This is the README file for simulation codes associated with:
Hiratani N, Fukai T (2018) Redundancy in synaptic connections enables neurons to learn optimally. Proc Natl Acad Sci U S A
The codes were created based on the L2/3 cell model in Smith et al. (2013) (http://www.opensourcebrain.org/projects/l23dendriticspikes).
This code was contributed by N Hiratani and questions should be addressed to n.hiratani"at"gmail.com
"neuron_simulation.py" is the main simulation code from which Figure 3-5 in the manuscript were generated. To run the simulation, first type "nrnivmodl" for compiling the mod files, then run "neuron_simulation.py" as a standard python program. The program receives six parameters from the standard input as below:
Kin: The number of synapses per connection. Here, the total number of presynaptic neurons is fixed at Min = 200, so that the total number of synaptic connections is given as Nin = Kin*Min.
gmax: The standard excitatory conductance. The synaptic weight of the i-th synaptic contact was defined by gmax*uks[i].
gI: The standard inhibitory conductance. All inhibitory weights were set to gI.
uk_min: Minimum value of the spine size.
sd_bias: Bias in synaptic distribution. If sd_bias < 1.0, then the synaptic distribution is biased towards the proximal side and vice versa.
release_probability: Release probability of synaptic vesicle at excitatory synapses.
Then, the program generates (some of) following output files:
nrn_simul_vkappas_... : Te value of the unit EPSP at each dendritic section. nrn_simul_perf_... : EPSP heights and areas for test stimuli. nrn_simul_duvk_... : Values of synaptic weight uk and unit EPSP vk. nrn_simul_mbps/nrn_simul_mbpf : The somatic membrane dynamics before/after learning.Example Run: