The selectivity of canary HVC neurons for the Bird's Own Song: rate coding, temporal coding, or both?
The neuronal selectivity observed in the avian song system for the Bird's Own Song progressively emerged as an extraordinary fruitful model to investigate the neural code underlying the recognition of complex stimuli and the occurrence of learned behaviors. In adult zebra finch, neurons from the HVC (used as a proper name) show very selective auditory responses, firing more to presentation of the Bird's Own Song (BOS) than to reverse BOS or other conspecific songs. However, as adult zebra finches always produce the same stereotyped song, the presence of such highly selective neurons in birds with larger repertoire still remains an open question. Data presented here show that neurons selective for the BOS can be found in adult canary, a seasonal breeding bird which display a large repertoire. More precisely, we found that a large proportion of neurons (29/36) exhibits higher responses to presentation of the forward than to the reverse BOS, and that 22% of the cells were identified as selective on the basis of the d' value. For a cell that was extensively studied, we evaluated to what extent temporal stimulus-related structure predicts the acoustic stimulus using linear or non-linear artificial neural networks (ANN). These analyses indicated that the temporal structure contained in spike trains characterizes more accurately the stimulus than the firing rate. The limitations of applying ANN analyses to electrophysiological data are discussed and potential solutions to increase the confidence in these analysis are proposed.
Huetz
Chloé
C
NAMC, UMR CNRS 8620, Bat 446, Université Paris-Sud, Orsay, France.
Del Negro
Catherine
C
Lehongre
Katia
K
Tarroux
Philippe
P
Edeline
Jean-Marc
JM
D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001307:Auditory Perception / Q000502:physiology*; D002171:Canaries / Q000502:physiology*; D003627:Data Interpretation, Statistical; D007858:Learning / Q000502:physiology; D008297:Male; D016571:Neural Networks, Computer; D009475:Neurons, Afferent / Q000502:physiology*; D046709:Pattern Recognition, Physiological / Q000502:physiology*; D013997:Time Factors; D014828:Vocalization, Animal / Q000502:physiology*