Abstract
Dendritic non-linearities increase neurons’ computation capacity, turning them intocomplex computing units [4]. However, network studies are usually based on point neuron models that do not incorporate dendrites and their non-linearities. In contrast, the study replicated here [2] uses a simple point-neuron model that contains an effective description of dendrites by a non-linear summation of its excitatory synaptic input. Due to the simplicity of the model, both large-scale parameter exploration of a medium-sized network, as well as an analytical investigation of its properties are feasible. The original study was based on simulation and analysis code in C and Mathematica, but this code is not publicly available. Here, we replicate the study using the neural simulator Brian 2 [1,6], a simulator based on the Python language that has become a common choice in computational neuroscience [3]. This simulator offers a good trade-off between flexibility and performance and is therefore a suitable choice for this study of a non-standard neuron model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: HAL (Le Centre pour la Communication Scientifique Directe)
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
