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Doherty DW, Chen L, Smith Y, Wichmann T, Chu HY, Lytton WW. Decreased cellular excitability of pyramidal tract neurons in primary motor cortex leads to paradoxically increased network activity in simulated parkinsonian motor cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595566. [PMID: 38948850 PMCID: PMC11212883 DOI: 10.1101/2024.05.23.595566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Decreased excitability of pyramidal tract neurons in layer 5B (PT5B) of primary motor cortex (M1) has recently been shown in a dopamine-depleted mouse model of parkinsonism. We hypothesized that decreased PT5B neuron excitability would substantially disrupt oscillatory and non-oscillatory firing patterns of neurons in layer 5 (L5) of primary motor cortex (M1). To test this hypothesis, we performed computer simulations using a previously validated computer model of mouse M1. Inclusion of the experimentally identified parkinsonism-associated decrease of PT5B excitability into our computational model produced a paradoxical increase in rest-state PT5B firing rate, as well as an increase in beta-band oscillatory power in local field potential (LFP). In the movement-state, PT5B population firing and LFP showed reduced beta and increased high-beta, low-gamma activity of 20-35 Hz in the parkinsonian, but not in control condition. The appearance of beta-band oscillations in parkinsonism would be expected to disrupt normal M1 motor output and contribute to motor activity deficits seen in patients with Parkinson's disease (PD).
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Dura-Bernal S, Griffith EY, Barczak A, O'Connell MN, McGinnis T, Moreira JVS, Schroeder CE, Lytton WW, Lakatos P, Neymotin SA. Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics. Cell Rep 2023; 42:113378. [PMID: 37925640 PMCID: PMC10727489 DOI: 10.1016/j.celrep.2023.113378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 09/05/2023] [Accepted: 10/19/2023] [Indexed: 11/07/2023] Open
Abstract
We developed a detailed model of macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB), and thalamic reticular nucleus, utilizing the NEURON simulator and NetPyNE tool. The A1 model simulates a cortical column with over 12,000 neurons and 25 million synapses, incorporating data on cell-type-specific neuron densities, morphology, and connectivity across six cortical layers. It is reciprocally connected to the MGB thalamus, which includes interneurons and core and matrix-layer-specific projections to A1. The model simulates multiscale measures, including physiological firing rates, local field potentials (LFPs), current source densities (CSDs), and electroencephalography (EEG) signals. Laminar CSD patterns, during spontaneous activity and in response to broadband noise stimulus trains, mirror experimental findings. Physiological oscillations emerge spontaneously across frequency bands comparable to those recorded in vivo. We elucidate population-specific contributions to observed oscillation events and relate them to firing and presynaptic input patterns. The model offers a quantitative theoretical framework to integrate and interpret experimental data and predict its underlying cellular and circuit mechanisms.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Erica Y Griffith
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Annamaria Barczak
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Monica N O'Connell
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Tammy McGinnis
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Departments of Psychiatry and Neurology, Columbia University Medical Center, New York, NY, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Kings County Hospital Center, Brooklyn, NY, USA
| | - Peter Lakatos
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department Psychiatry, NYU Grossman School of Medicine, New York, NY, USA.
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Srikanth S, Narayanan R. Heterogeneous off-target impact of ion-channel deletion on intrinsic properties of hippocampal model neurons that self-regulate calcium. Front Cell Neurosci 2023; 17:1241450. [PMID: 37904732 PMCID: PMC10613471 DOI: 10.3389/fncel.2023.1241450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/20/2023] [Indexed: 11/01/2023] Open
Abstract
How do neurons that implement cell-autonomous self-regulation of calcium react to knockout of individual ion-channel conductances? To address this question, we used a heterogeneous population of 78 conductance-based models of hippocampal pyramidal neurons that maintained cell-autonomous calcium homeostasis while receiving theta-frequency inputs. At calcium steady-state, we individually deleted each of the 11 active ion-channel conductances from each model. We measured the acute impact of deleting each conductance (one at a time) by comparing intrinsic electrophysiological properties before and immediately after channel deletion. The acute impact of deleting individual conductances on physiological properties (including calcium homeostasis) was heterogeneous, depending on the property, the specific model, and the deleted channel. The underlying many-to-many mapping between ion channels and properties pointed to ion-channel degeneracy. Next, we allowed the other conductances (barring the deleted conductance) to evolve towards achieving calcium homeostasis during theta-frequency activity. When calcium homeostasis was perturbed by ion-channel deletion, post-knockout plasticity in other conductances ensured resilience of calcium homeostasis to ion-channel deletion. These results demonstrate degeneracy in calcium homeostasis, as calcium homeostasis in knockout models was implemented in the absence of a channel that was earlier involved in the homeostatic process. Importantly, in reacquiring homeostasis, ion-channel conductances and physiological properties underwent heterogenous plasticity (dependent on the model, the property, and the deleted channel), even introducing changes in properties that were not directly connected to the deleted channel. Together, post-knockout plasticity geared towards maintaining homeostasis introduced heterogenous off-target effects on several channels and properties, suggesting that extreme caution be exercised in interpreting experimental outcomes involving channel knockouts.
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Affiliation(s)
- Sunandha Srikanth
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- Undergraduate Program, Indian Institute of Science, Bangalore, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
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Kelley C, Antic SD, Carnevale NT, Kubie JL, Lytton WW. Simulations predict differing phase responses to excitation vs. inhibition in theta-resonant pyramidal neurons. J Neurophysiol 2023; 130:910-924. [PMID: 37609720 PMCID: PMC10648938 DOI: 10.1152/jn.00160.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/21/2023] [Accepted: 08/21/2023] [Indexed: 08/24/2023] Open
Abstract
Rhythmic activity is ubiquitous in neural systems, with theta-resonant pyramidal neurons integrating rhythmic inputs in many cortical structures. Impedance analysis has been widely used to examine frequency-dependent responses of neuronal membranes to rhythmic inputs, but it assumes that the neuronal membrane is a linear system, requiring the use of small signals to stay in a near-linear regime. However, postsynaptic potentials are often large and trigger nonlinear mechanisms (voltage-gated ion channels). The goals of this work were to 1) develop an analysis method to evaluate membrane responses in this nonlinear domain and 2) explore phase relationships between rhythmic stimuli and subthreshold and spiking membrane potential (Vmemb) responses in models of theta-resonant pyramidal neurons. Responses in these output regimes were asymmetrical, with different phase shifts during hyperpolarizing and depolarizing half-cycles. Suprathreshold theta-rhythmic stimuli produced nonstationary Vmemb responses. Sinusoidal inputs produced "phase retreat": action potentials occurred progressively later in cycles of the input stimulus, resulting from adaptation. Sinusoidal current with increasing amplitude over cycles produced "phase advance": action potentials occurred progressively earlier. Phase retreat, phase advance, and subthreshold phase shifts were modulated by multiple ion channel conductances. Our results suggest differential responses of cortical neurons depending on the frequency of oscillatory input, which will play a role in neuronal responses to shifts in network state. We hypothesize that intrinsic cellular properties complement network properties and contribute to in vivo phase-shift phenomena such as phase precession, seen in place and grid cells, and phase roll, also observed in hippocampal CA1 neurons.NEW & NOTEWORTHY We augmented electrical impedance analysis to characterize phase shifts between large-amplitude current stimuli and nonlinear, asymmetric membrane potential responses. We predict different frequency-dependent phase shifts in response excitation vs. inhibition, as well as shifts in spike timing over multiple input cycles, in theta-resonant pyramidal neurons. We hypothesize that these effects contribute to navigation-related phenomena such as phase precession and phase roll. Our neuron-level hypothesis complements, rather than falsifies, prior network-level explanations of these phenomena.
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Affiliation(s)
- Craig Kelley
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York, United States
| | - Srdjan D Antic
- Institute of Systems Genomics, Neuroscience Department, University of Connecticut Health, Farmington, Connecticut, United States
| | - Nicholas T Carnevale
- Department of Neuroscience, Yale University, New Haven, Connecticut, United States
| | - John L Kubie
- The Robert F. Furchgott Center for Neural and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Cell Biology, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
| | - William W Lytton
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York, United States
- The Robert F. Furchgott Center for Neural and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Neurology, SUNY Downstate Health Sciences University, Brooklyn, New York, United States
- Department of Neurology, Kings County Hospital Center, Brooklyn, New York, United States
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, Maryland, United States
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Dura-Bernal S, Neymotin SA, Suter BA, Dacre J, Moreira JVS, Urdapilleta E, Schiemann J, Duguid I, Shepherd GMG, Lytton WW. Multiscale model of primary motor cortex circuits predicts in vivo cell-type-specific, behavioral state-dependent dynamics. Cell Rep 2023; 42:112574. [PMID: 37300831 PMCID: PMC10592234 DOI: 10.1016/j.celrep.2023.112574] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 02/27/2023] [Accepted: 05/12/2023] [Indexed: 06/12/2023] Open
Abstract
Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Psychiatry, Grossman School of Medicine, New York University (NYU), New York, NY, USA
| | - Benjamin A Suter
- Department of Physiology, Northwestern University, Evanston, IL, USA
| | - Joshua Dacre
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | - Joao V S Moreira
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Eugenio Urdapilleta
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA
| | - Julia Schiemann
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK; Center for Integrative Physiology and Molecular Medicine, Saarland University, Saarbrücken, Germany
| | - Ian Duguid
- Centre for Discovery Brain Sciences, Edinburgh Medical School: Biomedical Sciences, University of Edinburgh, Edinburgh, UK
| | | | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY, USA; Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA; Department of Neurology, Kings County Hospital Center, Brooklyn, NY, USA
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Tikidji-Hamburyan RA, Govindaiah G, Guido W, Colonnese MT. Synaptic and circuit mechanisms prevent detrimentally precise correlation in the developing mammalian visual system. eLife 2023; 12:e84333. [PMID: 37211984 PMCID: PMC10202458 DOI: 10.7554/elife.84333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Accepted: 04/25/2023] [Indexed: 05/23/2023] Open
Abstract
The developing visual thalamus and cortex extract positional information encoded in the correlated activity of retinal ganglion cells by synaptic plasticity, allowing for the refinement of connectivity. Here, we use a biophysical model of the visual thalamus during the initial visual circuit refinement period to explore the role of synaptic and circuit properties in the regulation of such neural correlations. We find that the NMDA receptor dominance, combined with weak recurrent excitation and inhibition characteristic of this age, prevents the emergence of spike-correlations between thalamocortical neurons on the millisecond timescale. Such precise correlations, which would emerge due to the broad, unrefined connections from the retina to the thalamus, reduce the spatial information contained by thalamic spikes, and therefore we term them 'parasitic' correlations. Our results suggest that developing synapses and circuits evolved mechanisms to compensate for such detrimental parasitic correlations arising from the unrefined and immature circuit.
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Affiliation(s)
| | - Gubbi Govindaiah
- Department of Anatomical Sciences and Neurobiology, University of LouisvilleLouisvilleUnited States
| | - William Guido
- Department of Anatomical Sciences and Neurobiology, University of LouisvilleLouisvilleUnited States
| | - Matthew T Colonnese
- Department of Pharmacology and Physiology, The George Washington UniversityWashingtonUnited States
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7
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Haşegan D, Deible M, Earl C, D’Onofrio D, Hazan H, Anwar H, Neymotin SA. Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning. Front Comput Neurosci 2022; 16:1017284. [PMID: 36249482 PMCID: PMC9563231 DOI: 10.3389/fncom.2022.1017284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Artificial neural networks (ANNs) have been successfully trained to perform a wide range of sensory-motor behaviors. In contrast, the performance of spiking neuronal network (SNN) models trained to perform similar behaviors remains relatively suboptimal. In this work, we aimed to push the field of SNNs forward by exploring the potential of different learning mechanisms to achieve optimal performance. We trained SNNs to solve the CartPole reinforcement learning (RL) control problem using two learning mechanisms operating at different timescales: (1) spike-timing-dependent reinforcement learning (STDP-RL) and (2) evolutionary strategy (EVOL). Though the role of STDP-RL in biological systems is well established, several other mechanisms, though not fully understood, work in concert during learning in vivo. Recreating accurate models that capture the interaction of STDP-RL with these diverse learning mechanisms is extremely difficult. EVOL is an alternative method and has been successfully used in many studies to fit model neural responsiveness to electrophysiological recordings and, in some cases, for classification problems. One advantage of EVOL is that it may not need to capture all interacting components of synaptic plasticity and thus provides a better alternative to STDP-RL. Here, we compared the performance of each algorithm after training, which revealed EVOL as a powerful method for training SNNs to perform sensory-motor behaviors. Our modeling opens up new capabilities for SNNs in RL and could serve as a testbed for neurobiologists aiming to understand multi-timescale learning mechanisms and dynamics in neuronal circuits.
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Affiliation(s)
- Daniel Haşegan
- Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, United States
| | - Matt Deible
- Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, United States
| | - Christopher Earl
- Department of Computer Science, University of Massachusetts Amherst, Amherst, MA, United States
| | - David D’Onofrio
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Hananel Hazan
- Allen Discovery Center, Tufts University, Boston, MA, United States
| | - Haroon Anwar
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States
| | - Samuel A. Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY, United States
- Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, United States
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Jedlicka P, Bird AD, Cuntz H. Pareto optimality, economy-effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons. Open Biol 2022; 12:220073. [PMID: 35857898 PMCID: PMC9277232 DOI: 10.1098/rsob.220073] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits.
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Affiliation(s)
- Peter Jedlicka
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus-Liebig-University, Giessen, Germany,Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, Frankfurt/Main, Germany,Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
| | - Alexander D. Bird
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus-Liebig-University, Giessen, Germany,Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany,Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - Hermann Cuntz
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany,Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, Germany
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Medlock L, Sekiguchi K, Hong S, Dura-Bernal S, Lytton WW, Prescott SA. Multiscale Computer Model of the Spinal Dorsal Horn Reveals Changes in Network Processing Associated with Chronic Pain. J Neurosci 2022; 42:3133-3149. [PMID: 35232767 PMCID: PMC8996343 DOI: 10.1523/jneurosci.1199-21.2022] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 02/17/2022] [Accepted: 02/17/2022] [Indexed: 11/21/2022] Open
Abstract
Pain-related sensory input is processed in the spinal dorsal horn (SDH) before being relayed to the brain. That processing profoundly influences whether stimuli are correctly or incorrectly perceived as painful. Significant advances have been made in identifying the types of excitatory and inhibitory neurons that comprise the SDH, and there is some information about how neuron types are connected, but it remains unclear how the overall circuit processes sensory input or how that processing is disrupted under chronic pain conditions. To explore SDH function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based neuron models that reproduce the characteristic firing patterns of spinal neurons. Excitatory and inhibitory neuron populations, defined by their expression of genetic markers, spiking pattern, or morphology, were synaptically connected according to available qualitative data. Using a genetic algorithm, synaptic weights were tuned to reproduce projection neuron firing rates (model output) based on primary afferent firing rates (model input) across a range of mechanical stimulus intensities. Disparate synaptic weight combinations could produce equivalent circuit function, revealing degeneracy that may underlie heterogeneous responses of different circuits to perturbations or pathologic insults. To validate our model, we verified that it responded to the reduction of inhibition (i.e., disinhibition) and ablation of specific neuron types in a manner consistent with experiments. Thus validated, our model offers a valuable resource for interpreting experimental results and testing hypotheses in silico to plan experiments for examining normal and pathologic SDH circuit function.SIGNIFICANCE STATEMENT We developed a multiscale computer model of the posterior part of spinal cord gray matter (spinal dorsal horn), which is involved in perceiving touch and pain. The model reproduces several experimental observations and makes predictions about how specific types of spinal neurons and synapses influence projection neurons that send information to the brain. Misfiring of these projection neurons can produce anomalous sensations associated with chronic pain. Our computer model will not only assist in planning future experiments, but will also be useful for developing new pharmacotherapy for chronic pain disorders, connecting the effect of drugs acting at the molecular scale with emergent properties of neurons and circuits that shape the pain experience.
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Affiliation(s)
- Laura Medlock
- Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
| | - Kazutaka Sekiguchi
- Drug Developmental Research Laboratory, Shionogi Pharmaceutical Research Center, Toyonaka, Osaka 561-0825, Japan
- State University of New York Downstate Health Science University, Brooklyn, New York 11203
| | - Sungho Hong
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, 904-0495, Japan
| | - Salvador Dura-Bernal
- State University of New York Downstate Health Science University, Brooklyn, New York 11203
- Nathan Kline Institute for Psychiatric Research, Orangeburg, New York 10962
| | - William W Lytton
- State University of New York Downstate Health Science University, Brooklyn, New York 11203
- Kings County Hospital, Brooklyn, New York 11207
| | - Steven A Prescott
- Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
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10
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Sinha M, Narayanan R. Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations. Neuroscience 2021; 489:111-142. [PMID: 34506834 PMCID: PMC7612676 DOI: 10.1016/j.neuroscience.2021.08.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/30/2021] [Accepted: 08/31/2021] [Indexed: 10/27/2022]
Abstract
Neurons and glial cells are endowed with membranes that express a rich repertoire of ion channels, transporters, and receptors. The constant flux of ions across the neuronal and glial membranes results in voltage fluctuations that can be recorded from the extracellular matrix. The high frequency components of this voltage signal contain information about the spiking activity, reflecting the output from the neurons surrounding the recording location. The low frequency components of the signal, referred to as the local field potential (LFP), have been traditionally thought to provide information about the synaptic inputs that impinge on the large dendritic trees of various neurons. In this review, we discuss recent computational and experimental studies pointing to a critical role of several active dendritic mechanisms that can influence the genesis and the location-dependent spectro-temporal dynamics of LFPs, spanning different brain regions. We strongly emphasize the need to account for the several fast and slow dendritic events and associated active mechanisms - including gradients in their expression profiles, inter- and intra-cellular spatio-temporal interactions spanning neurons and glia, heterogeneities and degeneracy across scales, neuromodulatory influences, and activitydependent plasticity - towards gaining important insights about the origins of LFP under different behavioral states in health and disease. We provide simple but essential guidelines on how to model LFPs taking into account these dendritic mechanisms, with detailed methodology on how to account for various heterogeneities and electrophysiological properties of neurons and synapses while studying LFPs.
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Affiliation(s)
- Manisha Sinha
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka 560012, India.
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11
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Peng GCY, Alber M, Tepole AB, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Multiscale modeling meets machine learning: What can we learn? ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 28:1017-1037. [PMID: 34093005 PMCID: PMC8172124 DOI: 10.1007/s11831-020-09405-5] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 02/09/2020] [Indexed: 05/10/2023]
Abstract
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.
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Affiliation(s)
| | - Mark Alber
- University of California, Riverside, USA
| | | | - William R Cannon
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Suvranu De
- Rensselaer Polytechnic Institute, Troy, New York, USA
| | | | | | | | | | | | - Linda Petzold
- University of California, Santa Barbara, California, USA
| | - Ellen Kuhl
- Stanford University, Stanford, California, USA
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12
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Kelley C, Dura-Bernal S, Neymotin SA, Antic SD, Carnevale NT, Migliore M, Lytton WW. Effects of Ih and TASK-like shunting current on dendritic impedance in layer 5 pyramidal-tract neurons. J Neurophysiol 2021; 125:1501-1516. [PMID: 33689489 PMCID: PMC8282219 DOI: 10.1152/jn.00015.2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/02/2021] [Accepted: 03/02/2021] [Indexed: 02/07/2023] Open
Abstract
Pyramidal neurons in neocortex have complex input-output relationships that depend on their morphologies, ion channel distributions, and the nature of their inputs, but which cannot be replicated by simple integrate-and-fire models. The impedance properties of their dendritic arbors, such as resonance and phase shift, shape neuronal responses to synaptic inputs and provide intraneuronal functional maps reflecting their intrinsic dynamics and excitability. Experimental studies of dendritic impedance have shown that neocortical pyramidal tract neurons exhibit distance-dependent changes in resonance and impedance phase with respect to the soma. We, therefore, investigated how well several biophysically detailed multicompartment models of neocortical layer 5 pyramidal tract neurons reproduce the location-dependent impedance profiles observed experimentally. Each model tested here exhibited location-dependent impedance profiles, but most captured either the observed impedance amplitude or phase, not both. The only model that captured features from both incorporates hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and a shunting current, such as that produced by Twik-related acid-sensitive K+ (TASK) channels. TASK-like channel density in this model was proportional to local HCN channel density. We found that although this shunting current alone is insufficient to produce resonance or realistic phase response, it modulates all features of dendritic impedance, including resonance frequencies, resonance strength, synchronous frequencies, and total inductive phase. We also explored how the interaction of HCN channel current (Ih) and a TASK-like shunting current shape synaptic potentials and produce degeneracy in dendritic impedance profiles, wherein different combinations of Ih and shunting current can produce the same impedance profile.NEW & NOTEWORTHY We simulated chirp current stimulation in the apical dendrites of 5 biophysically detailed multicompartment models of neocortical pyramidal tract neurons and found that a combination of HCN channels and TASK-like channels produced the best fit to experimental measurements of dendritic impedance. We then explored how HCN and TASK-like channels can shape the dendritic impedance as well as the voltage response to synaptic currents.
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Affiliation(s)
- Craig Kelley
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York
| | - Salvador Dura-Bernal
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, New York
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
| | - Samuel A Neymotin
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York
- Department of Psychiatry, NYU Grossman School of Medicine, New York City, New York
| | - Srdjan D Antic
- Neuroscience Department, Institute of Systems Genomics, University of Connecticut Health, Farmington, Connecticut
| | | | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - William W Lytton
- Program in Biomedical Engineering, SUNY Downstate Health Sciences University and NYU Tandon School of Engineering, Brooklyn, New York
- Department of Physiology and Pharmacology, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Neurology, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Neurology, Kings County Hospital Center, Brooklyn, New York
- The Robert F. Furchgott Center for Neural and Behavioral Science, Brooklyn, New York
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13
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Gao PP, Graham JW, Zhou WL, Jang J, Angulo S, Dura-Bernal S, Hines M, Lytton WW, Antic SD. Local glutamate-mediated dendritic plateau potentials change the state of the cortical pyramidal neuron. J Neurophysiol 2021; 125:23-42. [PMID: 33085562 PMCID: PMC8087381 DOI: 10.1152/jn.00734.2019] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 10/21/2020] [Accepted: 10/21/2020] [Indexed: 01/08/2023] Open
Abstract
Dendritic spikes in thin dendritic branches (basal and oblique dendrites) are traditionally inferred from spikelets measured in the cell body. Here, we used laser-spot voltage-sensitive dye imaging in cortical pyramidal neurons (rat brain slices) to investigate the voltage waveforms of dendritic potentials occurring in response to spatially restricted glutamatergic inputs. Local dendritic potentials lasted 200-500 ms and propagated to the cell body, where they caused sustained 10- to 20-mV depolarizations. Plateau potentials propagating from dendrite to soma and action potentials propagating from soma to dendrite created complex voltage waveforms in the middle of the thin basal dendrite, comprised of local sodium spikelets, local plateau potentials, and backpropagating action potentials, superimposed on each other. Our model replicated these voltage waveforms across a gradient of glutamatergic stimulation intensities. The model then predicted that somatic input resistance (Rin) and membrane time constant (tau) may be reduced during dendritic plateau potential. We then tested these model predictions in real neurons and found that the model correctly predicted the direction of Rin and tau change but not the magnitude. In summary, dendritic plateau potentials occurring in basal and oblique branches put pyramidal neurons into an activated neuronal state ("prepared state"), characterized by depolarized membrane potential and smaller but faster membrane responses. The prepared state provides a time window of 200-500 ms, during which cortical neurons are particularly excitable and capable of following afferent inputs. At the network level, this predicts that sets of cells with simultaneous plateaus would provide cellular substrate for the formation of functional neuronal ensembles.NEW & NOTEWORTHY In cortical pyramidal neurons, we recorded glutamate-mediated dendritic plateau potentials with voltage imaging and created a computer model that recreated experimental measures from dendrite and cell body. Our model made new predictions, which were then tested in experiments. Plateau potentials profoundly change neuronal state: a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to firing triggered by other afferent inputs.
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Affiliation(s)
- Peng P Gao
- Institute for Systems Genomics, UConn Health, Farmington, Connecticut
| | - Joseph W Graham
- Department of Physiology and Pharmacology, SUNY Downstate, Brooklyn, New York
| | - Wen-Liang Zhou
- Institute for Systems Genomics, UConn Health, Farmington, Connecticut
| | - Jinyoung Jang
- Institute for Systems Genomics, UConn Health, Farmington, Connecticut
| | - Sergio Angulo
- Department of Physiology and Pharmacology, SUNY Downstate, Brooklyn, New York
| | | | - Michael Hines
- Department of Neuroscience, Yale University, New Haven, Connecticut
| | - William W Lytton
- Department of Physiology and Pharmacology, SUNY Downstate, Brooklyn, New York
- Kings County Hospital, Brooklyn, New York
| | - Srdjan D Antic
- Institute for Systems Genomics, UConn Health, Farmington, Connecticut
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14
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Sherif MA, Neymotin SA, Lytton WW. In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia. NPJ SCHIZOPHRENIA 2020; 6:25. [PMID: 32958782 PMCID: PMC7506542 DOI: 10.1038/s41537-020-00109-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 06/23/2020] [Indexed: 02/08/2023]
Abstract
Treatment of schizophrenia has had limited success in treating core cognitive symptoms. The evidence of multi-gene involvement suggests that multi-target therapy may be needed. Meanwhile, the complexity of schizophrenia pathophysiology and psychopathology, coupled with the species-specificity of much of the symptomatology, places limits on analysis via animal models, in vitro assays, and patient assessment. Multiscale computer modeling complements these traditional modes of study. Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability. Our results are consistent with the idea that overly low or high gamma power is associated with pathological information flow and information processing. These data suggest the need for careful titration of schizophrenia pharmacotherapy to avoid extremes that alter information flow in different ways. These results also identify gamma power as a potential biomarker for monitoring pathology and multi-target pharmacotherapy.
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Affiliation(s)
- Mohamed A Sherif
- Department of Psychiatry, VA Connecticut Healthcare System, 950 Campbell Avenue, West Haven, CT, USA.
- Department of Psychiatry, Yale University, New Haven, CT, USA.
- Biomedical Engineering Graduate Program, SUNY Downstate Medical Center/NYU Tandon School of Engineering, Brooklyn, NY, USA.
| | - Samuel A Neymotin
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - William W Lytton
- Biomedical Engineering Graduate Program, SUNY Downstate Medical Center/NYU Tandon School of Engineering, Brooklyn, NY, USA
- Department of Physiology and Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, USA
- Department of Neurology, SUNY Downstate Medical Center, Brooklyn, NY, USA
- Department of Neurology, Kings County Hospital Center, Brooklyn, NY, USA
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15
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Adams JC, Bell PD, Bodine SC, Brooks HL, Bunnett N, Joe B, Keehan KH, Kleyman TR, Marette A, Morty RE, Ramírez JM, Thomsen MB, Yates BJ, Zucker IH. An American Physiological Society cross-journal Call for Papers on "Deconstructing Organs: Single-Cell Analyses, Decellularized Organs, Organoids, and Organ-on-a-Chip Models". Am J Physiol Lung Cell Mol Physiol 2020; 319:L266-L272. [PMID: 32609556 PMCID: PMC7473938 DOI: 10.1152/ajplung.00311.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Josephine C Adams
- School of Biochemistry, Faculty of Life Sciences, University of Bristol, Bristol, United Kingdom
| | - P Darwin Bell
- Division of Nephrology, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Sue C Bodine
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Carver College of Medicine, University of Iowa, Iowa City, Iowa
| | - Heddwen L Brooks
- Department of Physiology, University of Arizona College of Medicine, Tucson, Arizona
| | - Nigel Bunnett
- Department of Molecular Pathobiology, New York University, New York, New York
| | - Bina Joe
- Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio.,Center for Hypertension and Personalized Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, Ohio
| | | | - Thomas R Kleyman
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.,Department of Cell Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.,Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - André Marette
- Department of Medicine, Faculty of Medicine, Cardiology Axis of the Québec Heart and Lung Institute, Hôpital Laval, Quebec City, Quebec, Canada.,Institute of Nutrition and Functional Foods, Laval University, Quebec City, Quebec, Canada
| | - Rory E Morty
- Department of Lung Development and Remodelling, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany.,Department of Internal Medicine (Pulmonology), University of Giessen and Marburg Lung Center, Justus Liebig University Giessen, member of the German Center for Lung Research (DZL), Giessen, Germany
| | - Jan-Marino Ramírez
- Department of Neurological Surgery, University of Washington Medical Center, Seattle, Washington.,Center on Human Development and Disability, University of Washington, Seattle, Washington.,Center for Integrative Brain Research, Seattle Children's Research Institute, University of Washington, Seattle, Washington
| | - Morten B Thomsen
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Bill J Yates
- Department of Otolaryngology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Irving H Zucker
- Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, Nebraska
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16
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Sivagnanam S, Gorman W, Doherty D, Neymotin SA, Fang S, Hovhannisyan H, Lytton WW, Dura-Bernal S. Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform. PEARC20 : PRACTICE AND EXPERIENCE IN ADVANCED RESEARCH COMPUTING 2020 : CATCH THE WAVE : JULY 27-31, 2020, PORTLAND, OR VIRTUAL CONFERENCE. PRACTICE AND EXPERIENCE IN ADVANCED RESEARCH COMPUTING (CONFERENCE) (2020 : ONLINE) 2020; 2020:505-509. [PMID: 35098264 DOI: 10.1145/3311790.3399621] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Biophysically detailed modeling provides an unmatched method to integrate data from many disparate experimental studies, and manipulate and explore with high precision the resultin brain circuit simulation. We developed a detailed model of the brain motor cortex circuits, simulating over 10,000 biophysically detailed neurons and 30 million synaptic connections. Optimization and evaluation of the cortical model parameters and responses was achieved via parameter exploration using grid search parameter sweeps and evolutionary algorithms. This involves running tens of thousands of simulations requiring significant computational resources. This paper describes our experience in setting up and using Google Compute Platform (GCP) with Slurm to run these large-scale simulations. We describe the best practices and solutions to the issues that arose during the process, and present preliminary results from running simulations on GCP.
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Affiliation(s)
- Subhashini Sivagnanam
- State University of New York DMC, Brooklyn NY; San Diego Supercomputer Center / University California San Diego, La Jolla CA
| | | | | | | | | | | | - William W Lytton
- State University of New York DMC, Brooklyn NY; King's County Hospital, Brooklyn NY
| | - Salvador Dura-Bernal
- State University of New York DMC, Brooklyn NY; Nathan Kline Institute for Psychiatric Research, Orangeburg NY USA
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17
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Rumbell T, Kozloski J. Dimensions of control for subthreshold oscillations and spontaneous firing in dopamine neurons. PLoS Comput Biol 2019; 15:e1007375. [PMID: 31545787 PMCID: PMC6776370 DOI: 10.1371/journal.pcbi.1007375] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 10/03/2019] [Accepted: 09/04/2019] [Indexed: 11/20/2022] Open
Abstract
Dopaminergic neurons (DAs) of the rodent substantia nigra pars compacta (SNc) display varied electrophysiological properties in vitro. Despite this, projection patterns and functional inputs from DAs to other structures are conserved, so in vivo delivery of consistent, well-timed dopamine modulation to downstream circuits must be coordinated. Here we show robust coordination by linear parameter controllers, discovered through powerful mathematical analyses of data and models, and from which consistent control of DA subthreshold oscillations (STOs) and spontaneous firing emerges. These units of control represent coordinated intracellular variables, sufficient to regulate complex cellular properties with radical simplicity. Using an evolutionary algorithm and dimensionality reduction, we discovered metaparameters, which when regressed against STO features, revealed a 2-dimensional control plane for the neuron’s 22-dimensional parameter space that fully maps the natural range of DA subthreshold electrophysiology. This plane provided a basis for spiking currents to reproduce a large range of the naturally occurring spontaneous firing characteristics of SNc DAs. From it we easily produced a unique population of models, derived using unbiased parameter search, that show good generalization to channel blockade and compensatory intracellular mechanisms. From this population of models, we then discovered low-dimensional controllers for regulating spontaneous firing properties, and gain insight into how currents active in different voltage regimes interact to produce the emergent activity of SNc DAs. Our methods therefore reveal simple regulators of neuronal function lurking in the complexity of combined ion channel dynamics. Electrophysiological activity of the neuronal membrane and concomitant ion channel properties are highly variable within groups of neurons of the same type from the same brain region. Reconciliation of the mechanisms generating neuronal activity is challenging due to the complexity of the interactions between the channel currents involved. Here we present a set of mathematical analyses that uncover the low-dimensional intracellular parameter combinations capable of regulating features of subthreshold oscillations and spontaneous firing in empirically constrained models of nigral dopaminergic neurons. This method generates, from a naive starting point, linear combinations of ion channel properties that are surprisingly capable of reliably controlling a wide variety of emergent electrophysiological activity, thereby predicting drug effects and shedding light on unsuspected compensatory mechanisms that contribute to neuronal function.
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Affiliation(s)
- Timothy Rumbell
- IBM Research, Computational Biology Center, Thomas J. Watson Research Laboratories, Yorktown Heights, New York, United States of America
- * E-mail:
| | - James Kozloski
- IBM Research, Computational Biology Center, Thomas J. Watson Research Laboratories, Yorktown Heights, New York, United States of America
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18
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Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. PLoS Comput Biol 2019; 15:e1006753. [PMID: 31095552 PMCID: PMC6541309 DOI: 10.1371/journal.pcbi.1006753] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 05/29/2019] [Accepted: 04/10/2019] [Indexed: 01/12/2023] Open
Abstract
Somatosensory thalamocortical (TC) neurons from the ventrobasal (VB) thalamus are central components in the flow of sensory information between the periphery and the cerebral cortex, and participate in the dynamic regulation of thalamocortical states including wakefulness and sleep. This property is reflected at the cellular level by the ability to generate action potentials in two distinct firing modes, called tonic firing and low-threshold bursting. Although the general properties of TC neurons are known, we still lack a detailed characterization of their morphological and electrical properties in the VB thalamus. The aim of this study was to build biophysically-detailed models of VB TC neurons explicitly constrained with experimental data from rats. We recorded the electrical activity of VB neurons (N = 49) and reconstructed morphologies in 3D (N = 50) by applying standardized protocols. After identifying distinct electrical types, we used a multi-objective optimization to fit single neuron electrical models (e-models), which yielded multiple solutions consistent with the experimental data. The models were tested for generalization using electrical stimuli and neuron morphologies not used during fitting. A local sensitivity analysis revealed that the e-models are robust to small parameter changes and that all the parameters were constrained by one or more features. The e-models, when tested in combination with different morphologies, showed that the electrical behavior is substantially preserved when changing dendritic structure and that the e-models were not overfit to a specific morphology. The models and their analysis show that automatic parameter search can be applied to capture complex firing behavior, such as co-existence of tonic firing and low-threshold bursting over a wide range of parameter sets and in combination with different neuron morphologies. Thalamocortical neurons are one of the main components of the thalamocortical system, which is implicated in key functions including sensory transmission and the transition between brain states. These functions are reflected at the cellular level by the ability to generate action potentials in two distinct modes, called burst and tonic firing. Biophysically-detailed computational modeling of these cells can provide a tool to understand the role of these neurons within thalamocortical circuitry. We started by collecting single cell experimental data by applying standardized experimental procedures in brain slices of the rat. Prior work has demonstrated that biological constraints can be integrated using multi-objective optimization to build biologically realistic models of neurons. Here, we employed similar techniques, but extended them to capture the multiple firing modes of thalamic neurons. We compared the model results with additional experimental data, test their generalization and quantitatively reject those that deviated significantly from the experimental variability. These models can be readily integrated in a data-driven pipeline to reconstruct and simulate circuit activity in the thalamocortical system.
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19
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Dura-Bernal S, Suter BA, Gleeson P, Cantarelli M, Quintana A, Rodriguez F, Kedziora DJ, Chadderdon GL, Kerr CC, Neymotin SA, McDougal RA, Hines M, Shepherd GMG, Lytton WW. NetPyNE, a tool for data-driven multiscale modeling of brain circuits. eLife 2019; 8:e44494. [PMID: 31025934 PMCID: PMC6534378 DOI: 10.7554/elife.44494] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 04/25/2019] [Indexed: 12/22/2022] Open
Abstract
Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Benjamin A Suter
- Department of PhysiologyNorthwestern UniversityChicagoUnited States
| | - Padraig Gleeson
- Department of Neuroscience, Physiology and PharmacologyUniversity College LondonLondonUnited Kingdom
| | | | | | - Facundo Rodriguez
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- MetaCell LLCBostonUnited States
| | - David J Kedziora
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - George L Chadderdon
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
| | - Cliff C Kerr
- Complex Systems Group, School of PhysicsUniversity of SydneySydneyAustralia
| | - Samuel A Neymotin
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Nathan Kline Institute for Psychiatric ResearchOrangeburgUnited States
| | - Robert A McDougal
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
- Center for Medical InformaticsYale UniversityNew HavenUnited States
| | - Michael Hines
- Department of Neuroscience and School of MedicineYale UniversityNew HavenUnited States
| | | | - William W Lytton
- Department of Physiology & PharmacologyState University of New York Downstate Medical CenterBrooklynUnited States
- Department of NeurologyKings County HospitalBrooklynUnited States
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20
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Migliore R, Lupascu CA, Bologna LL, Romani A, Courcol JD, Antonel S, Van Geit WAH, Thomson AM, Mercer A, Lange S, Falck J, Rössert CA, Shi Y, Hagens O, Pezzoli M, Freund TF, Kali S, Muller EB, Schürmann F, Markram H, Migliore M. The physiological variability of channel density in hippocampal CA1 pyramidal cells and interneurons explored using a unified data-driven modeling workflow. PLoS Comput Biol 2018; 14:e1006423. [PMID: 30222740 PMCID: PMC6160220 DOI: 10.1371/journal.pcbi.1006423] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 09/27/2018] [Accepted: 08/08/2018] [Indexed: 11/19/2022] Open
Abstract
Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron's lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiments and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other is responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry.
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Affiliation(s)
- Rosanna Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Luca L. Bologna
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Stefano Antonel
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Werner A. H. Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | | | | | - Sigrun Lange
- University College London, London, United Kingdom
- University of Westminster, London, United Kingdom
| | - Joanne Falck
- University College London, London, United Kingdom
| | - Christian A. Rössert
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Ying Shi
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Olivier Hagens
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland
| | - Maurizio Pezzoli
- Laboratory of Neural Microcircuitry (LNMC), Brain Mind Institute, EPFL, Lausanne, Switzerland
| | - Tamas F. Freund
- Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Szabolcs Kali
- Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Eilif B. Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
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21
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Jȩdrzejewski-Szmek Z, Abrahao KP, Jȩdrzejewska-Szmek J, Lovinger DM, Blackwell KT. Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes. Front Neuroinform 2018; 12:47. [PMID: 30108495 PMCID: PMC6079282 DOI: 10.3389/fninf.2018.00047] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 07/06/2018] [Indexed: 11/25/2022] Open
Abstract
Computational models in neuroscience can be used to predict causal relationships between biological mechanisms in neurons and networks, such as the effect of blocking an ion channel or synaptic connection on neuron activity. Since developing a biophysically realistic, single neuron model is exceedingly difficult, software has been developed for automatically adjusting parameters of computational neuronal models. The ideal optimization software should work with commonly used neural simulation software; thus, we present software which works with models specified in declarative format for the MOOSE simulator. Experimental data can be specified using one of two different file formats. The fitness function is customizable as a weighted combination of feature differences. The optimization itself uses the covariance matrix adaptation-evolutionary strategy, because it is robust in the face of local fluctuations of the fitness function, and deals well with a high-dimensional and discontinuous fitness landscape. We demonstrate the versatility of the software by creating several model examples of each of four types of neurons (two subtypes of spiny projection neurons and two subtypes of globus pallidus neurons) by tuning to current clamp data. Optimizations reached convergence within 1,600-4,000 model evaluations (200-500 generations × population size of 8). Analysis of the parameters of the best fitting models revealed differences between neuron subtypes, which are consistent with prior experimental results. Overall our results suggest that this easy-to-use, automatic approach for finding neuron channel parameters may be applied to current clamp recordings from neurons exhibiting different biochemical markers to help characterize ionic differences between other neuron subtypes.
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Affiliation(s)
| | - Karina P. Abrahao
- Laboratory for Integrative Neuroscience, Section on Synaptic Pharmacology, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Rockville, MD, United States
| | | | - David M. Lovinger
- Laboratory for Integrative Neuroscience, Section on Synaptic Pharmacology, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Rockville, MD, United States
| | - Kim T. Blackwell
- Krasnow Institute of Advanced Study, George Mason University, Fairfax, VA, United States
- Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, VA, United States
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22
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Gouwens NW, Berg J, Feng D, Sorensen SA, Zeng H, Hawrylycz MJ, Koch C, Arkhipov A. Systematic generation of biophysically detailed models for diverse cortical neuron types. Nat Commun 2018; 9:710. [PMID: 29459718 PMCID: PMC5818534 DOI: 10.1038/s41467-017-02718-3] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 12/20/2017] [Indexed: 01/17/2023] Open
Abstract
The cellular components of mammalian neocortical circuits are diverse, and capturing this diversity in computational models is challenging. Here we report an approach for generating biophysically detailed models of 170 individual neurons in the Allen Cell Types Database to link the systematic experimental characterization of cell types to the construction of cortical models. We build models from 3D morphologies and somatic electrophysiological responses measured in the same cells. Densities of active somatic conductances and additional parameters are optimized with a genetic algorithm to match electrophysiological features. We evaluate the models by applying additional stimuli and comparing model responses to experimental data. Applying this technique across a diverse set of neurons from adult mouse primary visual cortex, we verify that models preserve the distinctiveness of intrinsic properties between subsets of cells observed in experiments. The optimized models are accessible online alongside the experimental data. Code for optimization and simulation is also openly distributed.
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Affiliation(s)
- Nathan W Gouwens
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Jim Berg
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - David Feng
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Staci A Sorensen
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Michael J Hawrylycz
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Christof Koch
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA
| | - Anton Arkhipov
- Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA.
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