Predictive feedback to V1 dynamically updates with sensory input

Mapping Intimacies ◽

10.1101/180539 ◽

2017 ◽

Author(s):

Grace Edwards ◽

Petra Vetter ◽

Fiona McGruer ◽

Lucy S. Petro ◽

Lars Muckli

Keyword(s):

Eye Movements ◽

Apparent Motion ◽

Sensory Input ◽

Predictive Coding ◽

Functional Brain Imaging ◽

Sensory Inputs ◽

Amplification Process ◽

Early Visual Cortex ◽

Higher Visual Areas ◽

The Brain

AbstractPredictive coding theories propose that the brain creates internal models of the environment to predict upcoming sensory input. Hierarchical predictive coding models of vision postulate that higher visual areas generate predictions of sensory inputs and feed them back to early visual cortex. In V1, sensory inputs that do not match the predictions lead to amplified brain activation, but does this amplification process dynamically update to new retinotopic locations with eye-movements? We investigated the effect of eye-movements in predictive feedback using functional brain imaging and eye-tracking whilst presenting an apparent motion illusion. Apparent motion induces an internal model of motion, during which sensory predictions of the illusory motion feed back to V1. We observed attenuated BOLD responses to predicted stimuli at the new post-saccadic location in V1. Therefore, pre-saccadic predictions update their retinotopic location in time for post-saccadic input, validating dynamic predictive coding theories in V1.

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Mapping between sound, brain and behaviour: four-level framework for understanding rhythm processing in humans and non-human primates

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2020.0325 ◽

2021 ◽

Vol 376 (1835) ◽

pp. 20200325

Author(s):

Tomas Lenc ◽

Hugo Merchant ◽

Peter E. Keller ◽

Henkjan Honing ◽

Manuel Varlet ◽

...

Keyword(s):

Sensory Input ◽

Body Movement ◽

Neural Substrates ◽

Theme Issue ◽

Musical Rhythm ◽

Sensory Inputs ◽

Flexible System ◽

Four Levels ◽

The Many ◽

The Brain

Humans perceive and spontaneously move to one or several levels of periodic pulses (a meter, for short) when listening to musical rhythm, even when the sensory input does not provide prominent periodic cues to their temporal location. Here, we review a multi-levelled framework to understanding how external rhythmic inputs are mapped onto internally represented metric pulses. This mapping is studied using an approach to quantify and directly compare representations of metric pulses in signals corresponding to sensory inputs, neural activity and behaviour (typically body movement). Based on this approach, recent empirical evidence can be drawn together into a conceptual framework that unpacks the phenomenon of meter into four levels. Each level highlights specific functional processes that critically enable and shape the mapping from sensory input to internal meter. We discuss the nature, constraints and neural substrates of these processes, starting with fundamental mechanisms investigated in macaque monkeys that enable basic forms of mapping between simple rhythmic stimuli and internally represented metric pulse. We propose that human evolution has gradually built a robust and flexible system upon these fundamental processes, allowing more complex levels of mapping to emerge in musical behaviours. This approach opens promising avenues to understand the many facets of rhythmic behaviours across individuals and species. This article is part of the theme issue ‘Synchrony and rhythm interaction: from the brain to behavioural ecology’.

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Untangling Sequences: Behavior vs. External Causes

10.1101/190678 ◽

2017 ◽

Cited By ~ 2

Author(s):

Subutai Ahmad ◽

Jeff Hawkins

Keyword(s):

Sensory Input ◽

Neural Mechanism ◽

The Body ◽

External Factors ◽

Sensory Inputs ◽

External Causes ◽

Cortical Regions ◽

Temporal Sequences ◽

The Brain ◽

Brain Change

ABSTRACTThere are two fundamental reasons why sensory inputs to the brain change over time. Sensory inputs can change due to external factors or they can change due to our own behavior. Interpreting behavior-generated changes requires knowledge of how the body is moving, whereas interpreting externally-generated changes relies solely on the temporal sequence of input patterns. The sensory signals entering the neocortex change due to a mixture of both behavior and external factors. The neocortex must disentangle them but the mechanisms are unknown. In this paper, we show that a single neural mechanism can learn and recognize both types of sequences. In the model, cells are driven by feedforward sensory input and are modulated by contextual input. If the contextual input includes information derived from efference motor copies, the cells learn sensorimotor sequences. If the contextual input consists of nearby cellular activity, the cells learn temporal sequences. Through simulation we show that a network containing both types of contextual input automatically separates and learns both types of input patterns. We review experimental data that suggests the upper layers of cortical regions contain the anatomical structure required to support this mechanism.

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Predictive coding is a consequence of energy efficiency in recurrent neural networks

10.1101/2021.02.16.430904 ◽

2021 ◽

Author(s):

Abdullahi Ali ◽

Nasir Ahmad ◽

Elgar de Groot ◽

Marcel A. J. van Gerven ◽

Tim C. Kietzmann

Keyword(s):

Neural Networks ◽

Energy Efficiency ◽

Recurrent Neural Networks ◽

Brain Function ◽

Message Passing ◽

Sensory Input ◽

Predictive Coding ◽

Computational Modelling ◽

Top Down ◽

The Brain

AbstractPredictive coding represents a promising framework for understanding brain function. It postulates that the brain continuously inhibits predictable sensory input, ensuring a preferential processing of surprising elements. A central aspect of this view is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modelling to demonstrate that such architectural hard-wiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency. When training recurrent neural networks to minimise their energy consumption while operating in predictive environments, the networks self-organise into prediction and error units with appropriate inhibitory and excitatory interconnections, and learn to inhibit predictable sensory input. Moving beyond the view of purely top-down driven predictions, we demonstrate via virtual lesioning experiments that networks perform predictions on two timescales: fast lateral predictions among sensory units, and slower prediction cycles that integrate evidence over time.

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Towards domain-general predictive coding: Expected TMS excites the motor system less effectively than unexpected stimulation

10.1101/2020.02.18.953562 ◽

2020 ◽

Author(s):

Dominic M. D. Tran ◽

Nicolas A. McNair ◽

Justin A. Harris ◽

Evan J. Livesey

Keyword(s):

Motor System ◽

Sensory Input ◽

Primary Motor Cortex ◽

Motor Evoked Potentials ◽

Predictive Coding ◽

Auditory Evoked Potential ◽

Environmental Cues ◽

Direct Stimulation ◽

Predictive Learning ◽

The Brain

AbstractThe brain’s response to sensory input is modulated by prediction. For example, sounds that are produced by one’s own actions, or those that are strongly predicted by environmental cues, are perceived as less salient and elicit an attenuated N1 component in the auditory evoked potential. Here we examined whether the neural response to direct stimulation of the brain is attenuated by prediction in a similar manner. Transcranial magnetic stimulation (TMS) applied over primary motor cortex can be used to gauge the excitability of the motor system. Motor-evoked potentials (MEPs), elicited by TMS and measured in peripheral muscles, are larger when actions are being prepared and smaller when actions are voluntarily suppressed. We tested whether the amplitude of MEPs was attenuated under circumstances where the TMS pulse can be reliably predicted, even though control of the relevant motor effector was never required. Self-initiation of the TMS pulse and reliable cuing of the TMS pulse both attenuated MEP amplitudes, compared to MEPs generated programmatically in an unpredictable manner. These results suggest that predictive coding may be governed by domain-general mechanisms responsible for all forms predictive learning.

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Endogenous activity modulates stimulus and circuit-specific neural tuning and perception

10.1101/687152 ◽

2019 ◽

Author(s):

Yuanning Li ◽

Michael J. Ward ◽

R. Mark Richardson ◽

Max G’Sell ◽

Avniel Singh Ghuman

Keyword(s):

Reaction Time ◽

Neural Circuits ◽

Neural Circuit ◽

Predictive Coding ◽

Sensory Inputs ◽

Specific Manner ◽

Endogenous Activity ◽

Intracranial Recordings ◽

The Brain

AbstractPerception reflects not only input from the sensory periphery, but also the endogenous neural state when sensory inputs enter the brain. Whether endogenous neural states influence perception only through global mechanisms, such as arousal, or can also perception in a neural circuit and stimulus specific manner remains largely unknown. Intracranial recordings from 30 pre-surgical epilepsy patients showed that endogenous activity independently modulated the strength of trial-by-trial neural tuning of different visual category-selective neural circuits. Furthermore, the same aspect of the endogenous activity that influenced tuning in a particular neural circuit also correlated with reaction time only for trials with the category of image that circuit was selective for. These results suggest that endogenous activity may influence neural tuning and perception through circuit-specific predictive coding processes.

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Dynamic Causal Modeling of the Response to Frequency Deviants

Journal of Neurophysiology ◽

10.1152/jn.90291.2008 ◽

2009 ◽

Vol 101 (5) ◽

pp. 2620-2631 ◽

Cited By ~ 105

Author(s):

Marta I. Garrido ◽

James M. Kilner ◽

Stefan J. Kiebel ◽

Karl J. Friston

Keyword(s):

Mismatch Negativity ◽

Sensory Input ◽

Model Comparison ◽

Memory Trace ◽

Effective Connectivity ◽

Predictive Coding ◽

Causal Modeling ◽

Evoked Responses ◽

Dynamic Causal Modeling ◽

The Brain

This article describes the use of dynamic causal modeling to test hypotheses about the genesis of evoked responses. Specifically, we consider the mismatch negativity (MMN), a well-characterized response to deviant sounds and one of the most widely studied evoked responses. There have been several mechanistic accounts of how the MMN might arise. It has been suggested that the MMN results from a comparison between sensory input and a memory trace of previous input, although others have argued that local adaptation, due to stimulus repetition, is sufficient to explain the MMN. Thus the precise mechanisms underlying the generation of the MMN remain unclear. This study tests some biologically plausible spatiotemporal dipole models that rest on changes in extrinsic top-down connections (that enable comparison) and intrinsic changes (that model adaptation). Dynamic causal modeling suggested that responses to deviants are best explained by changes in effective connectivity both within and between cortical sources in a hierarchical network of distributed sources. Our model comparison suggests that both adaptation and memory comparison operate in concert to produce the early (N1 enhancement) and late (MMN) parts of the response to frequency deviants. We consider these mechanisms in the light of predictive coding and hierarchical inference in the brain.

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Decoding the future from past experience: learning shapes predictions in early visual cortex

Journal of Neurophysiology ◽

10.1152/jn.00753.2014 ◽

2015 ◽

Vol 113 (9) ◽

pp. 3159-3171 ◽

Cited By ~ 7

Author(s):

Caroline D. B. Luft ◽

Alan Meeson ◽

Andrew E. Welchman ◽

Zoe Kourtzi

Keyword(s):

Visual Cortex ◽

Large Scale ◽

Sequence Structure ◽

Neural Populations ◽

Early Visual Cortex ◽

Temporal Structures ◽

Future Events ◽

Sensory Predictions ◽

Actual Stimulus ◽

The Brain

Learning the structure of the environment is critical for interpreting the current scene and predicting upcoming events. However, the brain mechanisms that support our ability to translate knowledge about scene statistics to sensory predictions remain largely unknown. Here we provide evidence that learning of temporal regularities shapes representations in early visual cortex that relate to our ability to predict sensory events. We tested the participants' ability to predict the orientation of a test stimulus after exposure to sequences of leftward- or rightward-oriented gratings. Using fMRI decoding, we identified brain patterns related to the observers' visual predictions rather than stimulus-driven activity. Decoding of predicted orientations following structured sequences was enhanced after training, while decoding of cued orientations following exposure to random sequences did not change. These predictive representations appear to be driven by the same large-scale neural populations that encode actual stimulus orientation and to be specific to the learned sequence structure. Thus our findings provide evidence that learning temporal structures supports our ability to predict future events by reactivating selective sensory representations as early as in primary visual cortex.

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The emulation theory of representation: Motor control, imagery, and perception

Behavioral and Brain Sciences ◽

10.1017/s0140525x04000093 ◽

2004 ◽

Vol 27 (3) ◽

pp. 377-396 ◽

Cited By ~ 565

Author(s):

Rick Grush

Keyword(s):

Motor Control ◽

Sensory Feedback ◽

Sensory Input ◽

Neural Circuits ◽

Sensory Information ◽

The Body ◽

Feedback Delay ◽

Run Off ◽

Effects Of Feedback ◽

The Brain

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

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Context-Dependent Modulation of Early Visual Cortical Responses to Numerical and Nonnumerical Magnitudes

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01774 ◽

2021 ◽

pp. 1-12

Author(s):

Joonkoo Park ◽

Sonia Godbole ◽

Marty G. Woldorff ◽

Elizabeth M. Brannon

Keyword(s):

Visual Processing ◽

Context Dependency ◽

Numerical Magnitude ◽

Continuous Variables ◽

Processing Stream ◽

Cortical Responses ◽

Early Visual Cortex ◽

Context Dependent ◽

Visual Cortical ◽

The Brain

Abstract Whether and how the brain encodes discrete numerical magnitude differently from continuous nonnumerical magnitude is hotly debated. In a previous set of studies, we orthogonally varied numerical (numerosity) and nonnumerical (size and spacing) dimensions of dot arrays and demonstrated a strong modulation of early visual evoked potentials (VEPs) by numerosity and not by nonnumerical dimensions. Although very little is known about the brain's response to systematic changes in continuous dimensions of a dot array, some authors intuit that the visual processing stream must be more sensitive to continuous magnitude information than to numerosity. To address this possibility, we measured VEPs of participants viewing dot arrays that changed exclusively in one nonnumerical magnitude dimension at a time (size or spacing) while holding numerosity constant and compared this to a condition where numerosity was changed while holding size and spacing constant. We found reliable but small neural sensitivity to exclusive changes in size and spacing; however, changing numerosity elicited a much more robust modulation of the VEPs. Together with previous work, these findings suggest that sensitivity to magnitude dimensions in early visual cortex is context dependent: The brain is moderately sensitive to changes in size and spacing when numerosity is held constant, but sensitivity to these continuous variables diminishes to a negligible level when numerosity is allowed to vary at the same time. Neurophysiological explanations for the encoding and context dependency of numerical and nonnumerical magnitudes are proposed within the framework of neuronal normalization.

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