# netpyne.cell.inputs

Module with functions to create patterned spike inputs in cells

Functions:

 `createRhythmicPattern`(params, rand) Creates the ongoing external inputs (rhythmic) input params: - start: time of first spike. `createEvokedPattern`(params, rand[, inc]) creates the ongoing external inputs (rhythmic) input params: - start: time of first spike. `createPoissonPattern`(params, rand) creates external Poisson inputs input params: - start: time of first spike (ms) - stop: stop time; if -1 the full duration (ms) - frequency: standard deviation of start (ms) `createGaussPattern`(params, rand) Creates Gaussian inputs input params: - mu: Gaussian mean - sigma: Gaussian variance
netpyne.cell.inputs.createRhythmicPattern(params, rand)[source]

Creates the ongoing external inputs (rhythmic) input params: - start: time of first spike. if -1, uniform distribution between startMin and startMax (ms) - startMin: minimum values of uniform distribution for start time (ms) - startMax: maximum values of uniform distribution for start time (ms) - startStd: standard deviation of normal distrinution for start time (ms); mean is set by start param. Only used if > 0.0 - freq: oscillatory frequency of rhythmic pattern (Hz) - freqStd: standard deviation of oscillatory frequency (Hz) - distribution: distribution type fo oscillatory frequencies; either ‘normal’ or ‘uniform’ - eventsPerCycle: spikes/burst per cycle; should be either 1 or 2 - repeats: number of times to repeat input pattern (equivalent to number of inputs) - stop: maximum time for last spike of pattern (ms)

netpyne.cell.inputs.createEvokedPattern(params, rand, inc=0)[source]

creates the ongoing external inputs (rhythmic) input params: - start: time of first spike. if -1, uniform distribution between startMin and startMax (ms) - inc: increase in time of first spike; from cfg.inc_evinput (ms) - startStd: standard deviation of start (ms) - numspikes: total number of spikes to generate

netpyne.cell.inputs.createPoissonPattern(params, rand)[source]

creates external Poisson inputs input params: - start: time of first spike (ms) - stop: stop time; if -1 the full duration (ms) - frequency: standard deviation of start (ms)

netpyne.cell.inputs.createGaussPattern(params, rand)[source]

Creates Gaussian inputs input params: - mu: Gaussian mean - sigma: Gaussian variance