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He R, Small MJ, Scott IJ, Olarinre M, Sandoval-Reyes M, Ferrão P. A Novel Domain Knowledge-Informed Machine Learning Approach for Modeling Solid Waste Management Systems. Environ Sci Technol 2023; 57:18215-18224. [PMID: 37776276 DOI: 10.1021/acs.est.3c04214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/02/2023]
Abstract
Sustainability challenges, such as solid waste management, are usually scientifically complex and data scarce, which makes them not amenable to science-based analytical forms or data-intensive learning paradigms. Deep integration between data science and sustainability science in highly complementary manners offers new opportunities for tackling these conundrums. This study develops a novel hybrid neural network (HNN) model that imposes the holistic decision-making context of solid waste management systems (SWMS) on a traditional neural network (NN) architecture. Equipped with adaptable hybridization designs of hand-crafted model structure, constrained or predetermined parameters, and a customized loss function, the HNN model is capable of learning various technical, economic, and social aspects of SWMS from a small and heterogeneous data set. In comparison, the versatile HNN model not only outperforms traditional NN models in convergence rates, which leads to a 22% lower mean testing error of 0.20, but also offers superior interpretability. The HNN model is capable of generating insights into the enabling factors, policy interventions, and driving forces of SWMS, laying a solid foundation for data-driven decision making.
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Affiliation(s)
- Rui He
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Mitchell J Small
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Ian J Scott
- NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisbon, Portugal
| | - Motolani Olarinre
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Mexitli Sandoval-Reyes
- IN+ Center for Innovation, Technology and Policy Research, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
| | - Paulo Ferrão
- IN+ Center for Innovation, Technology and Policy Research, LARSyS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal
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Kass RE, Bong H, Olarinre M, Xin Q, Urban KN. Identification of interacting neural populations: methods and statistical considerations. J Neurophysiol 2023; 130:475-496. [PMID: 37465897 PMCID: PMC10642974 DOI: 10.1152/jn.00131.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 07/20/2023] Open
Abstract
As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods can achieve and the circumstances under which they are likely to succeed. Toward this end, we include a discussion of four cross-cutting issues: the definition of neural populations, trial-to-trial variability and Poisson-like noise, time-varying dynamics, and causality.
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Affiliation(s)
- Robert E Kass
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Heejong Bong
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Motolani Olarinre
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Qi Xin
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
| | - Konrad N Urban
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
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Chen Y, Douglas H, Medina BJ, Olarinre M, Siegle JH, Kass RE. Population burst propagation across interacting areas of the brain. J Neurophysiol 2022; 128:1578-1592. [PMID: 36321709 PMCID: PMC9744659 DOI: 10.1152/jn.00066.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
For many perceptual and behavioral tasks, a prominent feature of neural spike trains involves high firing rates across relatively short intervals of time. We call these events "population bursts." Because during a population burst information is, presumably, transmitted from one part of the brain to another, burst timing should reveal activity related to the flow of information across neural circuits. We developed a statistical method (based on a point process model) of determining, accurately, the time of the maximum (peak) population firing rate on a trial-by-trial basis and used it to characterize burst propagation across areas. We then examined the tendency of peak firing rates in distinct brain areas to shift earlier or later in time, together, across repeated trials, and found this trial-to-trial coupling of peak times to be a sensitive indicator of interaction across populations. In the data we examined, from the Allen Brain Observatory, we found many very strong correlations (95% confidence intervals above 0.75) in cases where standard methods were unable to demonstrate cross-area correlation. The statistical model introduced cross-area covariation only through population-level trial-dependent time shifts and gain constants (values of which were learned from the data), yet it provided very good fits to data histograms, including histograms of spike count correlations within and across visual areas. Our results demonstrate the utility of carefully assessing timing and propagation, across brain regions, of transient bursts in neural population activity, based on multiple spike train recordings.NEW & NOTEWORTHY We developed a novel statistical method for identifying coordinated propagation of activity across populations of spiking neurons, with high temporal accuracy. Using simultaneous recordings from three visual areas we document precise timing relationships on a trial-by-trial basis, and we show how previously existing techniques can fail to discover coordinated activity in cases where the new approach finds very strong cross-area correlation.
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Affiliation(s)
- Yu Chen
- 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania,2Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Hannah Douglas
- 3Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Bryan J. Medina
- 5Department of Computer Science, University of Central Florida, Orlando, Florida
| | - Motolani Olarinre
- 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania,3Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | | | - Robert E. Kass
- 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania,2Neuroscience Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania,3Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
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Rotstein HG, Olarinre M, Golowasch J. Dynamic compensation mechanism gives rise to period and duty-cycle level sets in oscillatory neuronal models. J Neurophysiol 2016; 116:2431-2452. [PMID: 27559141 DOI: 10.1152/jn.00357.2016] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 08/24/2016] [Indexed: 02/07/2023] Open
Abstract
Rhythmic oscillation in neurons can be characterized by various attributes, such as the oscillation period and duty cycle. The values of these features depend on the amplitudes of the participating ionic currents, which can be characterized by their maximum conductance values. Recent experimental and theoretical work has shown that the values of these attributes can be maintained constant for different combinations of two or more ionic currents of varying conductances, defining what is known as level sets in conductance space. In two-dimensional conductance spaces, a level set is a curve, often a line, along which a particular oscillation attribute value is conserved. In this work, we use modeling, dynamical systems tools (phase-space analysis), and numerical simulations to investigate the possible dynamic mechanisms responsible for the generation of period and duty-cycle levels sets in simplified (linearized and FitzHugh-Nagumo) and conductance-based (Morris-Lecar) models of neuronal oscillations. A simplistic hypothesis would be that the tonic balance between ionic currents with the same or opposite effective signs is sufficient to create level sets. According to this hypothesis, the dynamics of each ionic current during a given cycle are well captured by some constant quantity (e.g., maximal conductances), and the phase-plane diagrams are identical or are almost identical (e.g., cubic-like nullclines with the same maxima and minima) for different combinations of these maximal conductances. In contrast, we show that these mechanisms are dynamic and involve the complex interaction between the nonlinear voltage dependencies and the effective time scales at which the ionic current's dynamical variables operate.
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Affiliation(s)
- Horacio G Rotstein
- Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey; and
| | - Motolani Olarinre
- Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey; and
| | - Jorge Golowasch
- Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey; and .,Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University, Newark, New Jersey
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