Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons

Rumbell, Timothy; Draguljić, Danel; Yadav, Aniruddha; Hof, Patrick R.; Luebke, Jennifer I.; Weaver, Christina M.

doi:10.1007/s10827-016-0605-9

Cited by 34 publications

(58 citation statements)

References 64 publications

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“…Recent compartmental models of pyramidal neurons in L3 of the prefrontal cortex of rhesus monkeys suggested that aged animals have smaller C m values (~0.7 µF/cm 2 ) than young animals (~1.1 µF/cm 2 ) (Rumbell et al, 2016). Further direct measurements of C m , e.g., using the nucleated patch technique in different animals, could provide insights to the evolution of the specific capacitance of neuronal membranes.…”

Section: Discussionmentioning

confidence: 99%

Unique membrane properties and enhanced signal processing in human neocortical neurons

Eyal

Verhoog

Testa-Silva

et al. 2016

eLife

180

237

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The advanced cognitive capabilities of the human brain are often attributed to our recently evolved neocortex. However, it is not known whether the basic building blocks of the human neocortex, the pyramidal neurons, possess unique biophysical properties that might impact on cortical computations. Here we show that layer 2/3 pyramidal neurons from human temporal cortex (HL2/3 PCs) have a specific membrane capacitance (Cm) of ~0.5 µF/cm2, half of the commonly accepted 'universal' value (~1 µF/cm2) for biological membranes. This finding was predicted by fitting in vitro voltage transients to theoretical transients then validated by direct measurement of Cm in nucleated patch experiments. Models of 3D reconstructed HL2/3 PCs demonstrated that such low Cm value significantly enhances both synaptic charge-transfer from dendrites to soma and spike propagation along the axon. This is the first demonstration that human cortical neurons have distinctive membrane properties, suggesting important implications for signal processing in human neocortex.DOI: http://dx.doi.org/10.7554/eLife.16553.001

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Section: Discussionmentioning

confidence: 99%

Unique membrane properties and enhanced signal processing in human neocortical neurons

Eyal

Verhoog

Testa-Silva

et al. 2016

eLife

180

237

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“…This network-level study represents an extension of our past modeling studies at the single-neuron level on parameters affecting firing properties of individual pyramidal neurons with aging (Coskren et al, 2015;Luebke et al, 2015;Rumbell et al, 2016), in different cortical regions , and in neurodegeneration (Goodliffe et al, 2018). The elegance of these network models lies in their ability to represent empirically observed changes in FR and synaptic weights with aging as perturbations to a small number of network parameters.…”

Section: Discussionmentioning

confidence: 99%

“…We conducted two general types of parameter exploration studies for the simulated DRT. The first were sweeps across parameter space using a space-filling Latin Hypercube Sampling (LHS) design, as in (Johnson et al, 1990;Rumbell et al, 2016). The LHS design identified 4200 points (networks) across the parameter space of synaptic weights (G EEa , G EEn , G IE , G EIa , G EIn , and G II ), representing semi-random combinations of the synaptic weights homogeneously distributed across the 6-dimensional space (Rumbell et al, 2016) (see Table 2).…”

Section: Exploring the Drt Model Parameter Spacementioning

confidence: 99%

“…The first were sweeps across parameter space using a space-filling Latin Hypercube Sampling (LHS) design, as in (Johnson et al, 1990;Rumbell et al, 2016). The LHS design identified 4200 points (networks) across the parameter space of synaptic weights (G EEa , G EEn , G IE , G EIa , G EIn , and G II ), representing semi-random combinations of the synaptic weights homogeneously distributed across the 6-dimensional space (Rumbell et al, 2016) (see Table 2). Bounds of the parameter space were chosen so that the mean firing rate of excitatory neurons in all "young" model networks performing the DRT successfully had mean firing rates of excitatory neurons near 35 Hz, a realistic value for the PFC (see Fig.…”

Section: Exploring the Drt Model Parameter Spacementioning

confidence: 99%

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Network models predict that pyramidal neuron hyperexcitability and synapse loss in the dlPFC lead to age-related spatial working memory impairment in rhesus monkeys

Ibañez

Luebke

Chang

et al. 2019

Preprint

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Behavioral studies have shown spatial working memory impairment with aging in several animal species, including humans. Persistent activity of layer 3 pyramidal dorsolateral prefrontal cortex (dlPFC) neurons during delay periods of working memory tasks is important for encoding memory of the stimulus. In vitro studies have shown that these neurons undergo significant age-related structural and functional changes, but the extent to which these changes affect neural mechanisms underlying spatial working memory is not understood fully. Here we confirm previous studies showing impairment on the Delayed Recognition Span Task in the spatial condition (DRSTsp), and increased in vitro action potential firing rates (hyperexcitability), across the adult life span of the rhesus monkey. We use a bump attractor model to predict how empirically observed changes in the aging dlPFC affect performance on the Delayed Response Task (DRT), and introduce a model of memory retention in the DRSTsp. Persistent activity-and, in turn, cognitive performance-in both models was affected much more by hyperexcitability of pyramidal neurons than by a loss of synapses. Our DRT simulations predict that additional changes to the network, such as increased firing of inhibitory interneurons, are needed to account for lower firing rates during the DRT with aging reported in vivo. Synaptic facilitation was an essential feature of the DRSTsp model, but it did not compensate fully for the effects of the other age-related changes on DRT performance. Modeling pyramidal neuron hyperexcitability and synapse loss simultaneously led to a partial recovery of function in both tasks, with the simulated level of DRSTsp impairment similar to that observed in aging monkeys. This modeling work integrates empirical data across multiple scales, from synapse counts to cognitive testing, to further our understanding of aging in non-human primates.

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“…Within the field of computational neuroscience, evolutionary algorithms have been predominantly applied to the tuning of single-cell models or small groups of neurons [24, 25]. Here, we use them for automated tuning of biological reinforcement learning metaparameters in large-scale spiking networks with behavioral outputs.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

Durá-Bernal¹,

Neymotin²,

Kerr³

et al. 2017

IBM J. Res. & Dev.

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Biomimetic simulation permits neuroscientists to better understand the complex neuronal dynamics of the brain. Embedding a biomimetic simulation in a closed-loop neuroprosthesis, which can read and write signals from the brain, will permit applications for amelioration of motor, psychiatric, and memory-related brain disorders. Biomimetic neuroprostheses require real-time adaptation to changes in the external environment, thus constituting an example of a dynamic data-driven application system. As model fidelity increases, so does the number of parameters and the complexity of finding appropriate parameter configurations. Instead of adapting synaptic weights via machine learning, we employed major biological learning methods: spike-timing dependent plasticity and reinforcement learning. We optimized the learning metaparameters using evolutionary algorithms, which were implemented in parallel and which used an island model approach to obtain sufficient speed. We employed these methods to train a cortical spiking model to utilize macaque brain activity, indicating a selected target, to drive a virtual musculoskeletal arm with realistic anatomical and biomechanical properties to reach to that target. The optimized system was able to reproduce macaque data from a comparable experimental motor task. These techniques can be used to efficiently tune the parameters of multiscale systems, linking realistic neuronal dynamics to behavior, and thus providing a useful tool for neuroscience and neuroprosthetics.

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Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons

Cited by 34 publications

References 64 publications

Unique membrane properties and enhanced signal processing in human neocortical neurons

Unique membrane properties and enhanced signal processing in human neocortical neurons

Network models predict that pyramidal neuron hyperexcitability and synapse loss in the dlPFC lead to age-related spatial working memory impairment in rhesus monkeys

Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis

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