1. Interpolate: How Resetting Active Neurons can also improve...
27 sep 2024 · This paper introduces a resetting-based method to address the plasticity loss issue in non-stationary learning settings, such as online learning ...
While neural networks have shown a significant gain in performance across a wide range of applications, they still struggle in non-stationary settings as they tend to lose their ability to adapt to...
2. Optimization of Efficient Neuron Models With Realistic Firing ... - Frontiers
14 jul 2020 · This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency
Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primit...
3. A Neuron-Optimized CRISPR/dCas9 Activation System for Robust and ...
We report a neuron-optimized CRISPR/dCas9 activation (CRISPRa) system that produces robust and specific upregulation of targeted genes in neurons both in vitro ...
CRISPR-based technology has provided new avenues to interrogate gene function, but difficulties in transgene expression in post-mitotic neurons has delayed incorporation of these tools in the central nervous system (CNS). Here, we demonstrate a ...
4. BrainPhys™ Imaging Optimized Neuronal Medium
BrainPhys™ Imaging Optimized Medium is a defined, serum-free imaging medium that supports synaptic activity for imaging live, functional neurons.
BrainPhys™ Imaging Optimized Medium is a defined, serum-free imaging medium that supports synaptic activity for imaging live, functional neurons
5. Optimization of Efficient Neuron Models With Realistic Firing ...
14 jul 2020 · This study aims to develop a methodological pipeline for creating neuron models that maintain biological realism and computational efficiency
Biologically relevant large-scale computational models currently represent one of the main methods in neuroscience for studying information processing primitives of brain areas. However, biologically realistic neuron models tend to be ...
6. Efficient optimization of ODE neuron models using gradient descent - arXiv
4 jul 2024 · Here we demonstrate the use of differential ODE solver to efficiently optimize simulations of single neurons with heterogeneous ion channel ...
Ilenna Simone Jones The Kempner Institute, Harvard University Konrad Paul Kording Department of Neuroscience, University of Pennsylvania Department of Bioengineering, University of Pennsylvania
7. Evaluation and comparison of methods for neuronal parameter ...
4 apr 2024 · Abstract. Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research.
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently fo...
8. Setting the conditions for efficient, robust and reproducible generation ...
12 sep 2008 · ... neurons when exposed to an optimized neuronal differentiation protocol. ... active GABAergic neurons of the adult mammalian brain. We ...
Although new culture conditions enable homogeneous and long-term propagation of radial glia-like neural stem (NS) cells in monolayer and serum-free conditions, the efficiency of the conversion of NS cells into terminally differentiated, functionally mature neurons is relatively limited and poorly characterized. We demonstrate that NS cells derived from adult mouse subventricular zone robustly develop properties of mature neurons when exposed to an optimized neuronal differentiation protocol. A high degree of cell viability was preserved. At 22 days in vitro, most cells (65%) were microtubule-associated protein 2+ and coexpressed γ-aminobutyric acid (GABA), GAD67, calbindin and parvalbumin. Nearly all neurons exhibited sodium, potassium and calcium currents, and 70% of them fired action potentials. These neurons expressed functional GABAA receptors, whereas activable kainate, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid and N-methyl-D-aspartic acid receptors were present in approximately 80, 30 and 2% of cells, respectively. Antigenic and functional properties were efficiently and reliably reproduced across experiments and cell passages (up to 68). This is the first report showing a consistent and reproducible generation of large amounts of neurons from long-term passaged adult neural stem cells. Remarkably, the neuronal progeny carries a defined set of antigenic, biochemical and functional characteristics that make this system suitable for studies of NS cell biology a...
9. [PDF] Designing optimal stimuli to control neuronal spike timing
We also tested the intracellular current injection method on pyramidal cells in mouse cortical slices, quantifying the dependence of spiking reliability and ...
10. Optimal control of oscillatory neuronal models with applications to ... - arXiv
15 nov 2023 · Thus, to communicate effectively, neural populations must synchronize their oscillatory activity, ensuring that input volleys from the ...
Macroscopic oscillations in the brain are involved in various cognitive and physiological processes, yet their precise function is not not completely understood. Communication Through Coherence (CTC) theory proposes that these rhythmic electrical patterns might serve to regulate the information flow between neural populations. Thus, to communicate effectively, neural populations must synchronize their oscillatory activity, ensuring that input volleys from the presynaptic population reach the postsynaptic one at its maximum phase of excitability. We consider an Excitatory-Inhibitory (E-I) network whose macroscopic activity is described by an exact mean-field model. The E-I network receives periodic inputs from either one or two external sources, for which effective communication will not be achieved in the absence of control. We explore strategies based on optimal control theory for phase-amplitude dynamics to design a control that sets the target population in the optimal phase to synchronize its activity with a specific presynaptic input signal and establish communication. The control mechanism resembles the role of a higher cortical area in the context of selective attention. To design the control, we use the phase-amplitude reduction of a limit cycle and leverage recent developments in this field in order to find the most effective control strategy regarding a defined cost function. Furthermore, we present results that guarantee the local controllability of the system clos...
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