@article {66025,
	title = {Internal state configures olfactory behavior and early sensory processing in  larvae.},
	journal = {Science Advances},
	volume = {7},
	year = {2021},
	month = {2021 Jan 01},
	abstract = {<p>Animals exhibit different behavioral responses to the same sensory cue depending on their internal state at a given moment. How and where in the brain are sensory inputs combined with state information to select an appropriate behavior? Here, we investigate how food deprivation affects olfactory behavior in larvae. We find that certain odors repel well-fed animals but attract food-deprived animals and that feeding state flexibly alters neural processing in the first olfactory center, the antennal lobe. Hunger differentially modulates two output pathways required for opposing behavioral responses. Upon food deprivation, attraction-mediating uniglomerular projection neurons show elevated odor-evoked activity, whereas an aversion-mediating multiglomerular projection neuron receives odor-evoked inhibition. The switch between these two pathways is regulated by the lone serotonergic neuron in the antennal lobe, CSD. Our findings demonstrate how flexible behaviors can arise from state-dependent circuit dynamics in an early sensory processing center.</p>
},
	issn = {2375-2548},
	doi = {10.1126/sciadv.abd6900},
	author = {Vogt, Katrin and Zimmerman, David M and Schlichting, Matthias and Hernandez-Nunez, Luis and Qin, Shanshan and Malacon, Karen and Rosbash, Michael and Pehlevan, Cengiz and Cardona, Albert and Samuel, Aravinthan D T}
}
@conference {65046,
	title = {A Hebbian/Anti-Hebbian network derived from online non-negative matrix factorization can cluster and discover sparse features.},
	booktitle = {2014 48th Asilomar Conference on Signals, Systems and Computers2014 48th Asilomar Conference on Signals, Systems and Computers},
	year = {2014},
	month = {2015 Mar 02},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Pacific Grove, CA, USA},
	abstract = {<p>Olshausen and Field (OF) proposed that neural computations in the primary visual cortex (V1) can be partially modelled by sparse dictionary learning. By minimizing the regularized representation error they derived an online algorithm, which learns Gabor-filter receptive fields from a natural image ensemble in agreement with physiological experiments. Whereas the OF algorithm can be mapped onto the dynamics and synaptic plasticity in a single-layer neural network, the derived learning rule is nonlocal - the synaptic weight update depends on the activity of neurons other than just pre- and postsynaptic ones {\textendash} and hence biologically implausible. Here, to overcome this problem, we derive sparse dictionary learning from a novel cost-function - a regularized error of the symmetric factorization of the input{\textquoteright}s similarity matrix. Our algorithm maps onto a neural network of the same architecture as OF but using only biologically plausible local learning rules. When trained on natural images our network learns Gabor-filter receptive fields and reproduces the correlation among synaptic weights hard-wired in the OF network. Therefore, online symmetric matrix factorization may serve as an algorithmic theory of neural computation.\&nbsp;</p>
},
	doi = {10.1109/ACSSC.2014.7094553},
	url = {http://ieeexplore.ieee.org/document/7094553/http://xplorestaging.ieee.org/ielx7/7066991/7094373/07094553.pdf?arnumber=7094553},
	author = {Pehlevan, Cengiz and Chklovskii, Dmitri B.}
}
