Function to process autocorrelation of neuron list

First estimates autocorrelation of each neuron in the list, then fits an exponential decay function to the estimated autocorrelation, and finally collects and returns summary statistics for each neuron.

Usage

process.autocorr(
  neuron_list,
  bin_count_action = "sum",
  max_lag = 0,
  A0 = 0.001,
  tau0 = 1,
  ctol = 1e-08,
  max_evals = 500,
  check_autofiring_ratio = FALSE,
  print_plots = FALSE,
  plot_time_cutoff = Inf,
  use_raw = TRUE
)

Arguments

neuron_list: An R list of neuron objects.
bin_count_action: Method for counting spikes in each bin when computing autocorrelation; one of "boolean", "mean", or "sum" (default: "sum").
max_lag: Maximum lag (in units of the trial data) to compute autocorrelation; if 0, uses the number of time bins in the neuron's trial data (default: 0).
A0: Initial guess for amplitude parameter of exponential decay function (default: 0.001).
tau0: Initial guess for time constant parameter of exponential decay function (default: 1.0).
ctol: Convergence tolerance for fitting exponential decay function (default: 1e-8).
max_evals: Maximum number of evaluations for fitting exponential decay function (default: 500).
check_autofiring_ratio: Logical indicating whether to check the assumption that autocorrelation values are below the mean firing rate with test.sigma.assumption function (default: FALSE).
print_plots: Logical indicating whether to print autocorrelation plots for each neuron (default: FALSE).
plot_time_cutoff: Maximum lag (in bins) to display on the x-axis of plots (default: Inf).
use_raw: Logical indicating whether to use raw autocorrelation (true) or standard centered and normalized correlation (false) (default: TRUE).

Value

A data frame with one row per neuron and columns for lambda (mean spike rate per ms and per bin), amplitude (A), time constant (tau), bias term, first autocorrelation value, maximum autocorrelation value, mean autocorrelation value, and minimum autocorrelation value.