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We use a globally consistent, time-resolved data set of CO2 emission proxies to quantify urban CO2 emissions in 91 cities. We decompose emission trends into contributions from changes in urban extent, population density and per capita emission. We find that urban CO2 emissions are increasing everywhere but that the dominant contributors differ according to development level. A cluster analysis of factors shows that developing countries were dominated by cities with the rapid area and per capita CO2 emissions increases. Cities in the developed world, by contrast, show slow area and per capita CO2 emissions growth. China is an important intermediate case with rapid urban area growth combined with slower per capita CO2 emissions growth. Urban per capita emissions are often lower than their national average for many developed countries, suggesting that urbanisation may reduce overall emissions. However, trends in per capita urban emissions are higher than their national equivalent almost everywhere, suggesting that urbanisation will become a more serious problem in the future. An important exception is China,whose per capita urban emissions are growing more slowly than the national value. We also see a negative correlation between trends in population density and per capita CO2 emissions, highlighting a strong role for densification as a tool to reduce CO2 emissions.

The basal ganglia are a group of sub-cortical structures believed to play a critical role in action selection and sequencing. The striatum is the largest input structure of the basal ganglia and contains the neuropeptide substance P in abundance. Recent computational work has suggested that substance P could play a critical role in action sequence performance and acquisition, but this has not been tested experimentally before. The aim of the present study was to test how blocking substance P's main NK1-type receptors affected the sequential and temporal organization of spontaneous behavioral patterns. We did this in rats by focusing on the grooming chain, an innate and highly stereotyped ordered sequence. We performed an open field experiment in which the NK1 receptor antagonist L-733,060 was injected intraperitoneally in rats at two doses (2 and 4 mg/kg/ml), in a within-subject counterbalanced design. We used first order transition probabilities, Variable Length Markov Models, entropy metrics and T-pattern analysis to evaluate the effects of L-733,060 on sequential and temporal aspects of spontaneously ordered behavioral sequences. Our results suggest that blocking NK1 receptors made the transitions between the grooming chain elements significantly more variable, the transition structure of the grooming bouts simpler, and it increased the probability of transitioning from active to inactive states. Overall, this suggest that blocking substance P receptors led to a general break down in the fluency of spontaneous behavioral sequences, suggesting that substance P could be playing a key role in the implementation of sequential patterns.

How does your brain decide what you will do next? Over the past few decades compelling evidence has emerged that the basal ganglia, a collection of nuclei in the fore- and mid-brain of all vertebrates, are vital to action selection. Gurney, Prescott, and Redgrave published an influential computational account of this idea in Biological Cybernetics in 2001. Here we take a look back at this pair of papers, outlining the "GPR" model contained therein, the context of that model's development, and the influence it has had over the past twenty years. Tracing its lineage into models and theories still emerging now, we are encouraged that the GPR model is that rare thing, a computational model of a brain circuit whose advances were directly built on by others., 6 pages, 2 figures

The bright and understudied classical Be star HD 6226 has exhibited multiple outbursts in the last several years during which the star grew a viscous decretion disk. We analyze 659 optical spectra of the system collected from 2017-2020, along with a UV spectrum from the Hubble Space Telescope and high cadence photometry from both TESS and the KELT survey. We find that the star has a spectral type of B2.5IIIe, with a rotation rate of 74% of critical. The star is nearly pole-on with an inclination of $13.4$ degree. We confirm the spectroscopic pulsational properties previously reported, and report on three photometric oscillations from KELT photometry. The outbursting behavior is studied with equivalent width measurements of H$\alpha$ and H$\beta$, and the variations in both of these can be quantitatively explained with two frequencies through a Fourier analysis. One of the frequencies for the emission outbursts is equal to the difference between two photometric oscillations, linking these pulsation modes to the mass ejection mechanism for some outbursts. During the TESS observation time period of 2019 October 7 to 2019 November 2, the star was building a disk. With a large dataset of H$\alpha$ and H$\beta$ spectroscopy, we are able to determine the timescales of dissipation in both of these lines, similar to past work on Be stars that has been done with optical photometry. HD 6226 is an ideal target with which to study the Be disk-evolution given its apparent periodic nature, allowing for targeted observations with other facilities in the future., Comment: 48 pages including appendices, accepted to MNRAS. MNRAS online version has a 3D printed dynamical spectrum in the appendix. Contact for this section (due to size limitations)

The problem of selecting one action from a set of different possible actions, simply referred to as the problem of action selection, is a ubiquitous challenge in the animal world. For vertebrates, the basal ganglia (BG) are widely thought to implement the core computation to solve this problem, as the anatomy and physiology of the BG are well-suited to this end. However, the BG still displays physiological features whose role in achieving efficient action selection remains unclear. In particular, it is known that the two types of dopaminergic receptors (D1 and D2) present in the BG give rise to mechanistically different responses. The overall effect will be a difference in sensitivity to dopamine which may have ramifications for action selection. However, which receptor type leads to a stronger response is, a priori, unclear, due to the complexity of the intracellular mechanisms involved. In this study, we use the action selection hypothesis to {\em predict} which of D1 or D2 has the greater sensitivity. Thus, we ask - what sensitivity ratio would result in enhanced action selection functionality in the basal ganglia? To do this, we incorporated differential D1 and D2 sensitivity in an existing, high level computational model of the macro-architecture of the basal ganglia, via a simple weighting variable. We then quantitatively assessed the model's capacity to perform action selection as we parametrically manipulated the new feature. We show that differential (rather than equal) D1 and D2 sensitivity to dopaminergic input improves action selection, and specifically, that greater D1 sensitivity (compared to that for D2) leads to these improvements.

Evolution has endowed animals with outstanding adaptive behaviours which are grounded in the organization of their sensorimotor system. This paper uses inspiration from these principles of organization in the design of an artificial agent for autonomous driving. After distilling the relevant principles from biology, their functional role in the implementation of an artificial system are explained. The resulting Agent, developed in an EU H2020 Research and Innovation Action, is used to concretely demonstrate the emergence of adaptive behaviour with a significant level of autonomy. Guidelines to adapt the same principled organization of the sensorimotor system to other agents for driving are also obtained. The demonstration of the system abilities is given with example scenarios and open access simulation tools. Prospective developments concerning learning via mental imagery are finally discussed.

Projections of greenhouse gas (GHG) emissions are critical to better understanding and anticipating future climate change under different socio-economic conditions and mitigation strategies. The climate projections and scenarios assessed by the Intergovernmental Panel on Climate Change, following the Shared Socioeconomic Pathway (SSP)-Representative Concentration Pathway (RCP) framework, have provided a rich understanding of the constraints and opportunities for policy action. However, the current emissions scenarios lack an explicit treatment of urban emissions within the global context. Given the pace and scale of urbanization, with global urban populations expected to increase from about 4.4 billion today to about 7 billion by 2050, there is an urgent need to fill this knowledge gap. Here, we estimate the share of global GHG emissions emanating from urban areas from 1990 to 2100 based on the SSP-RCP framework. The urban GHG emissions are presented in five regional aggregates and are based on a combination of the urban population share, 2015 urban per capita CO2eq emissions, SSP-based national CO2eq emissions, and recent analysis of urban per capita CO2eq trends. We find that urban areas account for the majority of global GHG emissions in 2015 (61.8%). Moreover, the urban share of global GHG emissions progressively increases into the future, exceeding 80% in some scenarios by the end of the century. The combined urban areas in Asia and Developing Pacific, and Developed Countries account for 65.0% to 73.3% of cumulative urban emissions between 2020 and 2100 across the scenarios. Given these dominant roles, we describe the implications to potential urban mitigation in each of the scenario narratives in order to meet the goal of climate neutrality within this century.

We present a biologically-inspired and scalablemodel of the Basal Ganglia (BG) simulated on the SpiNNakermachine, a biologically-inspired low-power hardware platformallowing parallel, asynchronous computing. Our BG model con-sists of six cell populations, where the neuro-computational unitis a conductance-based Izhikevich spiking neuron; the numberof neurons in each population is proportional to that reported inanatomical literature. This model is treated as a single-channelof action-selection in the BG, and is scaled-up to three channelswith lateral cross-channel connections. When tested with twocompeting inputs, this three-channel model demonstrates action-selection behaviour. The SpiNNaker-based model is mappedexactly on to SpineML running on a conventional computer; bothmodel responses show functional and qualitative similarity, thusvalidating the usability of SpiNNaker for simulating biologically-plausible networks. Furthermore, the SpiNNaker-based modelsimulates in real time for time-steps1 ms; power dissipatedduring model execution is ≈ 1.8 W.

Neural oscillations in the basal ganglia are well studied yet remain poorly understood. Behavioural correlates of spectral activity are well described, yet a quantitative hypothesis linking time domain dynamics and spectral properties to basal ganglia function has been lacking. We show, for the first time, that a unified description is possible by interpreting previously ignored structure in data describing GPi responses to cortical stimulation. These data were used to expose a pair of distinctive neuronal responses to the stimulation. This observation formed the basis for a new mathematical model of the BG, quantitatively fitted to the data, which describes the dynamics in the data, and is validated against other stimulus protocol experiments. A key new result is that when the model is run using inputs hypothesised to occur during the performance of a motor task, beta and gamma frequency oscillations emerge naturally during static-force and movement respectively, consistent with experimental local field potentials. This new model predicts that the pallido-striatum connection has a key role in the generation of beta band activity, and that the gamma band activity associated with motor task performance has its origins in the pallido-subthalamic feedback loop. The network's functionality as a selection-mechanism also occurs as an emergent property, and closer fits to the data gave better selection properties. The model provides a coherent framework for the study of spectral, temporal and functional analyses of the BG and therefore lays the foundation for an integrated approach to study BG pathologies such as Parkinson's disease in silico.

There is a growing requirement in computational neuroscience for tools that permit collaborative model building, model sharing, combining existing models into a larger system (multi-scale model integration), and are able to simulate models using a variety of simulation engines and hardware platforms. Layered XML model specification formats solve many of these problems, however they are difficult to write and visualise without tools. Here we describe a new graphical software tool, SpineCreator, which facilitates the creation and visualisation of layered models of point spiking neurons or rate coded neurons without requiring the need for programming. We demonstrate the tool through the reproduction and visualisation of published models and show simulation results using code generation interfaced directly into SpineCreator. As a unique application for the graphical creation of neural networks, SpineCreator represents an important step forward for neuronal modelling.

We present a global dataset of anthropogenic carbon dioxide (CO

Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics., Author summary Decision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinson’s and Huntington’s diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decision-making has, hitherto, remained elusive. Here we show the essential composition of the brain’s decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys’ choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decision-making in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.

The striatum is the primary input nucleus for the basal ganglia, and receives glutamatergic afferents from the cortex. Under the hypothesis that basal ganglia perform action selection, these cortical afferents encode potential “action requests.” Previous studies have suggested the striatum may utilize a mutually inhibitory network of medium spiny neurons (MSNs) to filter these requests so that only those of high salience are selected. However, the mechanisms enabling the striatum to perform clean, rapid switching between distinct actions that form part of a learned action sequence are still poorly understood. Substance P (SP) and enkephalin are neuropeptides co-released with GABA in MSNs preferentially expressing D1 or D2 dopamine receptors respectively. SP has a facilitatory effect on subsequent glutamatergic inputs to target MSNs, while enkephalin has an inhibitory effect. Blocking the action of SP in the striatum is also known to affect behavioral transitions. We constructed phenomenological models of the effects of SP and enkephalin, and integrated these into a hybrid model of basal ganglia comprising a spiking striatal microcircuit and rate–coded populations representing other major structures. We demonstrated that diffuse neuropeptide connectivity enhanced the selection of unordered action requests, and that for true action sequences, where action semantics define a fixed structure, a patterning of the SP connectivity reflecting this ordering enhanced selection of actions presented in the correct sequential order and suppressed incorrect ordering. We also showed that selective pruning of SP connections allowed context–sensitive inhibition of specific undesirable requests that otherwise interfered with selection of an action group. Our model suggests that the interaction of SP and enkephalin enhances the contrast between selection and rejection of action requests, and that patterned SP connectivity in the striatum allows the “chunking” of actions and improves selection of sequences. Efficient execution of action sequences may therefore result from a combination of ordered cortical inputs and patterned neuropeptide connectivity within striatum.\ud \ud

It is hypothesised that the basal ganglia play a key role in solving the problem of action selection. Here we investigate this hypothesis through computational modelling of the primate saccadic oculomotor system. This system is an excellent target for computational modelling because it is supported by a reasonably well understood functional anatomy, has limited degrees of freedom, and there is a wealth of behavioural and electrophysiological data for model comparison. Here, we describe a computational model of the reflexive saccadic oculomotor system incorporating the basal ganglia, key structures in motor control and competition between possible actions. To restrict the likelihood of overfitting the model it is structured and parameterised by the known anatomy and neurophysiology along with data from a single experimental behavioural paradigm, then validated by testing against several additional behavioural experimental data without modification of the parameters. With this model we reproduce a range of fundamental reflexive saccadic results both qualitatively and quantitatively, comprising: the distribution of saccadic latencies; the effect of eccentricity, luminance and fixation-target interactions on saccadic latencies; and the effect of competing targets on saccadic endpoint. By investigating the model dynamics we are able to provide mechanistic explanations for the sources of these behaviours. Further, because of its accesibility, the oculomotor system has also been used to study general principle of sensorimotor control. We interpret the ability of the basal ganglia to successfully control saccade selection in our model as further evidence for the action selection hypothesis.

The study of action selection in humans can present challenges of task design since our actions are usually defined by many degrees of freedom and therefore occupy a large action-space. While saccadic eye-movement offers a more constrained paradigm for investigating action selection, the study of reach-and-grasp in upper limbs has often been defined by more complex scenarios, not easily interpretable in terms of such selection. Here we present a novel motor behaviour task which addresses this by limiting the action space to a single degree of freedom in which subjects have to track (using a stylus) a vertical coloured target line displayed on a tablet computer, whilst ignoring a similarly oriented distractor line in a different colour. We ran this task with 55 subjects and showed that, in agreement with previous studies, the presence of the distractor generally increases the movement latency and directional error rate. Further, we used two distractor conditions according to whether the distractor's location changes asynchronously or synchronously with the location of the target. We found that the asynchronous distractor yielded poorer performance than its synchronous counterpart, with significantly higher movement latencies and higher error rates. We interpret these results in an action selection framework with two actions (move left or right) and competing 'action requests' offered by the target and distractor. As such, the results provide insights into action selection performance in humans and supply data for directly constraining future computational models therein., Comment: 28 pages, 12 figures, journal article

Small Unmanned Air Vehicles (UAVs) have become increasingly used in many fields to keep up with demands for technological growth and due to their unique ability to provide an “eye-in-the-sky”. However, traditional guidance systems are not always suitable for a transition to smaller UAVs. Honeybee navigation has long been proposed as a basis for developing navigation for robotics as they are known to solve complex tasks efficiently and robustly. An approach for bio-inspired navigation using optic flow is presented here based on honeybee reactive flight control. This approach is tested and verified in the benchmark hallway navigation problem. It is shown that the control approach can explain a wide-range of biological behaviors using minimal sensors similar to flying insects.

One of the most widely implemented models for multialternative\ud decision-making is the multihypothesis sequential\ud probability ratio test (MSPRT). It is asymptotically optimal,\ud straightforward to implement, and has found application in\ud modelling biological decision-making. However, the MSPRT\ud is limited in application to discrete (‘trial-based’), non-timevarying\ud scenarios. By contrast, real world situations will\ud be continuous and entail stimulus non-stationarity. In these\ud circumstances, decision-making mechanisms (like the MSPRT)\ud which work by accumulating evidence, must be able to discard\ud outdated evidence which becomes progressively irrelevant. To\ud address this issue, we introduce a new decision mechanism\ud by augmenting the MSPRT with a rectangular integration\ud window and a transparent decision boundary. This allows\ud selection and de-selection of options as their evidence changes\ud dynamically. Performance was enhanced by adapting the\ud window size to problem difficulty. Further, we present an\ud alternative windowing method which exponentially decays\ud evidence and does not significantly degrade performance,\ud while greatly reducing the memory resources necessary. The\ud methods presented have proven successful at allowing for the\ud MSPRT algorithm to function in a non-stationary environment.

Deep brain stimulation (DBS) is a remarkably successful treatment for the motor symptoms of Parkinson's disease. High-frequency stimulation of the subthalamic nucleus (STN) within the basal ganglia is a main clinical target, but the physiological mechanisms of therapeutic STN DBS at the cellular and network level are unclear. We set out to begin to address the hypothesis that a mixture of responses in the basal ganglia output nuclei, combining regularized firing and inhibition, is a key contributor to the effectiveness of STN DBS. We used our computational model of the complete basal ganglia circuit to show how such a mixture of responses in basal ganglia output naturally arises from the network effects of STN DBS. We replicated the diversification of responses recorded in a primate STN DBS study to show that the model's predicted mixture of responses is consistent with therapeutic STN DBS. We then showed how this 'mixture of response' perspective suggests new ideas for DBS mechanisms: first, that the therapeutic frequency of STN DBS is above 100�€ƒHz because the diversification of responses exhibits a step change above this frequency; and second, that optogenetic models of direct STN stimulation during DBS have proven therapeutically ineffective because they do not replicate the mixture of basal ganglia output responses evoked by electrical DBS.

Pieron's Law describes the relationship between stimulus intensity and reaction time. Previously (Stafford & Gurney, 2004), we have shown that Pieron's Law is a necessary consequence of rise-to-threshold decision making and thus will arise from optimal simple decision-making algorithms (e.g., Bogacz, Brown, Moehlis, Holmes, & Cohen, 2006). Here, we manipulate the color saturation of a Stroop stimulus. Our results show that Pieron's Law holds for color intensity and color-naming reaction time, extending the domain of this law, in line with our suggestion of the generality of the processes that can give rise to Pieron's Law. In addition, we find that Stroop condition does not interact with the effect of color saturation; Stroop interference and facilitation remain constant at all levels of color saturation. An analysis demonstrates that this result cannot be accounted for by single-stage decision-making algorithms which combine all the evidence pertaining to a decision into a common metric. This shows that human decision making is not information-optimal and suggests that the generalization of current models of simple perceptual decision making to more complex decisions is not straightforward.

In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-fire (LIF) neurons; the second version provides performance enhancing features relative to the first. Both versions of the architecture use fixed-point arithmetic and have been implemented using a single field-programmable gate array (FPGA). They have successfully simulated networks of over 1000 neurons configured using biologically plausible models of mammalian neural systems. The neuroprocessor has been designed to be employed primarily for use on mobile robotic vehicles, allowing bio-inspired neural processing models to be integrated directly into real-world control environments. When a neuroprocessor has been designed to act as part of the closed-loop system of a feedback controller, it is imperative to maintain strict real-time performance at all times, in order to maintain integrity of the control system. This resulted in the reevaluation of some of the architectural features of existing hardware for biologically plausible neural networks (NNs). In addition, we describe a development system for rapidly porting an underlying model (based on floating-point arithmetic) to the fixed-point representation of the FPGA-based neuroprocessor, thereby allowing validation of the hardware architecture. The developmental system environment facilitates the cooperation of computational neuroscientists and engineers working on embodied (robotic) systems with neural controllers, as demonstrated by our own experience on the Whiskerbot project, in which we developed models of the rodent whisker sensory system.

The Stroop task is a paradigmatic psychological task for investigating stimulus conflict and the effect this has on response selection. The model of Cohen et al. (Cohen et al . 1990 Psychol. Rev. 97 , 332–361) has hitherto provided the best account of performance in the Stroop task, but there remains certain key data that it fails to match. We show that this failure is due to the mechanism used to perform final response selection—one based on the diffusion model of choice behaviour (Ratcliff 1978 Psychol. Rev. 85 , 59–108). We adapt the model to use a selection mechanism which is based on the putative human locus of final response selection, the basal ganglia/thalamo-cortical complex (Redgrave et al. 1999 Neuroscience 89 , 1009–1023). This improves the match to the core human data and, additionally, makes it possible for the model to accommodate, in a principled way, additional mechanisms of cognitive control that enable better fits to the data. This work prompts a critique of the diffusion model as a mechanism of response selection, and the features that any response mechanism must possess to provide adaptive action selection. We conclude that the consideration of biologically constrained solutions to the action selection problem is vital to the understanding and improvement of cognitive models of response selection.

Neurophysiological studies have identified a number of brain regions critically involved in solving the problem of action selection or decision making. In the case of highly practiced tasks, these regions include cortical areas hypothesized to integrate evidence supporting alternative actions and the basal ganglia, hypothesized to act as a central switch in gating behavioral requests. However, despite our relatively detailed knowledge of basal ganglia biology and its connectivity with the cortex and numerical simulation studies demonstrating selective function, no formal theoretical framework exists that supplies an algorithmic description of these circuits. This article shows how many aspects of the anatomy and physiology of the circuit involving the cortex and basal ganglia are exactly those required to implement the computation defined by an asymptotically optimal statistical test for decision making: the multihypothesis sequential probability ratio test (MSPRT). The resulting model of basal ganglia provides a new framework for understanding the computation in the basal ganglia during decision making in highly practiced tasks. The predictions of the theory concerning the properties of particular neuronal populations are validated in existing experimental data. Further, we show that this neurobiologically grounded implementation of MSPRT outperforms other candidates for neural decision making, that it is structurally and parametrically robust, and that it can accommodate cortical mechanisms for decision making in a way that complements those in basal ganglia.

Neural networks are modelling tools that are, in principle, able to capture the input–output behaviour of arbitrary systems that may include the dynamics of animal populations or brain circuits. While a neural network model is useful if it captures phenomenologically the behaviour of the target system in this way, its utility is amplified if key mechanisms of the model can be discovered, and identified with those of the underlying system. In this review, we first describe, at a fairly high level with minimal mathematics, some of the tools used in constructing neural network models. We then go on to discuss the implications of network models for our understanding of the system they are supposed to describe, paying special attention to those models that deal with neural circuits and brain systems. We propose that neural nets are useful for brain modelling if they are viewed in a wider computational framework originally devised by Marr. Here, neural networks are viewed as an intermediate mechanistic abstraction between ‘algorithm’ and ‘implementation’, which can provide insights into biological neural representations and their putative supporting architectures.