Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.neunet.2009.05.004 | Neural Networks |
Keywords | Field | DocType |
Hippocampus,Spike,Spatio-temporal pattern,Volterra kernel,Feedback,Multiple-input multiple-output system | Kernel (linear algebra),Population,Linear model,Control theory,Input/output,Statistical model,Overfitting,Artificial neural network,Mathematics,Feed forward | Journal |
Volume | Issue | ISSN |
22 | 9 | 0893-6080 |
Citations | PageRank | References |
11 | 0.84 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Song | 1 | 202 | 34.25 |
| Rosa H M Chan | 2 | 182 | 22.79 |
| vasilis z marmarelis | 3 | 219 | 29.17 |
| Robert E Hampson | 4 | 105 | 12.12 |
| Sam A Deadwyler | 5 | 98 | 10.89 |
| theodore w berger | 6 | 380 | 87.26 |