganglia is a critical link between the etiology and pathophysiology of dystonia. In this review we set up the background for this hypothesis by integrating a large body of disparate indirect evidence that dystonia may involve abnormalities in synaptic plasticity in the striatum. After reviewing evidence implicating the striatum in dystonia, we focus on the influence of two neuromodulatory systems: dopamine and acetylcholine. For both of these neuromodulators, we first describe the evidence for abnormalities in dystonia and then the means by which it may influence striatal synaptic plasticity. Collectively, the evidence suggests that many different forms of dystonia may involve abnormal plasticity in the striatum. An improved understanding of these altered plastic processes would help inform our understanding of the pathophysiology of dystonia, and, given the role of the striatum in sensorimotor learning, provide a principled basis for designing therapies aimed at the dynamic processes linking etiology to pathophysiology of the disease.

Capturing dopaminergic modulation and bimodal membrane behaviour of striatal medium spiny neurons in accurate, reduced models

Loss of dopamine from the striatum can cause both profound motor deficits, as in Parkinson's disease, and disrupt learning. Yet the effect of dopamine on striatal neurons remains a complex and controversial topic, and is in need of a comprehensive framework. We extend a reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tune our D1 and D2 receptor MSN models using data from a recent large-scale compartmental model. The new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. We analytically derive a full set of equilibrium points and their stability for the original and dopamine modulated forms of the MSN model. We find that the stability types are not changed by dopamine activation, and our models predict that the MSN is never bistable. Nonetheless, the MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors. Our models also predict a more pronounced membrane potential bimodality following D1 receptor activation. This work thus establishes reduced yet accurate dopamine-modulated models of MSNs, suitable for use in large-scale models of the striatum. More importantly, these provide a tractable framework for further study of dopamine's effects on computation by individual neurons.

Cooperative activity of neurons in the nucleus accumbens and frontal cortex in cats trained to select reinforcements of different value

Results obtained at the level of the organization of interneuronal interactions of cells in the nucleus accumbens and frontal cortex revealed the features of the involvement of this component in "impulsive" and "self-controlled" behavior, consisting of an increase in bidirectional interactions between the structures of interest, accompanied by simultaneous reductions in the regularity of interactions with increases in "impulsivity" and decreases in "self-control." Long-latency reactions appearing only in "impulsive" animals were associated with decreases in the control of frontal cortex cells by the nucleus accumbens during the signal period, which correlated with the low activity of the network activity of the nucleus accumbens in these animals. Comparison of the patterns of frontal-accumbens interactions as the animals performed a single type of activity demonstrated that the connections in neuron pairs during the presignal and signal periods were similar, while significant differences in patterns were seen during the performance of different types of activity.

The first second: models of short-term memory traces in the brain

Many network models in computational neuroscience rise to the challenge of explaining behavioural phenomena ranging from microseconds to tens of seconds using components operating mostly on a time-scale of milliseconds. These models have in common that the underlying system has a memory, which implies that its output depends on its past input history. In this review we compare how such memory traces or delayed responses may be implemented in different brain areas supporting a diversity of functions.

Computational perspectives on forebrain microcircuits implicated in reinforcement learning, action selection, and cognitive control

Abundant new information about signaling pathways in forebrain microcircuits presents many challenges, and opportunities for discovery, to computational neuroscientists who strive to bridge from microcircuits to flexible cognition and action. Accurate treatment of microcircuit pathways is especially critical for creating models that correctly predict the outcomes of candidate neurological therapies. Recent models are trying to specify how cortical circuits that enable planning and voluntary actions interact with adaptive subcortical microcircuits in the basal ganglia. The basal ganglia are strongly implicated in reinforcement learning, and in all behavior and cognition over which the frontal lobes exert flexible control. The persisting role of the basal ganglia shows that ancient vertebrate designs for motivated action selection proved adaptable enough to support many "modern" behavioral innovations, including fluent generation of language and speech. This paper summarizes how recent models have incorporated realistic representations of microcircuit features, and have begun to trace their computational implications. Also summarized are recent empirical discoveries that provide guidance regarding how to formulate the rules for synaptic modification that govern learning in cortico-striatal pathways. Such efforts are contributing to an emerging synthesis based on an interlocking set of computational hypotheses regarding cortical interactions with basal ganglia and thalamic nuclei. These hypotheses specify how specialized microcircuits solve learning and control problems inherent to the brain's parallel design.

A neurocomputational model of tonic and phasic dopamine in action selection: a comparison with cognitive deficits in Parkinson's disease

The striatal dopamine signal has multiple facets; tonic level, phasic rise and fall, and variation of the phasic rise/fall depending on the expectation of reward/punishment. We have developed a network model of the striatal direct pathway using an ionic current level model of the medium spiny neuron that incorporates currents sensitive to changes in the tonic level of dopamine. The model neurons in the network learn action selection based on a novel set of mathematical rules that incorporate the phasic change in the dopamine signal. This network model is capable of learning to perform a sequence learning task that in humans is thought to be dependent on the basal ganglia. When both tonic and phasic levels of dopamine are decreased, as would be expected in unmedicated Parkinson's disease (PD), the model reproduces the deficits seen in a human PD group off medication. When the tonic level is increased to normal, but with reduced phasic increases and decreases in response to reward and punishment, respectively, as would be expected in PD medicated with L-Dopa, the model again reproduces the human data. These findings support the view that the cognitive dysfunctions seen in Parkinson's disease are not solely either due to the decreased tonic level of dopamine or to the decreased responsiveness of the phasic dopamine signal to reward and punishment, but to a combination of the two factors that varies dependent on disease stage and medication status.

Inhibitory contribution to suprathreshold corticostriatal responses: an experimental and modeling study

Neostriatal neurons may undergo events of spontaneous synchronization as those observed in recurrent networks of excitatory neurons, even when cortical afferents are transected. It is necessary to explain these events because the neostriatum is a recurrent network of inhibitory neurons. Synchronization of neuronal activity may be caused by plateau-like depolarizations. Plateau-like orthodromic depolarizations that resemble up-states in medium spiny neostriatal neurons (MSNs) may be induced by a single corticostriatal suprathreshold stimulus. Slow synaptic depolarizations may last hundreds of milliseconds, decay slower than the monosynaptic glutamatergic synaptic potentials that induce them, and sustain repetitive firing. Because inhibitory inputs impinging onto MSNs have a reversal potential above the resting membrane potential but below the threshold for firing, they conform a type of "shunting inhibition". This work asks if shunting GABAergic inputs onto MSNs arrive asynchronously enough as to help in sustaining the plateau-like corticostriatal response after a single cortical stimulus. This may help to begin explaining autonomous processing in the striatal micro-circuitry in the presence of a tonic excitatory drive and independently of spatio-temporally organized inputs. It is shown here that besides synaptic currents from AMPA/KA- and NMDA-receptors, as well as L-type intrinsic Ca(2+)- currents, inhibitory synapses help in maintaining the slow depolarization, although they accomplish the role of depressing firing at the beginning of the response. We then used a NEURON model of spiny cells to show that inhibitory synapses arriving asynchronously on the dendrites can help to simulate a plateau potential similar to that observed experimentally.

Python scripting in the nengo simulator

Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.

Dynamical model of salience gated working memory, action selection and reinforcement based on basal ganglia and dopamine feedback

A simple working memory model based on recurrent network activation is proposed and its application to selection and reinforcement of an action is demonstrated as a solution to the temporal credit assignment problem. Reactivation of recent salient cue states is generated and maintained as a type of salience gated recurrently active working memory, while lower salience distractors are ignored. Cue reactivation during the action selection period allows the cue to select an action while its reactivation at the reward period allows the reinforcement of the action selected by the reactivated state, which is necessarily the action which led to the reward being found. A down-gating of the external input during the reactivation and maintenance prevents interference. A double winner-take-all system which selects only one cue and only one action allows the targeting of the cue-action allocation to be modified. This targeting works both to reinforce a correct cue-action allocation and to punish the allocation when cue-action allocations change. Here we suggest a firing rate neural network implementation of this system based on the basal ganglia anatomy with input from a cortical association layer where reactivations are generated by signals from the thalamus. Striatum medium spiny neurons represent actions. Auto-catalytic feedback from a dopamine reward signal modulates three-way Hebbian long term potentiation and depression at the cortical-striatal synapses which represent the cue-action associations. The model is illustrated by the numerical simulations of a simple example--that of associating a cue signal to a correct action to obtain reward after a delay period, typical of primate cue reward tasks. Through learning, the model shows a transition from an exploratory phase where actions are generated randomly, to a stable directed phase where the animal always chooses the correct action for each experienced state. When cue-action allocations change, we show that this is noticed by the model, the incorrect cue-action allocations are punished and the correct ones discovered.

Attractor models of working memory and their modulation by reward

This work reports an empirical examination of two key issues in theoretical neuroscience: distractibility in the context of working memory (WM) and its reward dependence. While these issues have been examined fruitfully in isolation (e.g. Macoveanu et al. in Biol Cybern 96(4): 407-19, 2007), we address them here in tandem, with a focus on how distractibility and reward interact. In particular, we parameterise an observation model that embodies the nonlinear form of such interactions, as described in a recent neuronal network model (Gruber et al. in J Comput Neurosci 20:153-166, 2006). We observe that memory for a target stimulus can be corrupted by distracters in the delay period. Interestingly, in contrast to our theoretical predictions, this corruption was only partial. Distracters do not simply overwrite target; rather, a compromise is reached between target and distracter. Finally, we observed a trend towards a reduced distractibility under conditions of high reward. We discuss the implications of these findings for theoretical formulations of basal and dopamine (DA)-modulated neural bump- attractor networks of working memory.

Action selection and refinement in subcortical loops through basal ganglia and cerebellum

Subcortical loops through the basal ganglia and the cerebellum form computationally powerful distributed processing modules (DPMs). This paper relates the computational features of a DPM's loop through the basal ganglia to experimental results for two kinds of natural action selection. First, functional imaging during a serial order recall task was used to study human brain activity during the selection of sequential actions from working memory. Second, microelectrode recordings from monkeys trained in a step-tracking task were used to study the natural selection of corrective submovements. Our DPM-based model assisted in the interpretation of puzzling data from both of these experiments. We come to posit that the many loops through the basal ganglia each regulate the embodiment of pattern formation in a given area of cerebral cortex. This operation serves to instantiate different kinds of action (or thought) mediated by different areas of cerebral cortex. We then use our findings to formulate a model of the aetiology of schizophrenia.

Effects of dopaminergic modulation on the integrative properties of the ventral striatal medium spiny neuron

Dopaminergic modulation produces a variety of functional changes in the principal cell of the striatum, the medium spiny neuron (MSN). Using a 189-compartment computational model of a ventral striatal MSN, we simulated whole cell D1- and D2-receptor-mediated modulation of both intrinsic (sodium, calcium, and potassium) and synaptic currents (AMPA and NMDA). Dopamine (DA) modulations in the model were based on a review of published experiments in both ventral and dorsal striatum. To objectively assess the net effects of DA modulation, we combined reported individual channel modulations into either D1- or D2-receptor modulation conditions and studied them separately. Contrary to previous suggestions, we found that D1 modulation had no effect on MSN nonlinearity and could not induce bistability. In agreement with previous suggestions, we found that dopaminergic modulation leads to changes in input filtering and neuronal excitability. Importantly, the changes in neuronal excitability agree with the classical model of basal ganglia function. We also found that DA modulation can alter the integration time window of the MSN. Interestingly, the effects of DA modulation of synaptic properties opposed the effects of DA modulation of intrinsic properties, with the synaptic modulations generally dominating the net effect. We interpret this lack of synergy to suggest that the regulation of whole cell integrative properties is not the primary functional purpose of DA. We suggest that D1 modulation might instead primarily regulate calcium influx to dendritic spines through NMDA and L-type calcium channels, by both direct and indirect mechanisms.