ArticleNeural networks : the official journal of the International Neural Network Society2009
Nonlinear modeling of neural population dynamics for hippocampal prostheses.
Article in Neural networks : the official journal of the International Neural Network Society, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Integrating neural decoding, memristive materials, and adaptive control frameworks for next-generation hippocampal memory prosthetics.iScience · 2026Review
- A spatially discretized convolutional neural mass model for studying meso-scale spatio-temporal transformations in the rat hippocampus.Journal of computational neuroscience · 2026Article
- A spatially discretized convolutional neural mass model for studying meso-scale spatio-temporal transformations in the rat hippocampus.Research square · 2026Article
- ReBaCCA-ss: Relevance-Balanced Continuum Correlation Analysis With Smoothing and Surrogating for Quantifying Similarity Between Population Spiking Activities.Neural computation · 2026Article
- Article
- Patterned Hippocampal Stimulation Facilitates Memory in Patients With a History of Head Impact and/or Brain Injury.Frontiers in human neuroscience · 2022Article
- A Double-Layer Multi-Resolution Classification Model for Decoding Spatiotemporal Patterns of Spikes With Small Sample Size.Neural computation · 2021Article
- Stimulation and Recording of the Hippocampus Using the Same Pt-Ir Coated Microelectrodes.Frontiers in neuroscience · 2021Article
- Multi-Input, Multi-Output Neuronal Mode Network Approach to Modeling the Encoding Dynamics and Functional Connectivity of Neural Systems.Neural computation · 2019Article
- A Multi-Channel Asynchronous Neurostimulator With Artifact Suppression for Neural Code-Based Stimulations.Frontiers in neuroscience · 2019Article
- Memory Prosthesis: Is It Time for a Deep Neuromimetic Computing Approach?Frontiers in neuroscience · 2019Article
- Sparse Large-Scale Nonlinear Dynamical Modeling of Human Hippocampus for Memory Prostheses.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2018Article
- A cognitive prosthesis for memory facilitation by closed-loop functional ensemble stimulation of hippocampal neurons in primate brain.Experimental neurology · 2017Article
- Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank.Frontiers in neuroscience · 2017Article
- Tracking recurrence of correlation structure in neuronal recordings.Journal of neuroscience methods · 2017Article
- Hippocampal closed-loop modeling and implications for seizure stimulation design.Journal of neural engineering · 2015Article
- Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling.Journal of neuroscience methods · 2015Article
- Model-based asessment of an in-vivo predictive relationship from CA1 to CA3 in the rodent hippocampus.Journal of computational neuroscience · 2015Article
- On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes.Journal of computational neuroscience · 2014Article
- Extraction and restoration of hippocampal spatial memories with non-linear dynamical modeling.Frontiers in systems neuroscience · 2014Article
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Authors and funding
6 authors.
Funding
Abstract
Developing a neural prosthesis for the damaged hippocampus requires restoring the transformation of population neural activities performed by the hippocampal circuitry. To bypass a damaged region, output spike trains need to be predicted from the input spike trains and then reinstated through stimulation. We formulate a multiple-input, multiple-output (MIMO) nonlinear dynamic model for the input-output transformation of spike trains. In this approach, a MIMO model comprises a series of physiologically-plausible multiple-input, single-output (MISO) neuron models that consist of five components each: (1) feedforward Volterra kernels transforming the input spike trains into the synaptic potential, (2) a feedback kernel transforming the output spikes into the spike-triggered after-potential, (3) a noise term capturing the system uncertainty, (4) an adder generating the pre-threshold potential, and (5) a threshold function generating output spikes. It is shown that this model is equivalent to a generalized linear model with a probit link function. To reduce model complexity and avoid overfitting, statistical model selection and cross-validation methods are employed to choose the significant inputs and interactions between inputs. The model is applied successfully to the hippocampal CA3-CA1 population dynamics. Such a model can serve as a computational basis for the development of hippocampal prostheses.
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