Transcript Hatfield
Lauren Jones, Alfredo Fontanini, Brian Sadacca, Paul Miller, and Donald Katz Lauren Jones – Ph.D. ◦ Studied the rodent whisker somatosensory system at University of Maryland School of Medicine Alfredo Fontanini – MD, Ph.D. ◦ Previous research at Caltech in the olfactory cortex Brian Sadacca – Ph.D. Student ◦ Graduated from University of Pittsburgh where he studied the vestibular system Paul Miller – Volen Center for Complex Systems Donald Katz – Lab at Brandeis University, MA •Female rats anesthetized •Microelectrodes inserted bilaterally into the gustatory cortex along with intraoral cannulae •Rats received 40 µl of 100 mM NaCl, 100 mM sucrose, 100 mM citric acid, or 1 mM quinone HCl •Neuron considered a taste neuron if response was different for at least one taste (38%) •Hidden Markov Models (HMM) •Detect coherent rate patterning in populations of simultaneously recorded neurons •Peristimulus Time Histograms (PSTHs) •Across trial averages – sequentially recorded neurons PSTHs •In pairs of trials with similar response magnitudes, variability is still high. State Sequences •Progression through 3 – 4 firing rate states •Brief transitions not identified as a certain state •Transition from one state to another is a result of the coordinated activity of many neurons •During transition, 51% of neurons per ensemble changed firing rates •Timing of states may change but the sequence remains the same •States are stimulus specific •Gradual rate changes should not increase the duration of transitions •Trial Shuffled and Trial/Taste Shuffled is much slower than the original or simulated data •Fast change of state in sequences is characteristic of ensemble sensory responses Trial/Taste Shuffling •Trial shuffling reduces correctly identified trials Conclusions •State sequences were reliable and stimulus specific •States were recognizable only with simultaneously recorded ensembles •State sequences provide more information than averages •PSTHs obscure the rapid transitions observed in ensemble analysis •“Sensory neurons act as parts of a systemslevel dynamic process.”