@Article{Fishbach2003,
  author   = {Alon Fishbach and Yehezkel Yeshurun and Israel Nelken},
  journal  = {J Neurophysiol},
  title    = {Neural model for physiological responses to frequency and amplitude transitions uncovers topographical order in the auditory cortex.},
  year     = {2003},
  number   = {6},
  pages    = {3663-78},
  volume   = {90},
  abstract = {We characterize primary auditory cortex (AI) units using a neural
	model for the detection of frequency and amplitude transitions. The
	model is a generalization of a model for the detection of amplitude
	transition. A set of neurons, tuned in the spectrotemporal domain,
	is created by means of neural delays and frequency filtering. The
	sensitivity of the model to frequency and amplitude transitions is
	achieved by applying a 2-dimensional rotatable receptive field to
	the set of spectrotemporally tuned neurons. We evaluated the model
	using data recorded in AI of anesthetized ferrets. We show that the
	model is able to fit the responses of AI units to variety of stimuli,
	including single tones, delayed 2-tone stimuli and various frequency-modulated
	tones, using only a small number of parameters. Furthermore, we show
	that the topographical order in maps of the model parameters is higher
	than in maps created from response indices extracted directly from
	the responses to any single stimulus. These results suggest a possible
	ordered organization of a simple rotatable spectrotemporal receptive
	field in the mammalian AI.},
  doi      = {10.1152/jn.00654.2003},
  keywords = {Acoustic Stimulation, Algorithms, Artificial Intelligence, Auditory Cortex, Brain Mapping, Cluster Analysis, Computer Simulation, Human, Loudness Perception, Models, Neurological, Neurons, Pitch Perception, Reproducibility of Results, Support, U.S. Gov, ', t, Non-P.H.S., 14665682},
}

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The model is a generalization of a model for the detection of amplitude transition. A set of neurons, tuned in the spectrotemporal domain, is created by means of neural delays and frequency filtering. The sensitivity of the model to frequency and amplitude transitions is achieved by applying a 2-dimensional rotatable receptive field to the set of spectrotemporally tuned neurons. We evaluated the model using data recorded in AI of anesthetized ferrets. We show that the model is able to fit the responses of AI units to variety of stimuli, including single tones, delayed 2-tone stimuli and various frequency-modulated tones, using only a small number of parameters. Furthermore, we show that the topographical order in maps of the model parameters is higher than in maps created from response indices extracted directly from the responses to any single stimulus. These results suggest a possible ordered organization of a simple rotatable spectrotemporal receptive field in the mammalian AI.","doi":"10.1152/jn.00654.2003","keywords":"Acoustic Stimulation, Algorithms, Artificial Intelligence, Auditory Cortex, Brain Mapping, Cluster Analysis, Computer Simulation, Human, Loudness Perception, Models, Neurological, Neurons, Pitch Perception, Reproducibility of Results, Support, U.S. Gov, ', t, Non-P.H.S., 14665682","bibtex":"@Article{Fishbach2003,\n author = {Alon Fishbach and Yehezkel Yeshurun and Israel Nelken},\n journal = {J Neurophysiol},\n title = {Neural model for physiological responses to frequency and amplitude transitions uncovers topographical order in the auditory cortex.},\n year = {2003},\n number = {6},\n pages = {3663-78},\n volume = {90},\n abstract = {We characterize primary auditory cortex (AI) units using a neural\n\tmodel for the detection of frequency and amplitude transitions. The\n\tmodel is a generalization of a model for the detection of amplitude\n\ttransition. A set of neurons, tuned in the spectrotemporal domain,\n\tis created by means of neural delays and frequency filtering. The\n\tsensitivity of the model to frequency and amplitude transitions is\n\tachieved by applying a 2-dimensional rotatable receptive field to\n\tthe set of spectrotemporally tuned neurons. We evaluated the model\n\tusing data recorded in AI of anesthetized ferrets. We show that the\n\tmodel is able to fit the responses of AI units to variety of stimuli,\n\tincluding single tones, delayed 2-tone stimuli and various frequency-modulated\n\ttones, using only a small number of parameters. Furthermore, we show\n\tthat the topographical order in maps of the model parameters is higher\n\tthan in maps created from response indices extracted directly from\n\tthe responses to any single stimulus. These results suggest a possible\n\tordered organization of a simple rotatable spectrotemporal receptive\n\tfield in the mammalian AI.},\n doi = {10.1152/jn.00654.2003},\n keywords = {Acoustic Stimulation, Algorithms, Artificial Intelligence, Auditory Cortex, Brain Mapping, Cluster Analysis, Computer Simulation, Human, Loudness Perception, Models, Neurological, Neurons, Pitch Perception, Reproducibility of Results, Support, U.S. Gov, ', t, Non-P.H.S., 14665682},\n}\n\n","author_short":["Fishbach, A.","Yeshurun, Y.","Nelken, I."],"key":"Fishbach2003","id":"Fishbach2003","bibbaseid":"fishbach-yeshurun-nelken-neuralmodelforphysiologicalresponsestofrequencyandamplitudetransitionsuncoverstopographicalorderintheauditorycortex-2003","role":"author","urls":{},"keyword":["Acoustic Stimulation","Algorithms","Artificial Intelligence","Auditory Cortex","Brain Mapping","Cluster Analysis","Computer Simulation","Human","Loudness Perception","Models","Neurological","Neurons","Pitch Perception","Reproducibility of Results","Support","U.S. Gov","'","t","Non-P.H.S.","14665682"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"http://endress.org/publications/ansgar.bib","dataSources":["xPGxHAeh3vZpx4yyE"],"keywords":["acoustic stimulation","algorithms","artificial intelligence","auditory cortex","brain mapping","cluster analysis","computer simulation","human","loudness perception","models","neurological","neurons","pitch perception","reproducibility of results","support","u.s. gov","'","t","non-p.h.s.","14665682"],"search_terms":["neural","model","physiological","responses","frequency","amplitude","transitions","uncovers","topographical","order","auditory","cortex","fishbach","yeshurun","nelken"],"title":"Neural model for physiological responses to frequency and amplitude transitions uncovers topographical order in the auditory cortex.","year":2003}