directionselectivityNcell_learning_thresh

  DIRECTIONSELECTIVITYNCELL_LEARNING_THRESH

  OUT = DIRECTIONSELECTIVITYNCELL_LEARNING_THRESH

  Observe an N-input process develop direction selectivity

  No inhibition, threshold increases after each trial

  One can also adjust the parameters using: 
  OUT = DIRECTIONSELECTIVITY4CELL_LEARNING_THRESH(PARAM1NAME, PARAM1VALUE, ...)

  The following parameters are adjustable (default value in ()):
  ---------------------------------------------------------------------
  latency (0.200)         |  Latency between 1st and 2nd columns
  lag (0.200)             |  Lag between stim hitting 1st and 2nd rows
  Gmax_max (5e-9)         |  Maximum weights
  Gmax_min (0)            |  Minimum weights
  classic_stdp (1)        |  0/1 use classic stdp
  N (1)                   |  Number of different positions
  R (1)                   |  Number of different latencies
  Gmax_initial (3e-9)     |  1xN*R matrix of initial synaptic weights
  synapseparams           |  synapse parameters
    {'V_rev',-0.080}      |
  intfireparams           |  intfire parameters
  dt (0.0001)             |  time resolution
  trials (100)            |  number of trials
  plot_as_we_go (1)       |  0/1 plot to a figure as we go?
  unidir (1)              |  unidirectional trainin (1) or bi-directional training (2)?
  slow (0)                |  Show stimulus at half speed?
  V_threshold_initial     |  Starting threshold value (volts)
    (-0.055)           
  V_threshold_change      |  Multilpicative change in threshold at each iteration must be <1
    (0.95)
  V_threshold_max (-0.04) |  Ceiling output cell threshold can reach
  mask (1)
  nreps (1)
  phase ([1:N])           |  Phase?