Where is the error? Hierarchical predictive coding through dendritic error computation

Mikulasch, Fabian A.; Rudelt, Lucas; Wibral, Michael; Priesemann, Viola

doi:10.1016/j.tins.2022.09.007

Cited by 67 publications

(54 citation statements)

References 122 publications

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“…Inspired by the dendritic model in [21,22], we push the biological plausibility of the implicit model further by imposing a "stop gradient" (sg) operation on the objective function Eq 10:…”

Section: Dendritic Covariance-learning Pcnsmentioning

confidence: 99%

Recurrent predictive coding models for associative memory employing covariance learning

et al. 2023

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The computational principles adopted by the hippocampus in associative memory (AM) tasks have been one of the most studied topics in computational and theoretical neuroscience. Recent theories suggested that AM and the predictive activities of the hippocampus could be described within a unitary account, and that predictive coding underlies the computations supporting AM in the hippocampus. Following this theory, a computational model based on classical hierarchical predictive networks was proposed and was shown to perform well in various AM tasks. However, this fully hierarchical model did not incorporate recurrent connections, an architectural component of the CA3 region of the hippocampus that is crucial for AM. This makes the structure of the model inconsistent with the known connectivity of CA3 and classical recurrent models such as Hopfield Networks, which learn the covariance of inputs through their recurrent connections to perform AM. Earlier PC models that learn the covariance information of inputs explicitly via recurrent connections seem to be a solution to these issues. Here, we show that although these models can perform AM, they do it in an implausible and numerically unstable way. Instead, we propose alternatives to these earlier covariance-learning predictive coding networks, which learn the covariance information implicitly and plausibly, and can use dendritic structures to encode prediction errors. We show analytically that our proposed models are perfectly equivalent to the earlier predictive coding model learning covariance explicitly, and encounter no numerical issues when performing AM tasks in practice. We further show that our models can be combined with hierarchical predictive coding networks to model the hippocampo-neocortical interactions. Our models provide a biologically plausible approach to modelling the hippocampal network, pointing to a potential computational mechanism during hippocampal memory formation and recall, which employs both predictive coding and covariance learning based on the recurrent network structure of the hippocampus.

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“…Inspired by the dendritic model in [21,22], we push the biological plausibility of the implicit model further by imposing a "stop gradient" (sg) operation on the objective function Eq 10:…”

Section: Dendritic Covariance-learning Pcnsmentioning

confidence: 99%

Recurrent predictive coding models for associative memory employing covariance learning

et al. 2023

View full text Add to dashboard Cite

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“…Gamma-band synchronization may selectively increase the responsiveness of cortical error units without affecting the response of cortical prediction units that are tuned to signals at lower frequencies (Bastos et al, 2012). Additionally, an alternative proposal for neural implementation of predictive coding is to replace standard error units with dendritic error computation (Mikulasch et al, 2023), where the dendritic membrane potentials are integrated at the soma to form an error signal. Therefore, a spiking neuron can emit a spike when the somatic error potential grows too large, followed by a reduction in the overall error.…”

Section: Neural Implementation Of Hierarchical Predictive Codingmentioning

confidence: 99%

“…In the hierarchical predictive coding model, the same error units can mediate bottom-up errors to update prediction units in the next level, as well as modulate top-down errors to neurons of the same level. The synaptic plasticity between error units and prediction units can be easily modulated by the classic Hebbian rule (Mikulasch et al, 2023). Despite the theoretical elegance, these two proposals are restricted to the implementation within different cortical layers (Shipp, 2016), and requires a much-needed update to accommodate predictive coding scenarios that are performed in different subdivisions of a cortical area, or in distributed neural circuits.…”

Section: Neural Implementation Of Hierarchical Predictive Codingmentioning

confidence: 99%

Hierarchical predictive coding in distributed pain circuits

Chen

2023

Front. Neural Circuits

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Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

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“…By contrast, feedback projections arise from regions higher in the cortical hierarchy [77], and project to supragranular layers of lower cortical regions, wherein they innervate both interneurons and the apical dendrites of pyramidal neurons with cell bodies in L2, L3 and L5 (figure 5). These two streams of activity are presumed to combine together to instantiate a form of predict processing in the brain, albeit with distinct implementation-level predictions depending the particular cells under investigation [79][80][81]. For instance, one proposed implementation of predictive processing suggests that, when feedforward and feedback signals coincide within a precise (approx.…”

Section: Shifting Between Feedforward and Feedback Modes In The Cereb...mentioning

confidence: 99%

Neuromodulatory control of complex adaptive dynamics in the brain

Shine

2023

Interface Focus.

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How is the massive dimensionality and complexity of the microscopic constituents of the nervous system brought under sufficiently tight control so as to coordinate adaptive behaviour? A powerful means for striking this balance is to poise neurons close to the critical point of a phase transition, at which a small change in neuronal excitability can manifest a nonlinear augmentation in neuronal activity. How the brain could mediate this critical transition is a key open question in neuroscience. Here, I propose that the different arms of the ascending arousal system provide the brain with a diverse set of heterogeneous control parameters that can be used to modulate the excitability and receptivity of target neurons—in other words, to act as control parameters for mediating critical neuronal order. Through a series of worked examples, I demonstrate how the neuromodulatory arousal system can interact with the inherent topological complexity of neuronal subsystems in the brain to mediate complex adaptive behaviour.

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Where is the error? Hierarchical predictive coding through dendritic error computation

Cited by 67 publications

References 122 publications

Recurrent predictive coding models for associative memory employing covariance learning

Recurrent predictive coding models for associative memory employing covariance learning

Hierarchical predictive coding in distributed pain circuits

Neuromodulatory control of complex adaptive dynamics in the brain

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