@article {5661,
	title = {A mechanistic model of early sensory processing based on subtracting sparse representations.},
	journal = {Advances in Neural Information Processing Systems},
	volume = {25},
	year = {2012},
	month = {2012},
	pages = {1979-87},
	abstract = {<p>Early stages of sensory systems face the challenge of compressing information from numerous receptors onto a much smaller number of projection neurons, a so called communication bottleneck. To make more efficient use of limited bandwidth, compression may be achieved using predictive coding, whereby predictable, or redundant, components of the stimulus are removed. In the case of the retina, Srinivasan et al. (1982) suggested that feedforward inhibitory connections subtracting a linear prediction generated from nearby receptors implement such compression, resulting in biphasic center-surround receptive fields. However, feedback inhibitory circuits are common in early sensory circuits and furthermore their dynamics may be nonlinear. Can such circuits implement predictive coding as well? Here, solving the transient dynamics of nonlinear reciprocal feedback circuits through analogy to a signal-processing algorithm called linearized Bregman iteration we show that nonlinear predictive coding can be implemented in an inhibitory feedback circuit. In response to a step stimulus, interneuron activity in time constructs progressively less sparse but more accurate representations of the stimulus, a temporally evolving prediction. This analysis provides a powerful theoretical framework to interpret and understand the dynamics of early sensory processing in a variety of physiological experiments and yields novel predictions regarding the relation between activity and stimulus statistics.</p>
},
	author = {Druckmann, Shaul and Hu, T and Chklovskii, D}
}
@article {6175,
	title = {Super-resolution using sparse representations over learned dictionaries: reconstruction of brain structure using electron microscopy.},
	journal = {arXiv.org },
	year = {2012},
	month = {2012 Oct},
	abstract = {<p>A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.</p>
},
	author = {Hu, T and Nunez-Iglesias, J and Vitaladevuni, S. and Scheffer, L. and Xu, S and Bolorizadeh, M and Hess, H and Fetter, R and Chklovskii, D}
}
