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We report a computational algorithm that uses an inverse modeling scheme to infer neuron position and morphology of cortical pyramidal neurons using spatio-temporal extracellular action potential recordings.. We first develop a generic pyramidal neuron model with stylized morphology and active channels that could mimic the realistic electrophysiological dynamics of pyramidal cells from different cortical layers. The generic stylized single neuron model has adjustable parameters for soma location, and morphology and orientation of the dendrites. The ranges for the parameters were selected to include morphology of the pyramidal neuron types in the rodent primary motor cortex. We then developed a machine learning approach that uses the local field potential simulated from the stylized model for training a convolutional neural network that predicts the parameters of the stylized neuron model. Preliminary results suggest that the proposed methodology can reliably infer the key position and morphology parameters using the simulated spatio-temporal profile of EAP waveforms. We also provide partial support to validate the inference algorithm using in vivo data. Finally, we highlight the issues involved and ongoing work to develop a pipeline to automate the scheme.

Learning in the mammalian lateral amygdala (LA) during auditory fear conditioning (tone – foot shock pairing), one form of associative learning, requires N-methyl-D-aspartate (NMDA) receptor-dependent plasticity. Despite this fact being known for more than two decades, the biophysical details related to signal flow and the involvement of the coincidence detector, NMDAR, in this learning, remain unclear. Here we use a 4000-neuron computational model of the LA (containing two types of pyramidal cells, types A and C, and two types of interneurons, fast spiking FSI and low-threshold spiking LTS) to reverse engineer changes in information flow in the amygdala that underpin such learning; with a specific focus on the role of the coincidence detector NMDAR. The model also included a Ca(2s) based learning rule for synaptic plasticity. The physiologically constrained model provides insights into the underlying mechanisms that implement habituation to the tone, including the role of NMDARs in generating network activity which engenders synaptic plasticity in specific afferent synapses. Specifically, model runs revealed that NMDARs in tone-FSI synapses were more important during the spontaneous state, although LTS cells also played a role. Training trails with tone only also suggested long term depression in tone-PN as well as tone-FSI synapses, providing possible hypothesis related to underlying mechanisms that might implement the phenomenon of habituation.

Classification of brainwaves in recordings is of considerable interest to neuroscience and medical communities. Classification techniques used presently depend on the extraction of low-level features from the recordings, which in turn affects the classification performance. To alleviate this problem, this paper proposes an end-to-end approach using Convolutional Neural Network (CNN) which has been shown to detect complex patterns in a signal by exploiting its spatiotemporal nature. The present study uses time and frequency axes for the classification using synthesized Local Field Potential (LFP) data. The results are analyzed and compared with the FFT technique. In all the results, the CNN outperforms the FFT by a significant margin especially when the noise level is high. This study also sheds light on certain signal characteristics affecting network performance.

Existing predictive models of opioid use disorder (OUD) may change as the rate of opioid prescribing decreases. Using Veterans Administration’s EHR data, we developed machine-learning predictive models of new OUD diagnoses and ranked the importance of patient features based on their ability to predict a new OUD diagnosis in 2000–2012 and 2013–2021. Using patient characteristics, the three separate machine learning techniques were comparable in predicting OUD, achieving an accuracy of >80%. Using the random forest classifier, opioid prescription features such as early refills and length of prescription consistently ranked among the top five factors that predict new OUD. Younger age was positively associated with new OUD, and older age inversely associated with new OUD. Age stratification revealed prior substance abuse and alcohol dependency as more impactful in predicting OUD for younger patients. There was no significant difference in the set of factors associated with new OUD in 2000–2012 compared to 2013–2021. Characteristics of opioid prescriptions are the most impactful variables that predict new OUD both before and after the peak in opioid prescribing rates. Predictive models should be tailored to age groups. Further research is warranted to determine if machine learning models perform better when tailored to other patient subgroups.

The crustacean cardiac ganglion (CG) network coordinates the rhythmic contractions of the heart muscle to control the circulation of blood. The network consists of 9 cells, 5 large motor cells (LC1-5) and 4 small endogenous pacemaker cells (SCs). We report a new three-compartmental biophysical model of an LC that is morphologically realistic and includes provision for inputs from the SCs via a gap-junction coupled spike-initiation-zone (SIZ) compartments. To determine physiologically viable LC models in this realistic configuration, maximal conductances in three compartments of an LC are determined by random sampling from a biologically-characterized 9D-parameter space, followed by a three-stage rejection protocol that checks for conformity with electrophysiological features from single cell traces. LC models that pass the single cell rejection protocol are then incorporated into a network model which is then used in a final rejection protocol stage. Using disparate experimental data, the study provides hitherto unknown structure-function insights related to the crustacean cardiac ganglion large cell, including predictions about morphology including the role of its SIZ, and the differential roles of active conductances in the three compartments. Further, we extend analyses of emergent conductance relationships and correlations in model neurons relative to their biological counterparts, allowing us to make inferences both with respect to the biological system as well as the implications of the ability to detect such relationships in populations of model neurons going forward.

We propose a computational pipeline that uses biophysical modeling and sequential neural posterior estimation algorithm to infer the position and morphology of single neurons using multi-electrode in vivo extracellular voltage recordings. In this inverse modeling scheme, we designed a generic biophysical single neuron model with stylized morphology that had adjustable parameters for the dimensions of the soma, basal and apical dendrites, and their location and orientations relative to the multi-electrode probe. Preliminary results indicate that the proposed methodology can infer up to eight neuronal parameters well. We highlight the issues involved in the development of the novel pipeline and areas for further improvement.

Automation of the process of developing biophysical conductance-based neuronal models involves the selection of numerous interacting parameters, making the overall process computationally intensive, complex, and often intractable. A recently reported insight about the possible grouping of currents into distinct biophysical modules associated with specific neurocomputational properties also simplifies the process of automated selection of parameters. The present paper adds a new current module to the previous report to design spike frequency adaptation and bursting characteristics, based on user specifications. We then show how our proposed grouping of currents into modules facilitates the development of a pipeline that automates the biophysical modeling of single neurons that exhibit multiple neurocomputational properties. The software will be made available for public download via our site cyneuro.org.

Neuroscience is a highly interdisciplinary field, but more collaboration among STEM disciplines is needed to advance undergraduate neuroscience education. This paper reports the development of code-based virtual laboratories to cross-foster ideas from engineering and biology in the neuroscience classroom. The simulations use a combination of NEURON and Python code to model neurophysiological processes. We report that the use of computational tools in the classroom increases student self-reported comfort in participating in a computational research project. The tools we developed have potential to increase persistence and retention of undergraduate students by encouraging interdisciplinary thinking and reducing barriers to entry.

Gamma and beta rhythms in neocortical circuits are thought to be caused by distinct subcircuits involving different type of interneurons. However, it is not clear how these distinct but inter-linked intrinsic circuits interact with afferent drive to engender the two rhythms. We report a biophysical computational model to investigate the hypothesis that tonic and phasic drive might engender beta and gamma oscillations, respectively, in a neocortical circuit.

Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity, we present a novel "OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons).

Synchronized activity in neural circuits, detected as oscillations in the extracellular field potential, has been associated with learning and memory. Neural circuits in the basolateral amygdala (BLA), a mid-temporal lobe structure, generate oscillations in specific frequency bands to mediate emotional memory functions. However, how BLA circuits generate oscillations in distinct frequency bands is not known. Of these, sharp-waves (SWs) are repetitive, brief transitions that contain a low-frequency (

Algal productivity in steady-state cultivation systems depends on important factors such as biomass concentration, solids retention time (SRT), and light intensity. Current modeling of algal growth often ignores light distribution in algal cultivation systems and does not consider all these factors simultaneously. We developed a new algal growth model using a first principles approach to incorporate the effect of light intensity on algal growth while simultaneously considering biomass concentration and SRT. We first measured light attenuation (decay) with depth in an indoor algal membrane bioreactor (A-MBR) cultivating Chlorella sp. We then simulated the light decay using a multi-layer approach and correlated the decay with biomass concentration and SRT in model development. The model was calibrated by delineating specific light absorptivity and half-saturation constant to match the algal biomass concentration in the A-MBR operated at a target SRT. We finally applied the model to predict the maximum algal productivity in both indoor and outdoor A-MBRs. The predicted maximum algal productivities in indoor and outdoor A-MBRs were 6.7 g·m−2·d−1 (incident light intensity 5732 lx, SRT approximately 8 d) and 28 g·m−2·d−1 (sunlight intensity 28,660 lx, SRT approximately 4 d), respectively. The model can be extended to include other factors (e.g., water temperature and carbon dioxide bubbling) and such a modeling framework can be applied to full-scale, continuous flow outdoor systems to improve algal productivity.

The lateral (LA) and basolateral (BL) nuclei of the amygdala regulate emotional behaviors. Despite their dissimilar extrinsic connectivity, they are often combined, perhaps because their cellular composition is similar to that of the cerebral cortex, including excitatory principal cells reciprocally connected with fast-spiking interneurons (FSIs). In the cortex, this microcircuitry produces gamma oscillations that support information processing and behavior. We tested whether this was similarly the case in the rat (males) LA and BL using extracellular recordings, biophysical modeling, and behavioral conditioning. During periods of environmental assessment, both nuclei exhibited gamma oscillations that stopped upon initiation of active behaviors. Yet, BL exhibited more robust spontaneous gamma oscillations than LA. The greater propensity of BL to generate gamma resulted from several microcircuit differences, especially the proportion of FSIs and their interconnections with principal cells. Furthermore, gamma in BL but not LA regulated the efficacy of excitatory synaptic transmission between connected neurons. Together, these results suggest fundamental differences in how LA and BL operate. Most likely, gamma in LA is externally driven, whereas in BL it can also arise spontaneously to support ruminative processing and the evaluation of complex situations. SIGNIFICANCE STATEMENT The basolateral amygdala (BLA) participates in the production and regulation of emotional behaviors. It is thought to perform this using feedforward circuits that enhance stimuli that gain emotional significance and directs them to valence-appropriate downstream effectors. This perspective overlooks the fact that its microcircuitry is recurrent and potentially capable of generating oscillations in the gamma band (50–80 Hz), which synchronize spiking activity and modulate communication between neurons. This study found that BLA gamma supports both of these processes, is associated with periods of action selection and environmental assessment regardless of valence, and differs between BLA subnuclei in a manner consistent with their heretofore unknown microcircuit differences. Thus, it provides new mechanisms for BLA to support emotional behaviors.

Numerous intrinsic currents are known to collectively shape neuronal membrane potential dynamics, or neuronal signatures. Although how sets of currents shape specific signatures such as spiking characteristics or oscillations has been studied individually, it is less clear how a neuron’s suite of currents jointly shape its entire set of signatures. Biophysical conductance based models of neurons represent a viable tool to address this important question. We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. This insight into the neurobiological organization of currents also suggests an intuitive, systematic, and robust methodology to develop biophysical single cell models with multiple biological characteristics applicable for both hand- and automated- tuning approaches. We illustrate the methodology using two example case rodent pyramidal neurons, from the lateral amygdala and the hippocampus. The methodology also helped reveal that a single core compartment model could capture multiple neuronal properties. Such biophysical single compartment models have potential to improve the fidelity of large network models.

Recent experimental and modeling studies on the lateral amygdala (LA) have implicated intrinsic excitability and competitive synaptic interactions among principal neurons (PNs) in the formation of auditory fear memories. The present modeling studies, conducted over an expanded range of intrinsic excitability in the network, revealed that only excitable PNs that received tone inputs participate in the competition. Strikingly, the number of model PNs integrated into the fear memory trace remained constant despite the much larger range considered, and model runs highlighted several conditioning-induced tone responsive characteristics of the various PN populations. Furthermore, these studies showed that although excitation was important, disynaptic inhibition among PNs is the dominant mechanism that keeps the number of plastic PNs stable despite large variations in the network's excitability. Finally, we found that the overall level of inhibition in the model network determines the number of projection cells integrated into the fear memory trace.

Machine learning techniques underlying Big Data analytics have the potential to benefit data intensive communities in e.g., bioinformatics and neuroscience domain sciences. Today’s innovative advances in these domain communities are increasingly built upon multi-disciplinary knowledge discovery and cross-domain collaborations. Consequently, shortened time to knowledge discovery is a challenge when investigating new methods, developing new tools, or integrating datasets. The challenge for a domain scientist particularly lies in the actions to obtain guidance through query of massive information from diverse text corpus comprising of a wide-ranging set of topics. In this paper, we propose a novel "domain-specific topic model" (DSTM) that can drive conversational agents for users to discover latent knowledge patterns about relationships among research topics, tools and datasets from exemplar scientific domains. The goal of DSTM is to perform data mining to obtain meaningful guidance via a chatbot for domain scientists to choose the relevant tools or datasets pertinent to solving a computational and data intensive research problem at hand. Our DSTM is a Bayesian hierarchical model that extends the Latent Dirichlet Allocation (LDA) model and uses a Markov chain Monte Carlo algorithm to infer latent patterns within a specific domain in an unsupervised manner. We apply our DSTM to large collections of data from bioinformatics and neuroscience domains that include hundreds of papers from reputed journal archives, hundreds of tools and datasets. Through evaluation experiments with a perplexity metric, we show that our model has better generalization performance within a domain for discovering highly specific latent topics.

Neuro-scientists are increasingly relying on parallel and distributed computing resources for analysis and visualization of their neuron simulations. Although science gateways have democratized relevant high performance/throughput resources, users require expert knowledge about programming and infrastructure configuration that is beyond the repertoire of most neuroscience programs. These factors become deterrents for the successful adoption and the ultimate diffusion (i.e., systemic spread) of science gateways in the neuroscience community. In this paper, we present a novel intuitionistic fuzzy logic based conversational recommender that can provide guidance to users when using science gateways for research and education workflows. The users interact with a context-aware chatbot that is embedded within custom web-portals to obtain simulation tools/resources to accomplish their goals. In order to ensure user goals are met, the chatbot profiles a user’s cyberinfrastructure and neuroscience domain proficiency level using a ‘usability quadrant’ approach. Simulation of user queries for an exemplary neuroscience use case demonstrates that our chatbot can provide step-by-step navigational support and generate distinct responses based on user proficiency.

The basolateral nucleus of the amygdala (BL) is thought to support numerous emotional behaviors through specific microcircuits. These are often thought to be comprised of feedforward networks of principal cells (PNs) and interneurons. Neither well-understood nor often considered are recurrent and feedback connections, which likely engender oscillatory dynamics within BL. Indeed, oscillations in the gamma frequency range (40 − 100 Hz) are known to occur in the BL, and yet their origin and effect on local circuits remains unknown. To address this, we constructed a biophysically and anatomically detailed model of the rat BL and its local field potential (LFP) based on the physiological and anatomical literature, along within vivoandin vitrodata we collected on the activities of neurons within the rat BL. Remarkably, the model produced intermittent gamma oscillations (∼50 − 70 Hz) whose properties matched those recordedin vivo, including their entrainment of spiking. BL gamma-band oscillations were generated by the intrinsic circuitry, depending upon reciprocal interactions between PNs and fast-spiking interneurons (FSIs), while connections within these cell types affected the rhythm’s frequency. The model allowed us to conduct experimentally impossible tests to characterize the synaptic and spatial properties of gamma. The entrainment of individual neurons to gamma depended on the number of afferent connections they received, and gamma bursts were spatially restricted in the BL. Importantly, the gamma rhythm synchronized PNs and mediated competition between ensembles. Together, these results indicate that the recurrent connectivity of BL expands its computational and communication repertoire.

Contribution: This paper demonstrates curricular modules that incorporate engineering model-based approaches, including concepts related to circuits, systems, modeling, electrophysiology, programming, and software tutorials that enhance learning in undergraduate neuroscience courses. These modules can also be integrated into other neuroscience courses. Background: Educators in biological and physical sciences urge incorporation of computation and engineering approaches into biology. Model-based approaches can provide insights into neural function; prior studies show these are increasingly being used in research in biology. Reports about their integration in undergraduate neuroscience curricula, however, are scarce. There is also a lack of suitable courses to satisfy engineering students’ interest in the challenges in the growing area of neural sciences. Intended Outcomes: (1) Improved student learning in interdisciplinary neuroscience; (2) enhanced teaching by neuroscience faculty; (3) research preparation of undergraduates; and 4) increased interdisciplinary interactions. Application Design: An interdisciplinary undergraduate neuroscience course that incorporates computation and model-based approaches and has both software- and wet-lab components, was designed and co-taught by colleges of engineering and arts and science. Findings: Model-based content improved learning in neuroscience for three distinct groups: 1) undergraduates; 2) Ph.D. students; and 3) post-doctoral researchers and faculty. Moreover, the importance of the content and the utility of the software in enhancing student learning was rated highly by all these groups, suggesting a critical role for engineering in shaping the neuroscience curriculum. The model for cross-training also helped facilitate interdisciplinary research collaborations.

Abstract The Large Cell (LC) motor neurons of the crab (C. borealis) cardiac ganglion have variable membrane conductance magnitudes even within the same individual, yet produce identical synchronized activity in the intact network. In our previous study (Lane et al., 2016) we blocked a subset of K+conductances across LCs, resulting in loss of synchronous activity. In this study, we hypothesized that this same variability of conductances could make LCs vulnerable to desynchronization during neuromodulation. We exposed the LCs to serotonin (5HT) and dopamine (DA) while recording simultaneously from multiple LCs. Both amines had distinct excitatory effects on LC output, but only 5HT caused desynchronized output. We further determined that DA rapidly increased gap junctional conductance. Co-application of both amines induced 5HT-like output, but waveforms remained synchronized. Furthermore, DA prevented desynchronization induced by the K+channel blocker tetraethylammonium (TEA), suggesting that dopaminergic modulation of electrical coupling plays a protective role in maintaining network synchrony.

The perirhinal cortex supports recognition and associative memory. Prior unit recording studies revealed that recognition memory involves a reduced responsiveness of perirhinal cells to familiar stimuli whereas associative memory formation is linked to increasing perirhinal responses to paired stimuli. Both effects are thought to depend on perirhinal plasticity but it is unclear how the same network could support these opposite forms of plasticity. However, a recent study showed that when neocortical inputs are repeatedly activated, depression or potentiation could develop, depending on the extent to which the stimulated neocortical activity recruited intrinsic longitudinal connections. We developed a biophysically realistic perirhinal model that reproduced these phenomena and used it to investigate perirhinal mechanisms of associative memory. These analyses revealed that associative plasticity is critically dependent on a specific subset of neurons, termed conjunctive cells (CCs). When the model network was trained with spatially distributed but coincident neocortical inputs, CCs acquired excitatory responses to the paired inputs and conveyed them to distributed perirhinal sites via longitudinal projections. CC ablation during recall abolished expression of the associative memory. However, CC ablation during training did not prevent memory formation because new CCs emerged, revealing that competitive synaptic interactions governs the formation of CC assemblies.

Competitive synaptic interactions between principal neurons (PNs) with differing intrinsic excitability were recently shown to determine which dorsal lateral amygdala (LAd) neurons are recruited into a fear memory trace. Here, we explored the contribution of these competitive interactions in determining the stimulus specificity of conditioned fear associations. To this end, we used a realistic biophysical computational model of LAd that included multi-compartment conductance-based models of 800 PNs and 200 interneurons. To reproduce the continuum of spike frequency adaptation displayed by PNs, the model included three subtypes of PNs with high, intermediate, and low spike frequency adaptation. In addition, the model network integrated spatially differentiated patterns of excitatory and inhibitory connections within LA, dopaminergic and noradrenergic inputs, extrinsic thalamic and cortical tone afferents to simulate conditioned stimuli as well as shock inputs for the unconditioned stimulus. Last, glutamatergic synapses in the model could undergo activity-dependent plasticity. Our results suggest that plasticity at both excitatory (PN-PN) and di-synaptic inhibitory (PN-ITN and, particularly, ITN-PN) connections are major determinants of the synaptic competition governing the assignment of PNs to the memory trace. The model also revealed that training-induced potentiation of PN-PN synapses promotes, whereas that of ITN-PN synapses opposes, stimulus generalization. Indeed, suppressing plasticity of PN-PN synapses increased, whereas preventing plasticity of interneuronal synapses decreased the CS specificity of PN recruitment. Overall, our results indicate that the plasticity configuration imprinted in the network by synaptic competition ensures memory specificity. Given that anxiety disorders are characterized by tendency to generalize learned fear to safe stimuli or situations, understanding how plasticity of intrinsic LAd synapses regulates the specificity of learned fear is an important challenge for future experimental studies.

Acetylcholine regulates memory encoding and retrieval by inducing the hippocampus to switch between pattern separation and pattern completion modes. However, both processes can introduce significant variations in the level of network activity and potentially cause a seizure-like spread of excitation. Thus, mechanisms that keep network excitation within certain bounds are necessary to prevent such instability. We developed a biologically realistic computational model of the hippocampus to investigate potential intrinsic mechanisms that might stabilize the network dynamics during encoding and retrieval. The model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in stabilizing these processes. Results showed that when CA3 was considered in isolation, inhibition solely by BCs was not sufficient to control instability. However, both inhibition by OLM cells and short-term depression at the recurrent CA3 connections stabilized the network activity. In the larger network including the dentate gyrus, the model suggested that OLM inhibition could control the network during high cholinergic levels while depressing synapses at the recurrent CA3 connections were important during low cholinergic states. Our results demonstrate that short-term plasticity is a critical property of the network that enhances its robustness. Furthermore, simulations suggested that the low and high cholinergic states can each produce runaway excitation through unique mechanisms and different pathologies. Future studies aimed at elucidating the circuit mechanisms of epilepsy could benefit from considering the two modulatory states separately.

The relative contributions of plasticity in the amygdala vs. its afferent pathways to conditioned fear remain controversial. Some believe that thalamic and cortical neurons transmitting information about the conditioned stimulus (CS) to the lateral amygdala (LA) serve a relay function. Others maintain that thalamic and/or cortical plasticity is critically involved in fear conditioning. To address this question, we developed a large-scale biophysical model of the LA that could reproduce earlier findings regarding the cellular correlates of fear conditioning in LA. We then conducted model experiments that examined whether fear memories depend on (1) training-induced increases in the responsiveness of thalamic and cortical neurons projecting to LA, (2) plasticity at the synapses they form in LA, and/or (3) plasticity at synapses between LA neurons. These tests revealed that training-induced increases in the responsiveness of afferent neurons are required for fear memory formation. However, once the memory has been formed, this factor is no longer required because the efficacy of the synapses that thalamic and cortical neurons form with LA cells has augmented enough to maintain the memory. In contrast, our model experiments suggest that plasticity at synapses between LA neurons plays a minor role in maintaining the fear memory.

Biological and theoretical evidence suggest that individual neurons may achieve similar outputs by differentially balancing variable underlying ionic conductances. Despite the substantial amount of data consistent with this idea, a direct biological demonstration that cells with conserved output, particularly within the same network, achieve these outputs via different solutions has been difficult to achieve. Here we demonstrate definitively that neurons from native neural networks with highly similar output achieve this conserved output by differentially tuning underlying conductance magnitudes. Multiple motor neurons of the crab ( Cancer borealis ) cardiac ganglion have highly conserved output within a preparation, despite showing a 2–4-fold range of conductance magnitudes. By blocking subsets of these currents, we demonstrate that the remaining conductances become unbalanced, causing disparate output as a result. Therefore, as strategies to understand neuronal excitability become increasingly sophisticated, it is important that such variability in excitability of neurons, even among those within the same individual, is taken into account.

We propose a novel reduced-order neuronal network modeling framework that includes an enhanced firing rate model and a corresponding synaptic calcium-based synaptic learning rule. Specifically, we propose enhancements to the Wilson–Cowan firing-rate neuron model that permit full spike-frequency adaptation seen in biological lateral amygdala (LA) neurons, while being sufficiently general to accommodate other spike-frequency patterns. We also report a technique to incorporate calcium-dependent plasticity in the synapses of the network using a regression scheme to link firing rate to postsynaptic calcium. Together, the single-cell model and the synaptic learning scheme constitute a general framework to develop computationally efficient neuronal networks that employ biologically realistic synaptic learning. The reduced-order modeling framework was validated using a previously reported biophysical conductance-based neuronal network model of a rodent LA that modeled features of Pavlovian conditioning and extinction of auditory fear ( Li et al., 2009 ). The framework was then used to develop a larger LA network model to investigate the roles of tone and shock distributions and of intrinsic connectivity in auditory fear learning. The model suggested combinations of tone and shock densities that would provide experimental estimates of tone responsive and conditioned cell proportions. Furthermore, it provided several insights including how intrinsic connectivity might help distribute sensory inputs to produce conditioned responses in cells that do not directly receive both tone and shock inputs, and how a balance between potentiation of excitation and inhibition prevents stimulus generalization during fear learning.

Extracellular neurotransmitter concentrations vary over a wide range depending on the type of neurotransmitter and location in the brain. Neurotransmit- ter homeostasis near a synapse is achieved by a balance of several mechanisms includ- ing vesicular release from the presynapse, diffusion, uptake by transporters, nonsy- naptic production, and regulation of release by autoreceptors. These mechanisms are also affected by the glia surrounding the synapse. However, the role of these mecha- nisms in achieving neurotransmitter homeostasis is not well understood. A biophysical modeling framework was proposed, based on a cortico-accumbens synapse example case, to reverse engineer glial configurations and parameters related to homeostasis for synapses that support a range of neurotransmitter gradients. Model experiments reveal that synapses with extracellular neurotransmitter concentrations in the micro- molar range require nonsynaptic neurotransmitter sources and tight synaptic isolation by extracellular glial formations. The model was used to identify the role of perisynap- tic parameters on neurotransmitter homeostasis and to propose glial configurations that could support different levels of extracellular neurotransmitter concentrations. Ranking the parameters based on their effect on neurotransmitter homeostasis, nonsy- naptic sources were found to be the most important followed by transporter concentra- tion and diffusion coefficient. Synapse 66:608-621, 2012. V C

Intercalated (ITC) amygdala neurons regulate fear expression by controlling impulse traffic between the input (basolateral amygdala; BLA) and output (central nucleus; Ce) stations of the amygdala for conditioned fear responses. Previously, stimulation of the infralimbic (IL) cortex was found to reduce fear expression and the responsiveness of Ce neurons to BLA inputs. These effects were hypothesized to result from the activation of ITC cells projecting to Ce. However, ITC cells inhibit each other, leading to the question of how IL inputs could overcome the inter-ITC inhibition to regulate the responses of Ce neurons to aversive conditioned stimuli (CSs). To investigate this, we first developed a compartmental model of a single ITC cell that could reproduce their bistable electroresponsive properties, as observed experimentally. Next, we generated an ITC network that implemented the experimentally observed short-term synaptic plasticity of inhibitory inter-ITC connections. Model experiments showed that strongly adaptive CS-related BLA inputs elicited persistent responses in ITC cells despite the presence of inhibitory interconnections. The sustained CS-evoked activity of ITC cells resulted from an unusual slowly deinactivating K+ current. Finally, over a wide range of stimulation strengths, brief IL activation caused a marked increase in the firing rate of ITC neurons, leading to a persistent decrease in Ce output, despite inter-ITC inhibition. Simulations revealed that this effect depended on the bistable properties and synaptic heterogeneity of ITC neurons. These results support the notion that IL inputs are in a strategic position to control extinction of conditioned fear via the activation of ITC neurons.

Faculty from the Department of Electrical and Computer Engineering and the College of Education at the University of Missouri (MU), Columbia, developed a novel course for engineering graduate students emphasizing pedagogy and professional skills. The two-semester course sequence, titled “Preparing Engineering Faculty and Professionals,” includes readings from books that cover several different areas: How People Learn, with focus on the latest findings from cognitive science and their applicability to teaching; The 7 Habits of Highly Effective People for discussion of other professional skills; and The World is Flat for discussion of global trends and its effects on professionals. Other components of the course include lectures by guest speakers on topics ranging from how universities work and how to run successful research centers to leadership traits for engineers. A pilot survey of students at the end of the two-course sequence revealed that students had acquired little knowledge about pedagogy and professional skills from other courses in their undergraduate and graduate engineering curriculum; this course addresses such deficits by raising awareness and knowledge of these skills.

Chronic cocaine administration causes instability in extracellular glutamate in the nucleus accumbens that is thought to contribute to the vulnerability to relapse. A computational framework was developed to model glutamate in the extracellular space, including synaptic and nonsynaptic glutamate release, glutamate elimination by glutamate transporters and diffusion, and negative feedback on synaptic release via metabotropic glutamate receptors (mGluR2/3). This framework was used to optimize the geometry of the glial sheath surrounding excitatory synapses, and by inserting physiological values, accounted for known stable extracellular, extrasynaptic concentrations of glutamate measured by microdialysis and glutamatergic tone on mGluR2/3. By using experimental values for cocaine-induced reductions in cystine-glutamate exchange and mGluR2/3 signaling, and by predicting the down-regulation of glutamate transporters, the computational model successfully represented the experimentally observed increase in glutamate that is seen in rats during cocaine-seeking. This model provides a mathematical framework for describing how pharmacological or pathological conditions influence glutamate transmission measured by microdialysis.

A reduced order dynamic modeling method, and a pressure control strategy are proposed for a novel axial variable-displacement pump design. The reduced order model provides insights into the dynamics, and is useful for control design. Theoretical as well as implementation insights for the control problem are also developed, including proportional control limitations which provides guidance for effective control designs.

BACKGROUND:: Most prehospital triage strategies are based on physiologic, anatomic, and mechanism-related variables. Although previous studies have suggested the value of physiologic and anatomic triage criteria, the predictive capacity of mechanism of injury has been questioned. The purpose of the current study was to evaluate the relationship between mechanism of injury and resource utilization and outcome among injured children treated at trauma centers. METHODS:: The relationship between mechanism of injury and mortality and resource utilization (need for operative care, total and ICU length of stay) was analyzed using the records of pediatric patients (age

Hippocampal theta oscillations (4-12 Hz) are consistently recorded during memory tasks and spatial navigation. While computational models suggested specific mechanisms for theta generation, experimental inactivation of these mechanisms did not disrupt theta, precluding definitive conclusions about their roles. We investigated this discrepancy using a biophysical model of the hippocampus that included several of the components implicated in rhythm generation, all constrained by prior experimental results. The CA3 network model included recurrently connected pyramidal cells, and inhibitory basket cells (BC) and oriens-lacunosum moleculare (OLM) cells. The model was developed by matching experimental results characterizing neuronal firing patterns, synaptic dynamics, short-term synaptic plasticity and the three-dimensional organization of the hippocampus. The model revealed four mechanisms that generated theta oscillations: intrinsic theta resonance of pyramidal cells, recurrent connections between them, coupling between OLM and pyramidal cells, and, as a novel finding, the correlated input from entorhinal cortex. Consistent with experimental results, inactivation of any single mechanism did not disrupt the rhythm. Another novel finding was that the low and high cholinergic states differentially recruited theta generating mechanisms. Atropine -sensitive and -resistant forms of theta, however, corresponded to theta generated during low and high levels of network excitation, respectively. These findings provided an alternative interpretation of the atropine-based classification of theta oscillations, and suggested that the theta rhythm is an intrinsic property of the network. Any experimental manipulation or brain state that enhances or suppresses excitation might also, therefore, non-specifically enhance or suppress theta oscillations.

PURPOSE: The purpose of this study was to develop a model that accurately predicts mortality among injured children based on components of the initial patient evaluation and that is generalizable to diverse acute care settings. Important predictive variables obtained in an emergency setting are frequently missing in even large national databases, limiting their effectiveness for developing predictions. In this study, a model predicting pediatric trauma mortality was developed using a national database and methods to handle missing data that may avoid biases that can occur restricting analyses to complete cases. METHODS: Records of pediatric patients included in the National Pediatric Trauma Registry (NPTR) between 1996 and 1999 were used as a training set in a logistic regression model to predict hospital mortality using vital signs, Glasgow Coma Scale (GCS) score, and intubation status. Multiple imputation was applied to handle missing data. The model was tested using independent data from the NPTR and National Trauma Data Bank (NTDB). RESULTS: Complete case analysis identified only GCS-eye and intubation status as predictors of mortality. A model based on complete case analysis had good discrimination (c-index = 0.784) and excellent calibration (Hosmer-Lemeshow c-statistic, 6.8) (p > 0.05). Using multiple imputation, three additional predictors of mortality (systolic blood pressure, pulse, and GCS-motor) were identified and improved model performance was observed. The model developed using multiple imputation had excellent discrimination (c-index, 0.947-0.973) in both test datasets. Calibration was better in the NPTR testing set than in the NTDB (Hosmer-Lemeshow c-statistic, 9.2 for NPTR [p > 0.05] and 258.2 for NTDB [p < 0.05]). At a probability cutoff that minimized misclassification in the training set, the false-negative and false-negative rates of the model were better than those obtained with either the Revised Trauma Score (RTS) or Pediatric Trauma Score using data from the NPTR testing set. Although the false-positive rates were lower with the RTS using data from the NTDB, the false-negative rates of the proposed model and the RTS were similar in this test dataset. CONCLUSIONS: Using multiple imputation to handle missing data, a model predicting pediatric trauma mortality was developed that compared favorably with existing trauma scores. Application of these methods may produce predictive trauma models that are more statistically reliable and applicable in clinical practice.

Accurate simplification of the equations of motion of an aircraft, in a way that incorporates aeroelastic effects, is important to facilitate the development of reliable time-domain dynamic models. Such time-domain models are useful for control design and for the prediction of dynamic loads early in the design cycle. Various techniques reported in the literature for such model development are critically reviewed. A particular example case is used to illustrate the different methods. The issues involved are highlighted, and two possible error quantification methods are suggested.

A high-performance mechanism has been developed to provide force feedback during virtual reality simulations of endotracheal intubation for training purposes for the first time. The force feedback mechanism (FFM) prototype permits planar motion of the intubation tool with three degrees of freedom, each with force feedback. The FFM is computer controlled using a hybrid position-force feedback algorithm that includes a feedforward term to counterbalance the mechanism. This allows the intubation force profiles to be superimposed onto the FFM weight compensation to complete the overall force feedback effort. The development of a mechanism of this type introduces several theoretical and experimental design challenges that are addressed in this paper.

A robust adaptive pressure control strategy is proposed for a novel indexing variable-displacement pump. In the proposed approach, parametric uncertainties and unmodeled dynamics are identified to the extent possible using a model free learning network and used to decouple the dynamics using physical insights derived from careful reduced order modeling. The swash plate motion control is then carefully designed to provide the desired pressure response characteristics showing improved performance with learning. The proposed control framework and designs are validated using a detailed nonlinear simulation model.

The swash-plate in a variable displacement pump experiences very large forces and moments that try to dislocate its position; therefore, a large device is required for adequate control. In this paper, the dynamics of an alternative pump design using an indexing valve plate to position the swash-plate are reported. The indexing valve plate design is aimed at controlling the pressure transition for a piston, which is moving from a high-pressure port to a low-pressure port. In this paper, the governing equations for the pump are derived and the detailed open-loop and parametric studies, which are necessary for understanding the overall dynamic characteristics of the pump, are reported. Also, full nonlinear and simplified modelling approaches for the system are compared.

The thermal dynamics and design of an Advanced Space Suit are considered. A transient model of the Advanced Space Suit has been developed and implemented using MATLAB/Simulink to help with sizing, with design evaluation, and with the development of an automatic thermal comfort control strategy. The model is described and the thermal characteristics of the Advanced Space suit are investigated including various parametric design studies. The steady state performance envelope for the Advanced Space Suit is defined in terms of the thermal environment and human metabolic rate and the transient response of the human-suit-MPLSS system is analyzed.

A new, open-loop, reduced order model is proposed for the swash plate dynamics of an axial piston pump. The difference from previous reduced order models is the modeling of a damping mechanism not reported previously in the literature. An analytical expression for the damping mechanism is derived. The proposed reduced order model is validated by comparing with a complete nonlinear simulation of the pump dynamics over the entire range of operating conditions.

The dual goals of this study are: (1) to develop an empirically valid neural model of U.S. factories in a range of industries producing discrete products, and (2) to use the model to test the effect of changes in product line width on plant performance variables. Accordingly, a neural factory was developed using 59 input and 5 output/performance variables, and was trained using field data collected from 385 U.S. manufacturing plants. The model was validated using a holdout sample before conducting sensitivity tests. The study demonstrates that, through the use of parametric sensitivity analysis, the neural factory could be used to investigate the relationship between inputs and performance of a factory. While the focused factory principle would favor a smaller product line, economies of scope theory would favor a larger product line for the good of the factory; this implies a rather complex relationship between product line width (PLW) and plant performance. The neural factory was used to study the sensitivity of output/performance variables when product line width was varied over a range extending from 10% to 200% of the average values. The sensitivity analysis of the neural factory shows that, as the product line increases, it (1) does not affect cost-of-goods-sold (COGS), (2) decreases return on investment, (3) has a negative effect on the top management's perception of manufacturing performance, (4) increases inventory turns, and (5) increases sales per employee. The explanations for these findings show how complex and intertwined the relationships between PLW and performance variables are. They enhance our understanding of PLW and provide some new directions for future empirical research.

The dynamics of a booster station, which is critical for the control of a novel, long distance, hydraulic capsule pipeline, is simulated mathematically for design studies and control of the hydraulic transients caused by the valve actuators in the system. Several modifications to the pump bypass station configuration of the booster station have been studied. With the objective of eliminating column separation and reducing flow reversals, a configuration with several centrifugal pumps connected in series, and a carefully sized air chamber is found to be a viable design. A valve control method is designed to eliminate column separation and the design results in acceptable flow reversal levels in the main pipe. The simulation results match with trends in limited experimental studies performed on a small scale experimental capsule pipeline system.

Hydraulic capsule pipelines concepts are novel as compared to existing commercial pipeline systems. The complexity of such novel systems places greater demands on sensing, automation, and control strategy design for such systems as compared to existing commercial pipeline systems. These issues, as well as hydraulic design automation and control strategies, are reported. A novel capsule separator design has also been proposed to ensure reliable functioning of ‘booster’ stations for such pipelines. Detailed dynamic modeling of the proposed capsule separator is performed for generating design and control guidelines. Validation of the overall hydraulic capsule pipeline design and control, and limited validation of the proposed capsule separator subsystem, are provided using a prototype hardware computer controlled pipeline model.

Many real world business situations require classification decisions that must often be made on the basis of judgment and past performance. In this paper, we propose a decision framework that combines multiple models or techniques in a complementary fashion to provide input to managers who make such decisions on a routine basis. We illustrate the framework by specifically using five different classification techniques – neural networks, discriminant analysis, quadratic discriminant analysis (QDA), k -nearest neighbor (KNN), and multinomial logistic regression analysis (MNL). Application of the decision framework to an actual retail department store data shows that it is most useful in those cases where uncertainty is high and a priori classification cannot be made with a high degree of reliability. The proposed framework thus enhances the value of exception reporting, and provides managers additional insights into the phenomenon being studied.

A systematic model-free methodology for the identification and compensation of friction is proposed and is shown to be viable for a class of dynamic systems. Design of the proposed identifier for friction uses Gaussian networks and incorporates explicit performance bound information. The identifier is then used in a particular compensation strategy that provides error bound information. The proposed identification and control designs have been validated using a hardware example case system. The methodology for identifying friction is systematic and uses minimal knowledge of the dynamics which is particularly attractive for a large class of low-dimensional dynamic systems with friction.

This paper proposes an adaptive method for the compensation of uncertainties, for a class of nonlinear systems. A sliding-mode control strategy is used for the robust control design, using stable learning techniques. Gaussian networks are used to identify the class of system uncertainties. Learning and control bounds are guaranteed by properly constructing the training structure. The proposed technique has been validated using a hardware example case of an electromechanical robotic linkage system. Experiments have shown that the inclusion of the proposed learning technique in the robust control design results in improved system performance.