The relative advantages of sparse versus distributed encoding for associative neuronal networks in the brain

By E. T. Rolls, Alessandro Treves. 1990

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Citations in the corpus

Re-evaluating circuit mechanisms underlying pattern separation (2019, neutral): “As Marr-Albus theory was extended to other brain regions, such ‘ultra-sparse’ population coding formed the core of our current understanding of pattern separation, associative learning, and memory storage (Marr, 1971; Kanerva, 1988; Tsodyks et al., 1988; Rolls and Treves, 1990; Tyrrell and Willshaw,”
Pattern separation, completion, and categorisation in the hippocampus and neocortex. (2016, background): “In order for most associative networks to store information efficiently, heterosynaptic Long Term Depression (as well as LTP) is required (Fazeli & Collingridge, 1996; Rolls, 2008; Rolls & Deco, 2002; Rolls & Treves, 1990, 1998; Treves & Rolls, 1991).”
A computational theory of hippocampal function, and empirical tests of the theory. (2006, background): “Third, in order for most associative networks to store information efficiently, heterosynaptic Long Term Depression (as well as LTP) is required (Rolls, 1996a; Rolls and Treves, 1990; Treves and Rolls, 1991).”
A neural code for three-dimensional object shape in macaque inferotemporal cortex (2008, background): “A sparse object representation based on just a few signals can be more efficiently stored in memory and decoded by other parts of the brai”
Pattern separation in the hippocampus through the eyes of computational modeling (2017, background): “As CA3 receives input form EC directly from the Perforant Path (PP) and indirectly through the DG via the mossy fibers, it was proposed that the DG acts as a “ preprocessor ” by orthogonalizing its inputs before their storage in the CA3 (Rolls & Treves, 1990; Treves & Rolls, 1992).”
Computational analysis of the role of the hippocampus in memory (1994, background): “Estimates of these parameters are very important for defining how many memories could be stored in associative networks in the brain, as shown below and elsewhere (Rolls and Treves, 1990; Treves and Rolls, 1991).”
The mechanisms for pattern completion and pattern separation in the hippocampus (2013, background): “We have shown how this attractor type of network can be extended to have similar properties with diluted connectivity, and also with sparse representations with graded firing rates (Rolls and Treves, 1990; Treves, 1990, 1991; Treves and Rolls, 1991; Webb et al., 2011; Rolls and Webb, 2012).”
The Representation of Information About Taste and Odor in the Orbitofrontal Cortex (2010, methodology): “One is that it is the same measure of sparseness which has proven to be useful and tractable in formal analyses of the capacity of neural networks that use an approach derived from theoretical physics ( see Treves 1990 ; Treves and Rolls 1991 ; Rolls and Treves 1990 ) .”