Distributed hierarchical processing in the primate cerebral cortex.

By D. Felleman, D. C. Essen. 1991

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Citations in the corpus

Perceiver: General Perception with Iterative Attention (2021, background): “There is widespread evidence that it plays an important role in human vision (e.g. Felleman & Essen 1991; Olshausen et al. 1993; Lollo et al. 2000), which is characterized by limited bandwidth input streams (Wolfe et al., 2006).”
Deep learning in neural networks: An overview (2014, neutral): “…Rossi, & Desimone, 2005; Connor, Brincat, & Pasupathy, 2007; Desimone, Albright, Gross, & Bruce, 1984; DiCarlo, Zoccolan, & Rust, 2012; Felleman & Van Essen, 1991; Hubel & Wiesel, 1968; Hung, Kreiman, Poggio, & DiCarlo, 2005; Kobatake & Tanaka, 1994; Kriegeskorte et al., 2008; Lennie & Movshon,…”
Convolution by Evolution: Differentiable Pattern Producing Networks (2016, background): “These architectures were historically inspired by the structure of the mammalian visual system, namely the hierarchical structure of the ventral stream for visual processing [5] and the local, tiled nature of “receptive ﬁelds” in the primary visual cortex [10].”
The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions (2015, methodology): “General algorithmic procedure for plotting and sorting the behavioral databases upon connectivity matrices can be outlined in four steps: (1) Compute connectivity order, (2) Compute behavioral element frequencies, (3) Bin connectivity ordering, (4) Regress behavioral element frequency across bins, (”
Deep Networks with Internal Selective Attention through Feedback Connections (2014, background): “Felleman and Van Essen [36] constructed a (now famous) hierarchy diagram of 32 different visual cortical areas in macaque visual cortex.”
Human Pose Estimation with Iterative Error Feedback (2015, background): “Feature computation in these models is purely feedforward, however, unlike in the human visual system where feedback connections abound [11, 21, 22].”
Neurogenesis deep learning: Extending deep networks to accommodate new classes (2016, background): “Class conditional sampling from trained networks has biological inspiration [21]-[24] as well as historical and artificial neural network implementations [25]-[28].”
Conjunctive representations in learning and memory: principles of cortical and hippocampal function. (2001, background): “Second, Gallagher and Holland (1992) reported that rats with damage to the hippocampai formation were not impaired on an ambiguous feature problem, AC+, B+, AB—, C—, that is very similar to negative patterning (A + , B+, AB—).”
Network hubs in the human brain. (2013, background): “These regions are capable of complex and diverse responses (multimodal or transmodal regions [14]), are placed at higher levels within a cortical hierarchy [15], and represent focal points of convergence or divergence of more specialized neural information (‘conﬂuence zones’ [16,17]).”