Chronic alcohol use disorder (AUD) is associated with a transition from reward-driven to negative affect-driven alcohol seeking. However, the circuit-level mechanisms linking motivational, affective, and stress systems remain unclear. We develop a reduced computational model that integrates the basolateral amygdala (BLA), bed nucleus of the stria terminalis (BNST), and ventral tegmental area (VTA) into a closed feedback loop to examine how chronic alcohol exposure reshapes circuit dynamics. The model includes antagonistic BLA reward- and aversion-encoding populations, CRF-positive and CRF-negative BNST interneurons and projection neurons, and VTA GABA and dopamine (DA) neurons. Simulated DA modulation of recurrent inhibition in the BLA completes the feedback loop, with core behaviors governed by BLA competition, BNST-mediated control of DA levels, and DA-modulated BLA inhibition. Parameter changes mimicking chronic alcohol exposure are constrained by experimental data and implemented as alterations in BLA→BNST connectivity, BNST excitability, and paraventricular thalamus inputs. Simulations show that these adaptations (i) shift the system from a moderately high, "safe" reward/anxiolytic state to a hypodopaminergic, anxiety-prone state with reduced BNST CRF+ projection activity and elevated BNST anxiogenic interneuron activity, and (ii) concurrently amplify cue-evoked DA transients in response to alcohol-paired stimuli while blunting responses to natural rewards. A key mechanism is the weakening of BLA reward→BNST CRF+ projections and strengthening of BLA aversion→BNST CRF- projections, which drive a sharp decrease in DA tone and collapse of differentiation between BLA reward and aversion populations into a "mixed" state. In this regime, salient cues can evoke stochastic, exaggerated reward or aversion responses, providing a mechanism for maladaptive motivation, negative affect, and potential exacerbation of mood pathology in chronic AUD.

The basolateral amygdala (BLA) is central to emotional processing, fear learning, and memory. Dopamine (DA) significantly influences BLA function, yet its precise effects are not clear. We present a mathematical model exploring how DA modulation of BLA activity depends on the network's current state. Specifically, we model the firing rates of interconnected neural groups in the BLA and their responses to external stimuli and DA modulation. BLA projection neurons are separated into two groups according to their responses—fear and safety. These groups are connected by mutual inhibition though interneurons. We contrast 'differentiated' BLA states, where fear and safety projection neurons exhibit distinct activity levels, with 'non-differentiated' states. We posit that differentiated states support selective responses and short-term emotional memory. On the other hand, non-differentiated states represent either the case in which BLA is disengaged, or the activation of the fear and safety neurons is at a similar moderate or high level. We show that, while DA further disengages BLA in the low activity state, it destabilizes the moderate activity non-differentiated BLA state. We show that in the latter non-differentiated state the BLA is hypersensitive, and the polarity of its responses (fear or safety) to salient stimuli is highly random. We hypothesize that this non-differentiated state is related to anxiety and Post-Traumatic Stress Disorder (PTSD).

BACKGROUND:A connection between stress-related illnesses and alcohol use disorders is extensively documented. Fear conditioning is a standard procedure used to study stress learning and links it to the activation of amygdala circuitry. However, the connection between the changes in amygdala circuitry and function induced by alcohol and fear conditioning is not well established. METHODS:We introduce a computational model to test the mechanistic relationship between amygdala functional and circuit adaptations during fear conditioning and the impact of acute vs. repeated alcohol exposure. Using firing rate formalism, the model generates electrophysiological and behavioral responses in fear conditioning protocols via plasticity of amygdala inputs. The influence of alcohol is modeled by accounting for known modulation of connections within amygdala circuits, which consequently affect plasticity. Thus, the model connects the electrophysiological and behavioral experiments. We hypothesize that alterations within amygdala circuitry produced by alcohol cause abnormal plasticity of amygdala inputs such that fear extinction is slower to achieve and less robust. RESULTS:In accordance with prior experimental results, both acute and prior repeated alcohol decrease the speed and robustness of fear extinction in our simulations. The model predicts that, first, the delay in fear extinction caused by alcohol is mostly induced by greater activation of the basolateral amygdala (BLA) after fear acquisition due to alcohol-induced modulation of synaptic weights. Second, both acute and prior repeated alcohol shift the amygdala network away from the robust extinction regime by inhibiting activity in the central amygdala (CeA). Third, our model predicts that fear memories formed during acute or after chronic alcohol are more connected to the context. CONCLUSIONS:The model suggests how circuit changes induced by alcohol may affect fear behaviors and provides a framework for investigating the involvement of multiple neuromodulators in this neuroadaptive process.

Cortical and basal ganglia circuits play a crucial role in the formation of goal-directed and habitual behaviors. In this study, we investigate the cortico-striatal circuitry involved in learning and the role of this circuitry in the emergence of inflexible behaviors such as those observed in addiction. Specifically, we develop a computational model of cortico-striatal interactions that performs concurrent goal-directed and habit learning. The model accomplishes this by distinguishing learning processes in the dorsomedial striatum (DMS) that rely on reward prediction error signals as distinct from the dorsolateral striatum (DLS) where learning is supported by salience signals. These striatal subregions each operate on unique cortical input: the DMS receives input from the prefrontal cortex (PFC) which represents outcomes, and the DLS receives input from the premotor cortex which determines action selection. Following an initial learning of a two-alternative forced choice task, we subjected the model to reversal learning, reward devaluation, and learning a punished outcome. Behavior driven by stimulus-response associations in the DLS resisted goal-directed learning of new reward feedback rules despite devaluation or punishment, indicating the expression of habit. We repeated these simulations after the impairment of executive control, which was implemented as poor outcome representation in the PFC. The degraded executive control reduced the efficacy of goal-directed learning, and stimulus-response associations in the DLS were even more resistant to the learning of new reward feedback rules. In summary, this model describes how circuits of the dorsal striatum are dynamically engaged to control behavior and how the impairment of executive control by the PFC enhances inflexible behavior.

Pathology in neural circuits that control the expression of goal-directed and habitual behaviors is hypothesized to be a major contributing factor to addiction. In this study, we investigate cortico-striatal circuitry involved in learning and how cortical interactions with specific striatal subregions are involved in the emergence of inflexible behaviors such as compulsive drinking. Specifically, we develop a computational model of cortico-striatal interactions that performs concurrent goal-directed and stimulus-response learning. The model accomplishes learning by distinguishing between the dorsomedial striatum (DMS)—where dopamine release encodes reward prediction error—and the dorsolateral striatum (DLS)—where dopamine release encodes motivation or salience. These striatal subregions each operate on unique cortical input: the DMS receives input from the prefrontal cortex (PFC), which represented outcomes and the DLS receives input from the premotor cortex which determines action selection. Following an initial learning of a two-alternative forced choice task, we subjected the model to reversal learning, reward devaluation, and punishment learning. Behavior driven by stimulus-response associations in the DLS resisted goal-directed learning of new reward feedback rules despite devaluation or punishment, indicating the expression of habit. We repeated these simulations after the loss of executive control, which was implemented as poor outcome representation in the PFC. Following this manipulation, no detectable of reward devaluation was observed, however, the efficacy of goal-directed learning was reduced, and stimulus response associations in the DLS were even more resistant to the learning of new reward feedback rules. In summary, this model provides a mechanism that describes how the loss of executive control could contribute to the emergence of inflexible behavior. Introduction Advanced stages of addiction are hypothesized to coincide with the transition in the control of behavior from neural circuits that are optimized for flexible responding to those that are optimized for inflexible responding. It is thought that this transition involves the abnormal activation of neural circuitry in the basal ganglia that is devoted to habit and automaticity (Graybiel, 2008; Lipton et al., 2019). Molecular changes resulting from chronic substance abuse alter the role of this circuitry from enabling the performance of routine tasks without attention or cognitive load to driving compulsive drug seeking (Lüscher and Janak, 2021; Lüscher et al., 2020). Clearly articulating the computational processes that unfold across these circuits and how they are altered in addiction is critical for understanding this disease and identifying novel treatment strategies. Here, we focus on two structures within the basal ganglia—specifically, the dorsomedial striatum (DMS) and dorsolateral striatum (DLS)—that have been implicated in the pathophysiology of inflexible behavior and drug addiction (Corbit and Janak, 2016; Lipton et al., 2019). In an alcohol-seeking operant task, seeking behavior can be disrupted by inactivation of the DMS but is insensitive to inactivation of the DLS during early training (Corbit et al., 2012). After extensive training, the specificity of these manipulations becomes reversed: alcohol seeking is disrupted by inactivation of the DLS and is insensitive to inactivation of the DMS. Similarly, cocaine-seeking behavior is sensitive to disruption of the DMS during early learning and disruption of the DLS after extensive training (Murray et al., 2012). In summary, these partitions of the dorsal striatum have distinct temporal contributions to drug-seeking behavior where DMS is initially critical and eventually transitions to DLS. The distinct roles of the DMS and DLS in inflexible behavior are derived from differences in their computational properties. The DMS and DLS are respectively involved in goal-directed and habitual behavior (Schwabe and Wolf, 2011). Manipulations that destroy or disrupt the DMS and DLS during instrumental behavioral tasks reveal their respective roles in goal-directed learning and the formation of habit (Yin and Knowlton, 2006). The inactivation of the DMS decreases sensitivity to reward devaluation (Yin et al., 2005), and the destruction of the DLS increases sensitivity to reward devaluation and abolishes habitual seeking in the absence of a reward (Yin et al., 2004). There is evidence that it takes more time to engage learning mechanisms in the DLS; plasticity in the DLS occurs relatively slowly compared to DMS as training progresses (Yin et al., 2009). Differences in the computational function of the DMS and DLS are supported by differences in their synaptic plasticity mechanisms and differences in their cortical inputs. In the basal ganglia hypothesis for reward-based learning, action selection is gated at the striatum by dopamine-mediated synaptic plasticity of cortical projections to medium spiny neurons (Frank, 2005; Graybiel, 2008). In classical reinforcement learning, dopamine release encodes reward prediction error (RPE). However, different compartments of the striatum receive partition-specific nigro-striatal projections that encode different information (Lerner et al., 2015; Matsumoto and Hikosaka, 2009). Classical RPE-encoding nigrostriatal neurons project to the DMS, and salience-encoding neurons project to the DLS (Lerner et al., 2015). Moreover, dopamine release in the basal ganglia acts on cortico-striatal synapses, and cortical input to the striatum is topographically organized across the DMS and DLS. Inputs to the DS exhibit a clear topographic bias where the medial portion of the striatum is more likely to receive input from the prefrontal cortex (PFC), and the lateral portion more likely to receive input from somatosensory and motor regions (Hunnicutt et al., 2016; Pan et al., 2010; Peters et al., 2021). The input from PFC to the DMS is of particular interest. In rodents, the PFC is important for executive function and goal-directed learning (Barker et al., 2015; Hart et al., 2018a, 2018b; Kesner and Churchwell, 2011; Ostlund and Balleine, 2005; Tran-Tu-Yen et al., 2009). Anatomically comparable structures in humans and non-human primates are involved in cognitive and executive function as well as goal-directed behavior (Balleine and O’Doherty, 2010; Donahue and Lee, 2015; Kennerley et al., 2009; Laubach et al., 2018; Perry et al., 2011; Tsutsui et al., 2016). Critically, these regions are impaired following prolonged alcohol use (Schacht et al., 2013) and this impairment corresponds to increased responding to alcohol (Crews and Boettiger, 2009; Myrick et al., 2004). In recent work, we investigated how the computational properties of the PFC are altered in a rodent model of excessive alcohol consumption and neuronal activity in the medial PFC fails to appropriately code for intent to drink and seeking behavior (Linsenbardt et al., 2019; Timme et al., 2021). Taken in combination with observations about the DMS and DLS, we leverage these results to illustrate a hypothesis for how impairment in the PFC contributes to the emergence of inflexible behavior. Here we present a new computational model of cortico-striatal learning that incorporates goaldirected learning in the DMS and stimulus-response learning in the DLS. We derive this implementation from a theory of reinforcement learning based on dopamine-mediated plasticity of cortico-striatal projections to medium spiny neurons (MSNs) (Graybiel, 2008). Dopamine induces long-term potentiation and long-term depression in D1 and D2 receptor expressing MSNs respectively, and these changes are hypothesized to configure the basal ganglia to selectively disinhibit thalamocortical relay neurons in the context of ongoing behavioral tasks (Frank, 2005). This hypothesis has been incorporated in computational models that include reward-based learning that is based on RPE in the striatum (Frank, 2005; Kim et al., 2017; Mulcahy et al., 2020). However, stimulus-response or habit was previously implemented as a consequence of Hebbian learning in cortico-cortical projections (Kim et al., 2017; Mulcahy et al., 2020). Recent publications have investigated contemporaneous goal-directed and habitual learning in a model of reinforcement learning (Miller et al., 2019) and the consequences of the spatial distribution of dopamine release in the medial-lateral axis of the dorsal stratum (Hamid et al., 2021). To understand the mechanism of how behaviors transition from goal-directed to habitual, a computational model is required that is capable of articulating how changes in cortico-striatal plasticity support both goal-directed learning in the DMS and stimulus-response learning in the DLS, which we provide here for the first time. In the present study, we simulate two-alternative forced choice behavioral tasks to investigate the interaction of the DMS and the DLS. Following an initial learning session, we challenge the model with reward devaluation, reward reversal, and punishment learning. These different behavioral tasks are implemented by manipulations to the magnitude, action contingency, and valence of the reward feedback. We challenge the model again in scenarios characterized by the loss of executive control. In these simulations, neural activity of the PFC fails to appropriately code for action-selection. Our results demonstrate how the loss of executive function could reduce the efficacy of goal-directed learning and emphasize the expression of stimulus-response or habitual behavior. Results Organization of cortico-striatal partitions. In this study, we extend our previous model of the basal ganglia to implement a neural network that combines learning in the DMS and learning in the DLS to perform a two-alternative forced-choice decision-making task (Fig. 1A). This new model implements the pattern of choice-specific channels for neuronal circu

Acute ethanol (EtOH) intoxication results in several maladaptive behaviors that may be attributable, in part, to the effects of EtOH on neural activity in medial prefrontal cortex (mPFC). The acute effects of EtOH on mPFC function have been largely described as inhibitory. However, translating these observations on function into a mechanism capable of delineating acute EtOH's effects on behavior has proven difficult. This review highlights the role of acute EtOH on electrophysiological measurements of mPFC function and proposes that interpreting these changes through the lens of dynamical systems theory is critical to understand the mechanisms that mediate the effects of EtOH intoxication on behavior. Specifically, the present review posits that the effects of EtOH on mPFC N-methyl-d-aspartate (NMDA) receptors are critical for the expression of impaired behavior following EtOH consumption. This hypothesis is based on the observation that recurrent activity in cortical networks is supported by NMDA receptors, and, when disrupted, may lead to impairments in cognitive function. To evaluate this hypothesis, we discuss the representation of mPFC neural activity in low-dimensional, dynamic state spaces. This approach has proven useful for identifying the underlying computations necessary for the production of behavior. Ultimately, we hypothesize that EtOH-related alterations to NMDA receptor function produces alterations that can be effectively conceptualized as impairments in attractor dynamics and provides insight into how acute EtOH disrupts forms of cognition that rely on mPFC function. This article is part of the special Issue on 'Neurocircuitry Modulating Drug and Alcohol Abuse'.

The addictive component of tobacco, nicotine, acts via nicotinic acetylcholine receptors (nAChRs). The β2 subunit-containing nAChRs (β2-nAChRs) play a crucial role in the rewarding properties of nicotine and are particularly densely expressed in the mesolimbic dopamine (DA) system. Specifically, nAChRs directly and indirectly affect DA neurons in the ventral tegmental area (VTA). The understanding of ACh and nicotinic regulation of DA neuron activity is incomplete. By computational modeling, we provide mechanisms for several apparently contradictory experimental results. First, systemic knockout of β2-containing nAChRs drastically reduces DA neurons bursting, although the major glutamatergic (Glu) afferents that have been shown to evoke this bursting stay intact. Second, the most intuitive way to rescue this bursting-by re-expressing the nAChRs on VTA DA neurons-fails. Third, nAChR re-expression on VTA GABA neurons rescues bursting in DA neurons and increases their firing rate under the influence of ACh input, whereas nicotinic application results in the opposite changes in firing. Our model shows that, first, without ACh receptors, Glu excitation of VTA DA and GABA neurons remains balanced and GABA inhibition cancels the direct excitation. Second, re-expression of ACh receptors on DA neurons provides an input that impedes membrane repolarization and is ineffective in restoring firing of DA neurons. Third, the distinct responses to ACh and nicotine occur because of distinct temporal patterns of these inputs: pulsatile versus continuous. Altogether, this study highlights how β2-nAChRs influence coactivation of the VTA DA and GABA neurons required for motivation and saliency signals carried by DA neuron activity.

The basal ganglia (BG) is a collection of nuclei located deep beneath the cerebral cortex that is involved in learning and selection of rewarded actions. Here, we analyzed BG mechanisms that enable these functions. We implemented a rate model of a BG-thalamo-cortical loop and simulated its performance in a standard action selection task. We have shown that potentiation of corticostriatal synapses enables learning of a rewarded option. However, these synapses became redundant later as direct connections between prefrontal and premotor cortices (PFC-PMC) were potentiated by Hebbian learning. After we switched the reward to the previously unrewarded option (reversal), the BG was again responsible for switching to the new option. Due to the potentiated direct cortical connections, the system was biased to the previously rewarded choice, and establishing the new choice required a greater number of trials. Guided by physiological research, we then modified our model to reproduce pathological states of mild Parkinson's and Huntington's diseases. We found that in the Parkinsonian state PMC activity levels become extremely variable, which is caused by oscillations arising in the BG-thalamo-cortical loop. The model reproduced severe impairment of learning and predicted that this is caused by these oscillations as well as a reduced reward prediction signal. In the Huntington state, the potentiation of the PFC-PMC connections produced better learning, but altered BG output disrupted expression of the rewarded choices. This resulted in random switching between rewarded and unrewarded choices resembling an exploratory phase that never ended. Along with other computational studies, our results further reconcile the apparent contradiction between the critical involvement of the BG in execution of previously learned actions and yet no impairment of these actions after BG output is ablated by lesions or deep brain stimulation. We predict that the cortico-BG-thalamo-cortical loop conforms to previously learned choice in healthy conditions, but impedes those choices in disease states.

The article deals with the stages of solving image clustering problems using pre-trained neural networks. Some composite solutions of the clustering problem are presented, where clustering methods are used at the last stage, and most of the work is the extraction of features and their preprocessing. The analysis of modern approaches to feature extraction from images, including classical methods of pattern recognition theory and computer vision and feature extraction methods using convolutional neural networks. The paper provides recommendations for choosing the most effective architecture for convolutional neural networks, depending on the nature of the tasks. A classification of methods for reducing the dimension of images by types: preserving the distance between points when displaying from high-dimensional to low-dimensional space; preserving the global structure of data; search for the nearest vectors in large-dimensional spaces. We consider a special method of clustering images DeepCluster, which iteratively groups the features using a standard clustering algorithm. The obtained results can serve in further research in this area, as well as in solving problems in the preparation of pre-trained models of convolutional neural networks.

This paper examines the economic efficiency of Russian special economic zones (SEZs) established by federal authorities since 2005. The results are mixed: the payback of SEZs is low, but they continue to attract residents; SEZs have greater attractiveness for foreign investment, but their sectoral structure is fundamentally no better than the country-wide structure; SEZs’ enterprises have higher labour productivity than the country, but mainly owing to their recent creation. The common bottlenecks of SEZ development are the instability of legislation on SEZs, the low level of federal authorities’ activity in SEZ development before the economic crisis, competition with other preferential regimes for investors and the long period of searching for the optimal system of SEZ management. Differences in the efficiency of particular SEZs are explained by the peculiarities of the territories where SEZs are established. SEZs are successful if they are created on sites that enjoy a favourable geographic position and in regions that have advanced levels of industrial development.

The article shows proposed features of the regional policy of the European Union after 2020. In mid-2018 the EU outlined the main parameters of the Multiannual Financial Framework for 2021-2027. Revision of EU policies traditionally takes place with the adoption of such financial frameworks, and the regional policy (EU Cohesion Policy) is not an exception. The paper deals with the stated changes in the EU Cohesion Policy and new features of the distribution of funding for its needs. Based on the analysis of earlier reforms of the EU regional policy, the conclusion is made about cosmetic changes in the Cohesion Policy after 2020. In fact, as at the beginning of the current decade, when the parameters of the Multiannual Financial Framework for 2014-2020 were determined, the EU preferred minor adjustments, which are presented as significant improvements. Thus, Brexit did not become, like the crisis in the Euro area, an occasion for radical changes in the Cohesion policy designed to respond to the new challenges facing the European integration project. This once again calls into question the EU's ability to promote, through its Cohesion Policy, the competitiveness of European economies and the resolution of acute social problems.

A large body of data has identified numerous molecular targets through which ethanol (EtOH) acts on brain circuits. Yet how these multiple mechanisms interact to result in dysregulated dopamine (DA) release under the influence of alcohol in vivo remains unclear. In this manuscript, we delineate potential circuit-level mechanisms responsible for EtOH-dependent dysregulation of DA release from the ventral tegmental area (VTA) into its projection areas. For this purpose, we constructed a circuit model of the VTA that integrates realistic Glutamatergic (Glu) inputs and reproduces DA release observed experimentally. We modelled the concentration-dependent effects of EtOH on its principal VTA targets. We calibrated the model to reproduce the inverted U-shape dose dependence of DA neuron activity on EtOH concentration. The model suggests a primary role of EtOH-induced boost in the I-h and AMPA currents in the DA firing-rate/bursting increase. This is counteracted by potentiated GABA transmission that decreases DA neuron activity at higher EtOH concentrations. Thus, the model connects well-established in vitro pharmacological EtOH targets with its in vivo influence on neuronal activity. Furthermore, we predict that increases in VTA activity produced by moderate EtOH doses require partial synchrony and relatively low rates of the Glu afferents. We propose that the increased frequency of transient (phasic) DA peaks evoked by EtOH results from synchronous population bursts in VTA DA neurons. Our model predicts that the impact of acute ETOH on dopamine release is critically shaped by the structure of the cortical inputs to the VTA.

Alcoholism is the third leading cause of preventable mortality in the world. In the last decades a large body of experimental data has paved the way to a clearer knowledge of the specific molecular targets through which ethanol (EtOH) acts on brain circuits. Yet how these multiple mechanisms play together to result in a dysregulated dopamine (DA) release under alcohol influence remains unclear. In this manuscript, we delineate potential circuit-level mechanisms responsible for EtOH-dependent increase and dysregulation of DA release from the ventral tegmental area (VTA) into nucleus accumbens (Nac). For this purpose, we build a circuit model of the VTA composed of DA and GABAergic neurons, that integrate external Glutamatergic (Glu) inputs to result in DA release. In particular, we reproduced a non-monotonic dose dependence of DA neurons firing activity on EtOH: an increase in firing at small to intermediate doses and a drop below baseline (alcohol-free) levels at high EtOH concentrations. Our simulations predict that a certain level of synchrony is necessary for the firing rate increase produced by EtOH. Moreover, EtOH effect on the DA neuron firing rate and, consequently, DA release can reverse depending on the average activity level of the Glu afferents to VTA. Further, we propose a mechanism for emergence of transient (phasic) DA peaks and the increase in their frequency in EtOH. Phasic DA transients result from DA neuron population bursts, and these bursts are enhanced in EtOH. These results suggest the role of synchrony and average activity level of Glu afferents to VTA in shaping the phasic and tonic DA release under the acute influence of EtOH and in normal conditions.

Russian Abstract: Коллективная монография подготовлена по Программе комплексных фундаментальных научных исследований Отделения глобальных проблем и международных отношений РАН №III.10П «Дисбалансы современного миропорядка и Россия» (проект «Экономические и социальные дисбалансы в макрорегионах современного мира»). В первой части книги исследуются концептуальные вопросы регулирования инфляции. Анализируются наиболее важные аспекты соотношения динамики цен и дисбалансов в процессе воспроизводства, такие как экономический рост и инфляция, структурные проблемы инфляции, современные ценовые тренды на мировых товарных рынках, экспорт и импорт инфляции в открытых экономиках и т.д. Рассматриваются специфические особенности инфляционных процессов и государственной политики воздействия на динамику цен в отдельных странах и регионах мира – США, европейских государствах, Китае, Японии, Индии, Республике Корея, Аргентине, странах Африки южнее Сахары и странах Ближнего Востока и Северной Африки. Завершается монография изучением своеобразия инфляционных проблем в России.English Abstract: The book is supported by the Russian Academy of Sciences Program of fundamental research "The Imbalances of the Modern World Order and Russia" (project "The Economic and Social Disparities in the Regions of the Modern World"). Conceptual issues of governing inflation are studied in the first part of the book. Then investigated are the pivotal aspects of interrelationship between price dynamics and imbalances of the reproduction process, such as economic growth and inflation, structural causes of inflation, current price trends on the world commodity markets, export and import of inflation into open economies, etc. The country-specific inflation process as well as government policy impact on prices are analyzed for selected countries and regions. The United States, European countries, China, Japan, India, South Korea, Argentina, Sub-Saharan Africa, the Middle East and North Africa are taken as case studies. The book is concluded with a study of the origins of inflation in the Russian economy.

Dopaminergic (DA) neurons display two modes of firing: low-frequency tonic and high-frequency bursts. The high frequency firing within the bursts is attributed to NMDA, but not AMPA receptor activation. In our models of the DA neuron, both biophysical and abstract, the NMDA receptor current can significantly increase their firing frequency, whereas the AMPA receptor current is not able to evoke high-frequency activity and usually suppresses firing. However, both currents are produced by glutamate receptors and, consequently, are often co-activated. Here we consider combined influence of AMPA and NMDA synaptic input in the models of the DA neuron. Different types of neuronal activity (resting state, low frequency, or high frequency firing) are observed depending on the conductance of the AMPAR and NMDAR currents. In two models, biophysical and reduced, we show that the firing frequency increases more effectively if both receptors are co-activated for certain parameter values. In particular, in the more quantitative biophysical model, the maximal frequency is 40% greater than that with NMDAR alone. The dynamical mechanism of such frequency growth is explained in the framework of phase space evolution using the reduced model. In short, both the AMPAR and NMDAR currents flatten the voltage nullcline, providing the frequency increase, whereas only NMDA prevents complete unfolding of the nullcline, providing robust firing. Thus, we confirm a major role of the NMDAR in generating high-frequency firing and conclude that AMPAR activation further significantly increases the frequency.