English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

To understand the behavior of a neural circuit it is a presupposition that wehave a model of the dynamical system describing this circuit. This model isdetermined by several parameters, including not only the synaptic weights, butalso the parameters of each neuron. Existing works mainly concentrate on eitherthe synaptic weights or the neural parameters. In this paper we present analgorithm to reconstruct all parameters including the synaptic weights of aspiking neuron model. The model based on works of Eugene M. Izhikevich(Izhikevich 2007) consists of two differential equations and covers differenttypes of cortical neurons. It combines the dynamical properties ofHodgkin-Huxley-type dynamics with a high computational efficiency. Thepresented algorithm uses the recordings of the corresponding membranepotentials of the model for the reconstruction and consists of two maincomponents. The first component is a rank based Genetic Algorithm (GA) which isused to find the neural parameters of the model. The second one is a Least MeanSquares approach which computes the synaptic weights of all interconnectedneurons by minimizing the squared error between the calculated and the measuredmembrane potentials for each time step. In preparation for the reconstructionof the neural parameters and of the synaptic weights from real measuredmembrane potentials, promising results based on simulated data generated with arandomly parametrized Izhikevich model are presented. The reconstruction doesnot only converge to a global minimum of neural parameters, but alsoapproximates the synaptic weights with high precision.

Reconstructing Neural Parameters and Synapses of arbitrary interconnected Neurons from their Simulated Spiking Activity