The cost of cortical computation.

By P. Lennie. 2003

1,089 citations recorded by the source corpus.

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In Cognitive Neuroscience

Citations in the corpus

Selfless Sequential Learning (2018, background): “Yet at any given time, information is represented by only a few active neurons resulting in a sparsity of 90-95% (Lennie, 2003).”
Visual attention: The past 25 years (2011, background): “These energy limitations, which allow only a small fraction of the machinery to be engaged concurrently, provide a neurophysiological basis for the idea that selective attention arises from the brain’s limited capacity to process information (Lennie, 2003).”
Modeling the hemodynamic response to brain activation. (2004, neutral): “Theoretical analyses of the energy budget for neuronal signaling provide some support for this picture as well (Attwell and Laughlin, 2001; Lennie, 2003).”
Memory Capacities for Synaptic and Structural Plasticity (2010, background): “First, the principal cost of neural signaling appears to be restoring and maintaining ionic balances following postsynaptic potentials (Lennie, 2003; Laughlin & Sejnowski, 2003; Attwell & Laughlin, 2001).”
The Packet Switching Brain (2011, neutral): “6 Indeed, sparseness itself is thought to be required for metabolic and other efficiency-related reasons (Waydo, Kraskov, Quian Quiroga, Fried, & Koch, 2006; Lennie, 2003; Attwell & Laughlin, 2001).”
Bayesian just-so stories in psychology and neuroscience. (2012, methodology): “A good illustration of this approach is offered by McKenzie (2003), who described a number of instances in which performance in the laboratory appears to deviate from optimality but where a rational model with the appropriate assumptions can explain behavior.”
Neural Associative Memory with Optimal Bayesian Learning (2011, background): “More recent work considers also structural compression of synaptic networks and the energy or time requirements per retrieval (Poirazi & Mel, 2001; Stepanyants, Hof, & Chklovskii, 2002; Lennie, 2003; Knoblauch, 2003, 2005, 2009b; Knoblauch, Palm, & Sommer, 2010).”
Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony? (2012, background): “Rate coding with Poissonian firing statistics (black lines) can also induce strong LTP, however, only at the price of much higher firing rates and, thus, much higher energy expenditures (Attwell and Laughlin, 2001; Laughlin and Sejnowski, 2003; Lennie, 2003)8.”
Visual adaptation: neural, psychological and computational aspects. (2007, background): “Sharpee, Rust, & Bialek, 2004) and establishing the significance of potential constraints such as the metabolic cost of neuronal activity (Laughlin, de Ruyter van Steveninck, & Anderson, 1998; Lennie, 2003).”
Efficient Learning of Sparse Representations with an Energy-Based Model (2006, background): “Furthermore , there is considerable evidence that biological vision uses sparse representations in early visual areas [5] [6].”
On the biological plausibility of grandmother cells: implications for neural network theories in psychology and neuroscience. (2009, background): “Indeed, it is argued that metabolic efficiency constraints alone rule out dense distributed coding schemes (Lennie, 2003).”
Adaptation and visual coding (2011, background): “It has the advantage of increasing metabolic efficiency (Lennie, 2003) and may also aid the visual system in detecting novelty (Barlow, 1990a; Gardner et al., 2005; Ranganath & Rainer, 2003).”
Learning Deep Architectures for AI (2007, background): “Neurons in the cortex are believed to have a distributed and sparse representation (Olshausen & Field, 1997), with around 1-4% of the neurons active at any one time (Attwell & Laughlin, 2001; Lennie, 2003).”