Reconciling Predictive Coding and Biased Competition Models of Cortical Function

By Michael W. Spratling. 2008

162 citations recorded by the source corpus.

A simple variation of the standard biased competition model is shown, via some trivial mathematical manipulations, to be identical to predictive coding. Specifically, it is shown that a particular implementation of the biased competition model, in which nodes compete via inhibition that targets the inputs to a cortical region, is mathematically equivalent to the linear predictive coding model. This observation demonstrates that these two important and influential rival theories of cortical function are minor variations on the same underlying mathematical model.

Subfields: Cognitive Neuroscience

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Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, neutral): “By hypothesis, predictions codes are more precise, more computationally efficient, and less noisy than error codes (Friston, 2005; Jehee and Ballard, 2009; Rao and Ballard, 1999; Spratling, 2008).”
A review of predictive coding algorithms. (2017, methodology): “…Coding (PC; Rao & Ballard, 1999) reformulated to make it compatible with Biased Competition (BC) theories of cortical function (Spratling, 2008a, 2008b), and that is implemented using Divisive Input Modulation (DIM; Spratling, De Meyer, & Kompass, 2009) as the method for updating error and…”
How Do Expectations Shape Perception? (2018, background): “Different versions of predictive coding have been developed [117], differing primarily in how the error is computed (by subtraction or division) and in how prediction and prediction error neurons are connected [118].”
How Prediction Errors Shape Perception, Attention, and Motivation (2012, background): “While this scheme differs in some respects to implementations suggested previously (Rao and Ballard, 1999; Friston, 2005), it has been shown to be mathematically equivalent under some simplifying assumptions (Spratling, 2008) and relies on the same general principle of inhibition of PE units by P un”
Canonical microcircuits for predictive coding. (2012, unknown): “…suppression of responses to predictable stimuli can coexist with (top-down) attentional enhancement of evoked processing (Wyart et al., 2012): in predictive coding, attention is mediated by increasing the gain of populations encoding prediction error (Spratling, 2008; Feldman and Friston, 2010).”
An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity (2017, background): “Thus, to make the predictive coding network in accordance with this, the sign of the top-down input needs to be inverted by local inhibitory neurons (Spratling, 2008).”
Attention Reverses the Effect of Prediction in Silencing Sensory Signals (2011, unknown): “This may reflect faster resolution of prediction error within V1, consistent with recent computational models of predictive coding (Spratling 2008, 2010).”
Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback (2014, background): “The difference between these model frameworks is in the combination of FF and FB signals, which turn out to be two variants of the same generic principle in case of additive signal convergence (Spratling & Johnson, 2004; Spratling, 2008).”
Prediction, Cognition and the Brain (2009, background): “However, it has recently been demonstrated that this may not be the case, as the predictive coding model can be considered mathematically equivalent with a particular form of biased competition model of attention (Spratling, 2008a,b).”
Concurrent Repetition Enhancement and Suppression Responses in Extrastriate Visual Cortex (2012, background): “Cerebral Cortex September 2013, V 23 N 9 2241”
Predictive Coding as a Model of Response Properties in Cortical Area V1 (2010, methodology): “A value of equal to 1 has been used in previous work (Spratling, 2008a; De Meyer and Spratling, 2009; Spratling et al., 2009).”
The Limits of Feedforward Vision: Recurrent Processing Promotes Robust Object Recognition when Objects Are Degraded (2012, background): “…feedback in the brain is exclusively excitatory, and although there are ways to address the biological implausibility of predictive coding (e.g., Spratling, 2008), perhaps the larger issue is that it predicts that degradations that underspecify stimuli like the ones used in the present research…”