@article {48830,
	title = {Learning enhances sensory and multiple non-sensory representations in primary visual cortex.},
	journal = {Neuron},
	volume = {86},
	year = {2015},
	month = {2015 Jun 17},
	pages = {1478-90},
	abstract = {<p>We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli.</p>
},
	keywords = {Animals, Calcium, Discrimination (Psychology), Female, Learning, Luminescent Proteins, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Models, Neurological, Neurons, Nonlinear Dynamics, Optogenetics, Photic Stimulation, Sensory Receptor Cells, User-Computer Interface, Vision, Ocular, Visual Cortex},
	issn = {1097-4199},
	doi = {10.1016/j.neuron.2015.05.037},
	author = {Poort, Jasper and Khan, Adil G and Pachitariu, Marius and Nemri, Abdellatif and Orsolic, Ivana and Krupic, Julija and Bauza, Marius and Sahani, Maneesh and Keller, Georg B and Mrsic-Flogel, Thomas D and Hofer, Sonja B}
}
@conference {48835,
	title = {Learning visual motion in recurrent neural networks.},
	booktitle = {Neural Information Processing Systems (NIPS 2012)},
	year = {2012},
	month = {12/2003},
	address = {Lake Tahoe},
	abstract = {<p>We present a dynamic nonlinear generative model for visual motion based on a latent representation of binary-gated Gaussian variables. Trained on sequences of images, the model learns to represent different movement directions in different variables. We use an online approximate-inference scheme that can be mapped to the dynamics of networks of neurons. Probed with drifting grating stimuli and moving bars of light, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. Most model neurons also show speed tuning and respond equally well to a range of motion directions and speeds aligned to the constraint line of their respective preferred speed. We show how these computations are enabled by a specific pattern of recurrent connections learned by the model.</p>
},
	url = {http://papers.nips.cc/paper/4814-learning-visual-motion-in-recurrent-neural-networks},
	author = {Pachitariu, Marius and Sahani, Maneesh}
}
