Exploring how biological constraints shape neural encoding. Computational neuroscientist. PhD @bhalla_lab @NCBS_Bangalore https://xitter.cf/t.co/5qUOeDQGqH

Joined May 2010
In addition to elucidating the mechanisms underlying DBLO, which will result in more accurate and reliable models, we also provide a novel way to characterize a neuron’s morphological and electrotonic characteristics via its wide-spectrum impedance amplitude profile. 10/n
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Combining these insights leads to models with experimentally high DBLO 9/n
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Trivial mechanisms such as passive charging of neurons, spikes relayed from the axon initial segment, or high reversal potential of the principal potassium channel do not hold. 8/n
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Dendrites contribute a depolarizing current during the repolarization phase of an action potential that raises the DBLO. Rapid potassium channel kinetics and appropriate sodium channel kinetics also result in high DBLO. 7/n
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We found that many studies were ignoring liquid junction potential (LJP) correction, in the absence of which the sodium channels do not recover from inactivation after a single spike. 6/n
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DBLO interacts with various other features such as calcium influx and bistable firing. Incorrect DBLO in models thus affects their realism and predictive power. 5/n
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The mechanism behind this high DBLO are poorly understood. Many published models of CA1 pyramidal neurons do not match experimental DBLO. 4/n
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Many neurons especially pyramidal neurons do not show this prominent after-hyperpolarization and instead show a high depolarization baseline offset (DBLO): 3/n
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We are accustomed to seeing the following image of an action potential with a very strong after-hyperpolarization thanks to the seminal work by Hodgkin and Huxley: 2/n
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