Parametric modeling of the temporal dynamics of neuronal responses using connectionist architectures.
1. We describe an exploratory approach to the parametric modeling of dynamical (time-varying) neurophysiological data. The models use stimulus data from a window of time to predict the neuronal firing rate at the end of that window. The most successful models were feedforward three-layered networks of input, hidden, and output "nodes" connected by weights that were adjusted during a training phase by the backpropagation algorithm. The memory in these models was represented by delay lines of varying length propagating activation between the layers. Connectionist models with 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 (cell 1) defined a "canonical" architecture, which was most successful in modeling this cell. The canonical model had a zero-memory 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 of the neuron as a function of time. The canonical model achieved convergence on the entire data set for cell 1, including responses to single tone bursts and zebra finch songs. The "frequency" weights of the canonical model matched well excitatory and inhibitory frequencies for cell 1 as determined by the cell's frequency tuning curve. The "memory" weights of the canonical model were dominated by excitation over the first 25 ms followed by inhibition. 3. When trained with only the dynamical responses to tone bursts, the canonical model also accurately predicted the responses to song (average R2 = 0.823). Thus for this neuron the responses to single tone bursts were sufficient to predict most of the responses to six different songs, each presented at three different amplitudes, although a Monte Carlo procedure indicated the residual variance was not just due to noise (P < 0.001). 4. The model's ability to predict the responses to song was further explored by altering the tone burst (training) data. For cell 1, the responses to songs were most strongly related to the phasic/tonic details of the temporal responses to tone bursts. Changes in the characteristic frequency, frequency tuning curves, and rate/intensity function of the neuron had less effect. These experiments would be difficult or impossible to conduct electrophysiologically.(ABSTRACT TRUNCATED AT 400 WORDS)
Rand Corporation, Santa Monica, California 90407-2138.
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Bankes
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Margoliash
D000818:Animals; D001306:Auditory Pathways / Q000502:physiology; D001717:Birds; D001931:Brain Mapping; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D010897:Pitch Discrimination / Q000502:physiology; D011930:Reaction Time / Q000502:physiology; D009435:Synaptic Transmission / Q000502:physiology*; D013787:Thalamic Nuclei / Q000502:physiology; D014828:Vocalization, Animal / Q000502:physiology