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Sensory adaptation is the process whereby brain circuits adjust neuronal activity in response to redundant sensory stimuli. Although sensory adaptation has been extensively studied for individual neurons on timescales of tens of milliseconds to a few seconds, little is known about it over longer timescales or at the population level. We investigated population-level adaptation in the barrel field of the mouse somatosensory cortex (S1BF) using in vivo two-photon calcium imaging and Neuropixels recordings in awake mice. Among stimulus-responsive neurons, we found both adapting and facilitating neurons, which decreased or increased their firing, respectively, with repetitive whisker stimulation. The former outnumbered the latter by 2:1 in layers 2/3 and 4; hence, the overall population response of mouse S1BF was slightly adapting. We also discovered that population adaptation to one stimulus frequency (5 Hz) does not necessarily generalize to a different frequency (12.5 Hz). Moreover, responses of individual neurons to repeated rounds of stimulation over tens of minutes were strikingly heterogeneous and stochastic, such that their adapting or facilitating response profiles were not stable across time. Such representational drift was particularly striking when recording longitudinally across 8-9 days, as adaptation profiles of most whisker-responsive neurons changed drastically from one day to the next. Remarkably, repeated exposure to a familiar stimulus paradoxically shifted the population away from strong adaptation and toward facilitation. Thus, the adapting vs. facilitating response profile of S1BF neurons is not a fixed property of neurons but rather a highly dynamic feature that is shaped by sensory experience across days.

Changes in the function of inhibitory interneurons (INs) during cortical development could contribute to the pathophysiology of neurodevelopmental disorders. Using all-optical in vivo approaches, we find that parvalbumin (PV) INs and their immature precursors are hypoactive and transiently decoupled from excitatory neurons in postnatal mouse somatosensory cortex (S1) of Fmr1 KO mice, a model of fragile X syndrome (FXS). This leads to a loss of parvalbumin INs (PV-INs) in both mice and humans with FXS. Increasing the activity of future PV-INs in neonatal Fmr1 KO mice restores PV-IN density and ameliorates transcriptional dysregulation in S1, but not circuit dysfunction. Critically, administering an allosteric modulator of Kv3.1 channels after the S1 critical period does rescue circuit dynamics and tactile defensiveness. Symptoms in FXS and related disorders could be mitigated by targeting PV-INs.

Working memory (WM) and timing are generally considered distinct cognitive functions, but similar neural signatures have been implicated in both. To explore the hypothesis that WM and timing may rely on shared neural mechanisms, we used psychophysical tasks that contained either task-irrelevant timing or WM components. In both cases, the task-irrelevant component influenced performance. We then developed recurrent neural network (RNN) simulations that revealed that cue-specific neural sequences, which multiplexed WM and time, emerged as the dominant regime that captured the behavioural findings. During training, RNN dynamics transitioned from low-dimensional ramps to high-dimensional neural sequences, and depending on task requirements, steady-state or ramping activity was also observed. Analysis of RNN structure revealed that neural sequences relied primarily on inhibitory connections, and could survive the deletion of all excitatory-to-excitatory connections. Our results indicate that in some instances WM is encoded in time-varying neural activity because of the importance of predicting when WM will be used.

Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits, neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles in ex vivo cortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and nonstimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and nonstimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits and, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations.SIGNIFICANCE STATEMENT The connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used an ex vivo approach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms of their correlated activity and functional connectivity. The absence of feedback between different brain areas and full control of external input allowed for a tractable system to study the underlying mechanisms and development of a computational model. Results show that differential stimulation of subpopulations of neurons significantly reshapes cortical circuits and forms subnetworks referred to as neuronal ensembles.

Self-sustained neural activity maintained through local recurrent connections is of fundamental importance to cortical function. Converging theoretical and experimental evidence indicates that cortical circuits generating self-sustained dynamics operate in an inhibition-stabilized regime. Theoretical work has established that four sets of weights (WE←E, WE←I, WI←E, and WI←I) must obey specific relationships to produce inhibition-stabilized dynamics, but it is not known how the brain can appropriately set the values of all four weight classes in an unsupervised manner to be in the inhibition-stabilized regime. We prove that standard homeostatic plasticity rules are generally unable to generate inhibition-stabilized dynamics and that their instability is caused by a signature property of inhibition-stabilized networks: the paradoxical effect. In contrast, we show that a family of "cross-homeostatic" rules overcome the paradoxical effect and robustly lead to the emergence of stable dynamics. This work provides a model of how-beginning from a silent network-self-sustained inhibition-stabilized dynamics can emerge from learning rules governing all four synaptic weight classes in an orchestrated manner.

This editorial discusses the contributions of the papers belonging to the virtual special issue (VSI) of Energy Reports dedicated to 16th Sustainable Development of Energy, Water and Environment Systems (SDEWES) Conference held in Dubrovnik on 10–15 October, 2021. Original research articles presented at the SDEWES Conference relevant to the Journal are included in this VSI for a total of 27 accepted papers among those invited by the Guest Editors. Accepted papers deal with key research topic related to the scope of the conference, such as energy, water, and environmental systems, meeting the aim and scope of Energy Reports. The main topics of this VSI cover innovative and renewable energy technologies, building-plant design solutions and simulation tools for the early design of sustainable buildings and communities, energy management strategies, clean fuels and energy conservation measures. The latest advancements and challenges to accelerate the mitigation of climate change towards a net zero energy economy are discussed in detail. The VSI collects the most relevant contributions of researchers committed to develop novel energy solutions and multidisciplinary approaches towards the sustainable development, crucial to ensure a just energy transition and, simultaneously, to reduce sources of conflict and iniquity typical of geopolitical tensions. The final goal of the effort of academia, industry and institution is to support the sustainable development towards a cleaner environment, energy security, and social equity.To this goal, the Sustainable Development of Energy, Water and Environment Systems (SDEWES) Conference represents a platform for the development of inter-sectoral collaborations among scientists and stakeholders. The SDEWES Conference brought together around 630 scientists, researchers, and experts in the field of sustainable development from 58 Countries. According to the papers published in the VSI of Energy Reports dedicated to 16th SDEWES Conference, the editorial is split in several sections based on the relevant topics dealt by the VSI paper, such as: energy efficiency in buildings: measures and methods, solar based technologies for building applications, development and use of building energy performance simulation tools, energy communities and urban modelling, clean fuels and sustainable use of natural resources and energy saving practices.

The building sector is responsible for the highest portion of energy consumption and carbon dioxide emissions. To partly address this issue, the scientific community is placing greater emphasis on the utilization of Building Integrated Solar Systems (BISS) as a means of improving building thermophysics and decreasing primary energy demand. Within this framework, Integrated Collector Storage Solar Water Heaters (ICSSWH) have the potential to significantly decrease the costs associated with the installation and maintenance of building-integrated solar systems (BISS), all while optimizing the utilization of solar power. Nevertheless, ICSSWHs exhibit certain limitations in their capacity to retain thermal energy, particularly during nocturnal periods. One potential approach for enhancing the efficiency of ICSSWH collectors involves the integration of Thermal Diodes (TD), which have the capability to optimize heat absorption while minimizing heat retention. In this context, to advance the state-of-the-art in the field, this paper presents a novel dynamic simulation tool conceived to assess the performance of an innovative Hybrid Photovoltaic Thermal ICSSWH (the HyPVT), incorporating a cutting-edge Planar Liquid Vapour Thermal Diode (PLVTD), and purposely designed to be integrated into buildings’ envelope. By using the developed simulation tool, the performance of the proposed HyPVT collector concept is investigated with the aim of assessing its collection and retention efficiencies under different boundary and operating conditions. Furthermore, to show the effect of the Planar Liquid Vapour Thermal Diode (PLVTD) adoption, a comparative analysis is conducted between the proposed ICSSWH and a conventional one (without a thermal diode). Finally, the developed tool will be utilized in forthcoming research to determine the optimal device configuration through the utilization of a model-based design methodology.

Up states are the best studied example of an emergent neural dynamic regime. Computational models based on a single class of inhibitory neurons indicate that Up states reflect bistable dynamic systems in which positive feedback is stabilized by strong inhibition and predict a paradoxical effect in which increased drive to inhibitory neurons results in decreased inhibitory activity. To date, however, computational models have not incorporated empirically defined properties of parvalbumin (PV) and somatostatin (SST) neurons. Here we first experimentally characterized the frequency-current (F-I) curves of pyramidal (Pyr), PV, and SST neurons from mice of either sex, and confirmed a sharp difference between the threshold and slopes of PV and SST neurons. The empirically defined F-I curves were incorporated into a three-population computational model that simulated the empirically derived firing rates of pyramidal, PV, and SST neurons. Simulations revealed that the intrinsic properties were sufficient to predict that PV neurons are primarily responsible for generating the nontrivial fixed points representing Up states. Simulations and analytical methods demonstrated that while the paradoxical effect is not obligatory in a model with two classes of inhibitory neurons, it is present in most regimes. Finally, experimental tests validated predictions of the model that the Pyr ↔ PV inhibitory loop is stronger than the Pyr ↔ SST loop.SIGNIFICANCE STATEMENT Many cortical computations, such as working memory, rely on the local recurrent excitatory connections that define cortical circuit motifs. Up states are among the best studied examples of neural dynamic regimes that rely on recurrent excitatory excitation. However, this positive feedback must be held in check by inhibition. To address the relative contribution of PV and SST neurons, we characterized the intrinsic input-output differences between these classes of inhibitory neurons and, using experimental and theoretical methods, show that the higher threshold and gain of PV leads to a dominant role in network stabilization.

One of the most important factors in decision-making is estimating the value of available options. Subregions of the prefrontal cortex, including the orbitofrontal cortex (OFC), have been deemed essential for this process. Value computations require a complex integration across numerous dimensions, including, reward magnitude, effort, internal state, and time. The importance of the temporal dimension is well illustrated by temporal discounting tasks, in which subjects select between smaller-sooner versus larger-later rewards. The specific role of OFC in telling time and integrating temporal information into decision-making remains unclear. Based on the current literature, in this review we reevaluate current theories of OFC function, accounting for the influence of time. Incorporating temporal information into value estimation and decision-making requires distinct, yet interrelated, forms of temporal information including the ability to tell time, represent time, create temporal expectations, and the ability to use this information for optimal decision-making in a wide range of tasks, including temporal discounting and wagering. We use the term "temporal cognition" to refer to the integrated use of these different aspects of temporal information. We suggest that the OFC may be a critical site for the integration of reward magnitude and delay, and thus important for temporal cognition. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Cortical responses to sensory stimuli are strongly modulated by temporal context. One of the best studied examples of such modulation is sensory adaptation. We first show that in response to repeated tones pyramidal (Pyr) neurons in male mouse auditory cortex (A1) exhibit facilitating and stable responses, in addition to adapting responses. To examine the potential mechanisms underlying these distinct temporal profiles, we developed a reduced spiking model of sensory cortical circuits that incorporated the signature short-term synaptic plasticity (STP) profiles of the inhibitory parvalbumin (PV) and somatostatin (SST) interneurons. The model accounted for all three temporal response profiles as the result of dynamic changes in excitatory/inhibitory balance produced by STP, primarily through shifts in the relative latency of Pyr and inhibitory neurons. Transition between the three response profiles was possible by changing the strength of the inhibitory PV→Pyr and SST→Pyr synapses. The model predicted that a unit's latency would be related to its temporal profile. Consistent with this prediction, the latency of stable units was significantly shorter than that of adapting and facilitating units. Furthermore, because of the history-dependence of STP the model generated a paradoxical prediction: that inactivation of inhibitory neurons during one tone would decrease the response of A1 neurons to a subsequent tone. Indeed, we observed that optogenetic inactivation of PV neurons during one tone counterintuitively decreased the spiking of Pyr neurons to a subsequent tone 400 ms later. These results provide evidence that STP is critical to temporal context-dependent responses in the sensory cortex.SIGNIFICANCE STATEMENT Our perception of speech and music depends strongly on temporal context, i.e., the significance of a stimulus depends on the preceding stimuli. Complementary neural mechanisms are needed to sometimes ignore repetitive stimuli (e.g., the tic of a clock) or detect meaningful repetition (e.g., consecutive tones in Morse code). We modeled a neural circuit that accounts for diverse experimentally-observed response profiles in auditory cortex (A1) neurons, based on known forms of short-term synaptic plasticity (STP). Whether the simulated circuit reduced, maintained, or enhanced its response to repeated tones depended on the relative dominance of two different types of inhibitory cells. The model made novel predictions that were experimentally validated. Results define an important role for STP in temporal context-dependent perception.

Converging evidence suggests that the brain encodes time through dynamically changing patterns of neural activity, including neural sequences, ramping activity, and complex spatiotemporal dynamics. However, the potential computational significance and advantage of these different regimes have remained unaddressed. We combined large-scale recordings and modeling to compare population dynamics between premotor cortex and striatum in mice performing a two-interval timing task. Conventional decoders revealed that the dynamics within each area encoded time equally well; however, the dynamics in striatum exhibited a higher degree of sequentiality. Analysis of premotor and striatal dynamics, together with a large set of simulated prototypical dynamical regimes, revealed that regimes with higher sequentiality allowed a biologically constrained artificial downstream network to better read out time. These results suggest that, although different strategies exist for encoding time in the brain, neural sequences represent an ideal and flexible dynamical regime for enabling downstream areas to read out this information.

Fragile X syndrome is a neurodevelopmental disorder associated with a broad range of neural phenotypes. Interpreting these findings has proven challenging because some phenotypes may reflect compensatory mechanisms or normal forms of plasticity differentially engaged by experiential differences. To help minimize compensatory and experiential influences, we used an ex vivo approach to study network dynamics and plasticity of cortical microcircuits. In Fmr1-/y circuits, the spatiotemporal structure of Up-states was less reproducible, suggesting alterations in the plasticity mechanisms governing network activity. Chronic optical stimulation revealed normal homeostatic plasticity of Up-states, however, Fmr1-/y circuits exhibited abnormal experience-dependent plasticity as they did not adapt to chronically presented temporal patterns in an interval-specific manner. These results, suggest that while homeostatic plasticity is normal, Fmr1-/y circuits exhibit deficits in the ability to orchestrate multiple forms of synaptic plasticity and to adapt to sensory patterns in an experience-dependent manner-which is likely to contribute to learning deficits.

In this paper the implementation and application of a novel methodology for the estimation of the energy demand of the railway building stock is presented. To this aim, a bottom-up modelling approach implemented in a simulation tool is developed to assess the energy footprint and potential savings of railway buildings. The tool is intended to support operators and decision-makers in the planning of systematic energy retrofit necessary to up to date the railway infrastructure.The developed methodology is applied to the Italian railway building stock with a bottom-up approach, identifying several groups of similar stations (archetypes) that are clustered according to real data collected. Afterwards, a data-driven model is derived from the detailed dynamic simulations of physic-based models representing the whole building heritage. As a demonstration of the validity of the proposed methodology and its capability to be exploited in real applications, some energy-saving strategies are simulated, and a comprehensive analysis is conducted on the considered stations.The surrogate data-driven model shows R2 coefficients always above 0.93 compared to physic-based model in predicting heating, cooling and electricity demand. Depending on the size of the stations, the mean relative error is in the range 5.9–15.0%. Furthermore, the surrogate model turns out to be an easy-to-use tool to analyse retrofit scenarios and take informed decisions, while the methodology is easily extensible and scalable to other contexts.As demonstrated, the most impactful measure among the ones investigated is the adoption of high-performance lighting systems which entail an overall primary energy saving up to 26%, with very low pay back periods (∼1 year).

This editorial discusses the contributions of the papers belonging to the virtual special issue (VSI) of Energy Reports dedicated to the series of four SDEWES Conferences on Sustainable Development of Energy, Water and Environment Systems held in Cologne (Germany), Sarajevo (Bosnia and Herzegovina), Gold Coast (Australia) and Buenos Aires (Argentina) in 2020. High quality review papers and original research articles presented at the SDEWES Conferences relevant to the Journal are included in this VSI for a total of 20 accepted articles invited by the Guest Editors. Published papers deal with key research topic related to clean energies, advance energy technologies, building-plant design solutions, energy management measures, policies and regulations capable to ensure that all countries and regions develop and implement expertise and technologies to tackle climate change and make progress towards a net zero energy economy. Such a complex goal poses some major challenges and calls for the effort of scientists and stakeholders to commit to develop and carry out multidisciplinary approaches towards the sustainable development of modern cities for a decarbonized economy. This process is also crucial for achieving a successful and just energy transition, necessary to reduce sources of conflict and iniquity typical of geopolitical tensions, to avoid causes of uncertainty in the long run to the sustainable development Agenda, towards energy security, environmental protection, and the affordability of energy.In this framework, the Sustainable Development of Energy, Water and Environment Systems Series Conference represents a platform for the development of inter-sectoral collaborations among scientists and stakeholders and it promotes the development and improvements of advanced energy technologies and systems for the sustainable and just transition. The SDEWES Conferences brought together around 1000 researchers from all over the world working in the field of the sustainable development. According to the papers published in the VSI of Energy Reports dedicated to SDEWES Conferences, the editorial is subdivided in several sections based on the relevant topics on which the VSI papers are focused: renewable technologies for the energy efficiency of buildings and communities, advanced technologies and tools for the energy efficient design of buildings and systems, challenges and opportunities of policies and regulations to support the energy transition and meet the sustainable development targets.

Introduction: Despite the rising availability and use of medical marijuana (MM) in the USA, little is known about the demographics, clinical characteristics, or quality of life of MM patients. This study describes the demographic characteristics and health-related quality of life (HRQoL) of MM patients who are initiating treatment in Pennsylvania. Methods: Two-hundred adults naive to MM and referred for any of the 23 state-approved qualifying conditions were recruited at three MM dispensaries in Pennsylvania between September 2020 and March 2021. All participants consented to the study; completed semi-structured interviews that included demographic questionnaires, the Short Form-36 (SF-36), and Generalized Anxiety Disorder-7 (GAD-7); provided height and weight measurements; and allowed access their dispensary medical records. Results: Participants had a mean age of 48.5 ± 15.6 years, predominantly identified as female (67.5%), and were most commonly referred for chronic pain (63.5%) and/or anxiety (58.5%). Additionally, 46.0% were living with obesity as determined by BMI. Relative to a normative sample, participants reported diminished HRQoL in several domains, most notably in role limitations due to physical health (M = 46.0 ± 42.0), role limitations due to emotional problems (M = 52.5 ± 42.3), energy and fatigue (M = 39.8 ± 20.2), and pain (M = 49.4 ± 26.0). Discussion/Conclusion: Patients initiating MM treatment experienced low HRQoL in multiple domains. Future studies could evaluate the relationship between HRQoL and patients’ decisions to pursue MM treatment, as well as changes in HRQoL with MM use over time.

The ability to detect time intervals and temporal patterns is critical to some of the most fundamental computations the brain performs, including the ability to communicate and appraise a dynamically changing environment. Many of these computations take place on the scale of tens to hundreds of milliseconds. Electrophysiological evidence shows that some neurons respond selectively to duration, interval, rate, or order. Because the time constants of many time-varying neural and synaptic properties, including short-term synaptic plasticity (STP), are also in the range of tens to hundreds of milliseconds, they are strong candidates to underlie the formation of temporally selective neurons. Neurophysiological studies indicate that STP is indeed one of the mechanisms that contributes to temporal selectivity, and computational models demonstrate that neurons embedded in local microcircuits exhibit temporal selectivity if their synapses undergo STP. Converging evidence suggests that some forms of temporal selectivity emerge from the dynamic changes in the balance of excitation and inhibition imposed by STP.

Introduction: Despite the rising availability and use of medical marijuana (MM) in the USA, little is known about the demographics, clinical characteristics, or quality of life of MM patients. This study describes the demographic characteristics and health-related quality of life (HRQoL) of MM patients who are initiating treatment in Pennsylvania. Methods: Two-hundred adults naive to MM and referred for any of the 23 state-approved qualifying conditions were recruited at three MM dispensaries in Pennsylvania between September 2020 and March 2021. All participants consented to the study; completed semi-structured interviews that included demographic questionnaires, the Short Form-36 (SF-36), and Generalized Anxiety Disorder-7 (GAD-7); provided height and weight measurements; and allowed access their dispensary medical records. Results: Participants had a mean age of 48.5 ± 15.6 years, predominantly identified as female (67.5%), and were most commonly referred for chronic pain (63.5%) and/or anxiety (58.5%). Additionally, 46.0% were living with obesity as determined by BMI. Relative to a normative sample, participants reported diminished HRQoL in several domains, most notably in role limitations due to physical health (M = 46.0 ± 42.0), role limitations due to emotional problems (M = 52.5 ± 42.3), energy and fatigue (M = 39.8 ± 20.2), and pain (M = 49.4 ± 26.0). Discussion/Conclusion: Patients initiating MM treatment experienced low HRQoL in multiple domains. Future studies could evaluate the relationship between HRQoL and patients' decisions to pursue MM treatment, as well as changes in HRQoL with MM use over time. Keywords: Medical marijuana, Health-related quality of life, Clinical characteristics, Author(s): Lydia S. Buonomano [a]; Matthew M. Mitnick [a]; Thomas R. McCalmont [a]; Paulina Syracuse [a]; Karen L. Dugosh [b]; David S. Festinger [a]; Michelle R. Lent [a] Introduction Medical [...]