A review of predictive coding algorithms

Spratling, Michael W.

doi:10.1016/j.bandc.2015.11.003

Cited by 351 publications

(295 citation statements)

References 51 publications

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“…This notion can be traced back to seminal work by Helmholtz (1878). In the framework of predictive coding, these ideas are formalised as the interplay of (bottom-up) sensory inputs and (top-down) inferences, described in computational models with plausible neural substrates (Bastos et al, 2012;Spratling, 2017). In particular, these models describe how top-down predictions or inferences are matched to incoming sensory inputs across different levels of the cortical hierarchy.…”

Section: A Neurally Inspired Perspective On the Interpretation Of Cismentioning

confidence: 99%

Visualising mental representations: A primer on noise-based reverse correlation in social psychology

Brinkman

Todorov

Dotsch

2017

European Review of Social Psychology

152

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With the introduction of the psychophysical method of reverse correlation, a holy grail of social psychology appears to be within reach -visualising mental representations. Reverse correlation is a data-driven method that yields visual proxies of mental representations, based on judgements of randomly varying stimuli. This review is a primer to an influential reverse correlation approach in which stimuli vary by applying random noise to the pixels of images. Our review suggests that the technique is an invaluable tool in the investigation of social perception (e.g., in the perception of race, gender and personality traits), with ample potential applications. However, it is unclear how these visual proxies are best interpreted. Building on advances in cognitive neuroscience, we suggest that these proxies are visual reflections of the internal representations that determine how social stimuli are perceived. In addition, we provide a tutorial on how to perform reverse correlation experiments using R.

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Section: A Neurally Inspired Perspective On the Interpretation Of Cismentioning

confidence: 99%

Visualising mental representations: A primer on noise-based reverse correlation in social psychology

Brinkman

Todorov

Dotsch

2017

European Review of Social Psychology

152

View full text Add to dashboard Cite

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“…Whilst the various algorithms implementing these ideas may differ in their computational strategy (Spratling, 2016), they share a common set of tenets: that the brain intrinsically generates a model of the world in which it finds itself (both the external and internal milieu) which is refined – but not driven – by sensory data. That model is a ‘prediction’ in the sense that it is a guess; a best guess or a Bayesian optimal estimate based simultaneously on both sensory data and prior experience.…”

Section: Introductionmentioning

confidence: 99%

Neural Elements for Predictive Coding

2016

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Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural engineering in the mouse. The exercise highlights a number of recurring themes, amongst them the consideration of interneuron diversity as a spur to theoretical development and the potential for specifying a pyramidal neuron’s function by its individual ‘connectome,’ combining its extrinsic projection (forward, backward or subcortical) with evaluation of its intrinsic network (e.g., unidirectional versus bidirectional connections with other pyramidal neurons).

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“…However, the precise computational procedures employed by these algorithms as well as their connections to neuronal populations are controversial and vary widely across different studies (Spratling, 2016; Bastos et al, 2012; Bogacz, 2015; Rao & Ballard, 1999; Mumford, 1992; Spratling, 2008). …”

Section: Discussionmentioning

confidence: 99%

“…For example, inhibitory feedback connection implemented in the model proposed by Rao & Ballard (1999), is modified in Spratling (2008, 2016) to reflect excitatory feedback signals observed in physiology. In order to avoid negative responses, Spratling (Spratling, 2008, 2016) also replaced additive excitation and subtractive inhibition in Rao & Ballard (1999) by multiplicative and divisive modulations, respectively. In our model, we follow the approach in Rao & Ballard (1999) and implement additive excitation and subtractive inhibition for simplicity.…”

Section: Discussionmentioning

confidence: 99%

“…Predictive coding has been proposed as a method to create efficient neural codes, and has successfully described neural responses in a variety of different sensory systems (Bogacz, 2015; Bastos et al, 2012; Friston & Kiebel, 2009a, b; Srinivasan et al, 1982; Rao & Ballard, 1999; Spratling, 2016; Rao, 1997, 1999, 2004, 2005; Lee & Mumford, 2003; Yuille & Kersten, 2006). Notably, the predictive coding framework reproduces center-surround antagonism in retina (Srinivasan et al, 1982) and endstopping effects in V1 (Rao & Ballard, 1999).…”

Section: Introductionmentioning

confidence: 99%

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Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion

2018

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The primate visual system has an exquisite ability to discriminate partially occluded shapes. Recent electrophysiological recordings suggest that response dynamics in intermediate visual cortical area V4, shaped by feedback from prefrontal cortex (PFC), may play a key role. To probe the algorithms that may underlie these findings, we build and test a model of V4 and PFC interactions based on a hierarchical predictive coding framework. We propose that probabilistic inference occurs in two steps. Initially, V4 responses are driven solely by bottom-up sensory input and are thus strongly influenced by the level of occlusion. After a delay, V4 responses combine both feedforward input and feedback signals from the PFC; the latter reflect predictions made by PFC about the visual stimulus underlying V4 activity. We find that this model captures key features of V4 and PFC dynamics observed in experiments. Specifically, PFC responses are strongest for occluded stimuli and delayed responses in V4 are less sensitive to occlusion, supporting our hypothesis that the feedback signals from PFC underlie robust discrimination of occluded shapes. Thus, our study proposes that area V4 and PFC participate in hierarchical inference, with feedback signals encoding top-down predictions about occluded shapes.

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A review of predictive coding algorithms

Cited by 351 publications

References 51 publications

Visualising mental representations: A primer on noise-based reverse correlation in social psychology

Visualising mental representations: A primer on noise-based reverse correlation in social psychology

Neural Elements for Predictive Coding

Predictive Coding in Area V4: Dynamic Shape Discrimination under Partial Occlusion

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