parametric Modeling of the Temporat Dynamics of Neuronal

tr t . We describe an exploratory approach to the parametric modeling of dynamical (time-varying) neurophy^siological data' The modeb use stimulus data from a window of time to predict thre neuronal firing rate at the end ofthatwindow. The mostsuccessful models were IEedforwardthree-layered networks of input, hidden, and output "nodes" connected by weights that were adjusted during a training phase by the backpropagationalgorithm' The memoi i" ttt.ti -odels was represented by dglav lines of vTr1B teigtr propagating activation between the layers' Connectionist moAedwittr no memory (1 sequential node per layer) as well,as zero-memory nonlinear nonconnectionist models were also tested. 2. Models were tested with recordings of neuronal activity from the auditory thalamic nucleus ovoidalis of urethane-anesthetized zebra finches (Taeniopygia guttata)All cells reported here showed phasic/tonic responses. Extensive modeling of one neuron (cetl t) defined a "canonical" architecture, which was most successful in modeling this cell. The canonical model had a zeromemory input layer, a hidden layer with 29 bins representing 185.6 ms, and a single output node whose value as a function of sequential bin position represented the output ofthe neuron as a function of time. The canonical model achieved convergence on the entire data set for cell 1, including responses to single tone