| | 398 | """ |
| | 399 | Returns a SpikeList whose spikes are a realization of an inhomogeneous gamma process |
| | 400 | (dynamic rate). The implementation uses the thinning method, as presented in the |
| | 401 | references. |
| | 402 | |
| | 403 | Inputs: |
| | 404 | a,b - arrays of the parameters of the gamma PDF where a[i] and b[i] |
| | 405 | will be active on interval [t[i],t[i+1]] |
| | 406 | t - an array specifying the time bins (in milliseconds) at which to |
| | 407 | specify the rate |
| | 408 | t_stop - length of time to simulate process (in ms) |
| | 409 | array - if True, a numpy array of sorted spikes is returned, |
| | 410 | rather than a SpikeList object. |
| | 411 | |
| | 412 | Note: |
| | 413 | t_start=t[0] |
| | 414 | a is a dimensionless quantity > 0, but typically on the order of 2-10. |
| | 415 | a = 1 results in a poisson process. |
| | 416 | b is assumed to be in units of 1/Hz (seconds). |
| | 417 | |
| | 418 | References: |
| | 419 | |
| | 420 | Eilif Muller, Lars Buesing, Johannes Schemmel, and Karlheinz Meier |
| | 421 | Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories |
| | 422 | Neural Comput. 2007 19: 2958-3010. |
| | 423 | |
| | 424 | Devroye, L. (1986). Non-uniform random variate generation. New York: Springer-Verlag. |
| | 425 | |
| | 426 | Examples: |
| | 427 | See source:trunk/examples/stgen/inh_gamma_psth.py |
| | 428 | |
| | 429 | See also: |
| | 430 | inh_poisson_generator, gamma_hazard |
| | 431 | """ |
| | 432 | |