Modelling the effects of short and random proto-neural elongations (de Wiljes et al 2017)

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"To understand how neurons and nervous systems first evolved, we need an account of the origins of neural elongations: why did neural elongations (axons and dendrites) first originate, such that they could become the central component of both neurons and nervous systems? Two contrasting conceptual accounts provide different answers to this question. Braitenberg's vehicles provide the iconic illustration of the dominant input-output (IO) view. Here, the basic role of neural elongations is to connect sensors to effectors, both situated at different positions within the body. For this function, neural elongations are thought of as comparatively long and specific connections, which require an articulated body involving substantial developmental processes to build. Internal coordination (IC) models stress a different function for early nervous systems. Here, the coordination of activity across extended parts of a multicellular body is held central, in particular, for the contractions of (muscle) tissue. An IC perspective allows the hypothesis that the earliest proto-neural elongations could have been functional even when they were initially simple, short and random connections, as long as they enhanced the patterning of contractile activity across a multicellular surface. The present computational study provides a proof of concept that such short and random neural elongations can play this role. ..."
1 . de Wiljes OO, van Elburg RAJ, Keijzer FA (2017) Modelling the effects of short and random proto-neural elongations. J R Soc Interface [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Gap Junctions:
Simulation Environment: Brian 2; Python;
Model Concept(s): Early evolution;
Implementer(s): de Wiljes, O. O. [otdewiljes at];
# -*- coding: utf-8 -*-
Created on Mon Aug  1 20:39:26 2016

@author: oddman
from SkinBrian import *
import SkinBrianVectorViz
import stripeAnalysis
import os

CPUCount = 3
thisRunFolder = './output/TS/'
# Comic part (manually point to the correct folder and unzip spikefiles)
os.chdir('/set correct absolute dir here/')

# Stripe plot part
tiger = stripeAnalysis.StripeAnalysis(
    coincidenceWindow = 3.,
    angles = 37,
    getFromCache = False,
    fixedRingAmount = [16]
    ,filters = [

#    ,filters = [
#        [['networkParameters','excMikadoDensity'],[.5]],
#        [['networkParameters','length_circumference'],[(128,32)]],
#        ,
#        [['networkParameters','excMikadoDistance'],[0.,2.,4.]]
#    ]
## Perform the stripey visualizations & an analysis visualization
    picTypes = ['boxplot','stripeFig', 'errorbarPlot'],
    normalizeOverallSignificance = True,
    deviancePerRow = True,
    shiftAnalysis = False,
    shiftSavgol = False,
    savgol = None,
#    limitPic = (150,80),
    stripePlotScale = 'indiv'
    , errorbarDimension =  ['networkParameters','excMikadoDensity'] # 'perBinNorm'#

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