networkspikes             package:sjemea             R Documentation

_C_o_m_p_u_t_e _n_e_t_w_o_r_k _s_p_i_k_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Compute the network spikes in an MEA recording, by averaging over
     all the electrodes in the array.

_U_s_a_g_e:

     s$ns <- compute.ns(s, ns.T=0.003, ns.N=10,sur=100)
     show.ns(ns, ...)

_A_r_g_u_m_e_n_t_s:

       s: MEA data structure

    ns.T: Bin width (in msec) for counting spikes.

    ns.N: Threshold number of active electrodes required to make
          network spike

     sur: How many bins either side of peak to retain when computing
          the mean network spike

      ns: A network spike data structure, returned by 'compute.ns'

     ...: Other plot arguments to pass to 'show.ns'

_D_e_t_a_i_l_s:

     To see the mean network spikes after they have computed, just look
     at the mean object.

     If you wish to see the individual network spikes, try show.ns(ns,
     ...) where the remaining args are passed to the plot function.

_V_a_l_u_e:

     A list with the following elements: 

  counts: vector giving the number of active electrodes in each bin;
          this can be very long!

    ns.N: The value of ns.N used.

    ns.T: the value of ns.T used.

    mean: The profile of the mean network spike (this is a time series
          object)

measures: If N network spikes were found, this is a matrix with N rows,
          one per network spike.

   brief: A short vector summarizing the network spikes.

_A_u_t_h_o_r(_s):

     Stephen Eglen

_R_e_f_e_r_e_n_c_e_s:

     Eytan and Marom (2006) J Neuroscience.

_S_e_e _A_l_s_o:

     'sanger.read.spikes'

_E_x_a_m_p_l_e_s:

     data.file <- system.file("examples", "TC89_DIV15_A.nexTimestamps",
                              package = "sjemea")
     s <- sanger.read.spikes( data.file, beg=400, end=700)
     s$ns <- compute.ns(s, ns.T=0.003, ns.N=10,sur=100)
     plot(s$ns)
     plot(s$ns, xlim=c(450, 500))

     plot(s$ns$mean)                         #show mean NS.
     summary(s$ns)
     s$ns$brief

