Neural responses to action contingency error in different cortical areas are attributable to forward prediction or sensory processing

The hippocampus has been firmly established as playing a crucial role in flexible navigation. Recent evidence suggests that dorsal striatum may also play an important role in such goal-directed behaviour in both rodents and humans. Across recent studies, activity in the caudate nucleus has been linked to forward planning and adaptation to changes in the environment. In particular, several human neuroimaging studies have found the caudate nucleus tracks information traditionally associated with that by the hippocampus. In this brief review, we examine this evidence and argue the dorsal striatum encodes the transition structure of the environment during flexible, goal-directed behaviour. We highlight that future research should explore the following: (1) Investigate neural responses during spatial navigation via a biophysically plausible framework explained by reinforcement learning models and (2) Observe the interaction between cortical areas and both the dorsal striatum and hippocampus during flexible navigation.

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Going beyond rote auditory learning: Neural patterns of generalized auditory learning

10.1101/2020.12.30.424903 ◽

2021 ◽

Author(s):

Shannon L.M. Heald ◽

Stephen C. Van Hedger ◽

John Veillette ◽

Katherine Reis ◽

Joel S. Snyder ◽

...

Keyword(s):

Speech Perception ◽

Sensory Processing ◽

Auditory Processing ◽

Auditory Evoked Potentials ◽

Brain Regions ◽

Auditory Evoked Potential ◽

Cortical Processing ◽

Rote Learning ◽

Neural Responses ◽

Auditory Learning

AbstractThe ability to generalize rapidly across specific experiences is vital for robust recognition of new patterns, especially in speech perception considering acoustic-phonetic pattern variability. Behavioral research has demonstrated that listeners are rapidly able to generalize their experience with a talker’s speech and quickly improve understanding of a difficult-to-understand talker without prolonged practice, e.g., even after a single training session. Here, we examine the differences in neural responses to generalized versus rote learning in auditory cortical processing by training listeners to understand a novel synthetic talker using a Pretest-Posttest design with electroencephalography (EEG). Participants were trained using either (1) a large inventory of words where no words repeated across the experiment (generalized learning) or (2) a small inventory of words where words repeated (rote learning). Analysis of long-latency auditory evoked potentials at Pretest and Posttest revealed that while rote and generalized learning both produce rapid changes in auditory processing, the nature of these changes differed. In the context of adapting to a talker, generalized learning is marked by an amplitude reduction in the N1-P2 complex and by the presence of a late-negative (LN) wave in the auditory evoked potential following training. Rote learning, however, is marked only by temporally later source configuration changes. The early N1-P2 change, found only for generalized learning, suggests that generalized learning relies on the attentional system to reorganize the way acoustic features are selectively processed. This change in relatively early sensory processing (i.e. during the first 250ms) is consistent with an active processing account of speech perception, which proposes that the ability to rapidly adjust to the specific vocal characteristics of a new talker (for which rote learning is rare) relies on attentional mechanisms to adaptively tune early auditory processing sensitivity.Statement of SignificancePrevious research on perceptual learning has typically examined neural responses during rote learning: training and testing is carried out with the same stimuli. As a result, it is not clear that findings from these studies can explain learning that generalizes to novel patterns, which is critical in speech perception. Are neural responses to generalized learning in auditory processing different from neural responses to rote learning? Results indicate rote learning of a particular talker’s speech involves brain regions focused on the memory encoding and retrieving of specific learned patterns, whereas generalized learning involves brain regions involved in reorganizing attention during early sensory processing. In learning speech from a novel talker, only generalized learning is marked by changes in the N1-P2 complex (reflective of secondary auditory cortical processing). The results are consistent with the view that robust speech perception relies on the fast adjustment of attention mechanisms to adaptively tune auditory sensitivity to cope with acoustic variability.

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Olfaction Modulates Early Neural Responses to Matching Visual Objects

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00732 ◽

2015 ◽

Vol 27 (4) ◽

pp. 832-841 ◽

Cited By ~ 11

Author(s):

Amanda K. Robinson ◽

Judith Reinhard ◽

Jason B. Mattingley

Keyword(s):

Neural Activity ◽

Visual Processing ◽

Sensory Information ◽

Neural Correlates ◽

Neural Responses ◽

Visual Objects ◽

Cortical Areas ◽

Early Visual Processing ◽

Considerable Research ◽

Matter Concentration

Sensory information is initially registered within anatomically and functionally segregated brain networks but is also integrated across modalities in higher cortical areas. Although considerable research has focused on uncovering the neural correlates of multisensory integration for the modalities of vision, audition, and touch, much less attention has been devoted to understanding interactions between vision and olfaction in humans. In this study, we asked how odors affect neural activity evoked by images of familiar visual objects associated with characteristic smells. We employed scalp-recorded EEG to measure visual ERPs evoked by briefly presented pictures of familiar objects, such as an orange, mint leaves, or a rose. During presentation of each visual stimulus, participants inhaled either a matching odor, a nonmatching odor, or plain air. The N1 component of the visual ERP was significantly enhanced for matching odors in women, but not in men. This is consistent with evidence that women are superior in detecting, discriminating, and identifying odors and that they have a higher gray matter concentration in olfactory areas of the OFC. We conclude that early visual processing is influenced by olfactory cues because of associations between odors and the objects that emit them, and that these associations are stronger in women than in men.

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Accent-induced Modulation of Neural and Movement Patterns during Spontaneous Synchronization to Auditory Rhythms

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01605 ◽

2020 ◽

Vol 32 (12) ◽

pp. 2260-2271

Author(s):

Cécile J. Bouvet ◽

Benoît G. Bardy ◽

Peter E. Keller ◽

Simone Dalla Bella ◽

Sylvie Nozaradan ◽

...

Keyword(s):

Sensory Processing ◽

Neural Responses ◽

Rhythmic Movements ◽

Spontaneous Movement ◽

Passive Listening ◽

Complex Relationships ◽

Frequency Ratios ◽

And Control ◽

Movement Synchronization ◽

Synchronization Performance

Human rhythmic movements spontaneously synchronize with auditory rhythms at various frequency ratios. The emergence of more complex relationships—for instance, frequency ratios of 1:2 and 1:3—is enhanced by adding a congruent accentuation pattern (binary for 1:2 and ternary for 1:3), resulting in a 1:1 movement–accentuation relationship. However, this benefit of accentuation on movement synchronization appears to be stronger for the ternary pattern than for the binary pattern. Here, we investigated whether this difference in accent-induced movement synchronization may be related to a difference in the neural tracking of these accentuation profiles. Accented and control unaccented auditory sequences were presented to participants who concurrently produced finger taps at their preferred frequency, and spontaneous movement synchronization was measured. EEG was recorded during passive listening to each auditory sequence. The results revealed that enhanced movement synchronization with ternary accentuation was accompanied by enhanced neural tracking of this pattern. Larger EEG responses at the accentuation frequency were found for the ternary pattern compared with the binary pattern. Moreover, the amplitude of accent-induced EEG responses was positively correlated with the magnitude of accent-induced movement synchronization across participants. Altogether, these findings show that the dynamics of spontaneous auditory–motor synchronization is strongly driven by the multi-time-scale sensory processing of auditory rhythms, highlighting the importance of considering neural responses to rhythmic sequences for understanding and enhancing synchronization performance.

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The trait of sensory processing sensitivity and neural responses to changes in visual scenes

Social Cognitive and Affective Neuroscience ◽

10.1093/scan/nsq001 ◽

2010 ◽

Vol 6 (1) ◽

pp. 38-47 ◽

Cited By ~ 57

Author(s):

Jadzia Jagiellowicz ◽

Xiaomeng Xu ◽

Arthur Aron ◽

Elaine Aron ◽

Guikang Cao ◽

...

Keyword(s):

Sensory Processing ◽

Neural Responses ◽

Sensory Processing Sensitivity ◽

Visual Scenes

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Modulation of artificial whisking related signals in barrel cortex

Journal of Neurophysiology ◽

10.1152/jn.00809.2014 ◽

2015 ◽

Vol 113 (5) ◽

pp. 1287-1301 ◽

Cited By ~ 8

Author(s):

Manuel A. Castro-Alamancos ◽

Tatiana Bezdudnaya

Keyword(s):

Superior Colliculus ◽

Sensory Processing ◽

Barrel Cortex ◽

Motor Nerve ◽

Low Frequency ◽

Cortical Activation ◽

Neural Responses ◽

Population Responses ◽

Processing Abilities ◽

Network States

Rats use rhythmic whisker movements, called active whisking, to sense the environment, which include whisker protractions followed by retractions at various frequencies. Using a proxy of active whisking in anesthetized rats, called artificial whisking, which is induced by electrically stimulating the facial motor nerve, we characterized the neural responses evoked in the barrel cortex by whisking in air (without contact) and on a surface (with contact). Neural responses were compared between distinct network states consisting of cortical deactivation (synchronized slow oscillations) and activation (desynchronized state) produced by neuromodulation (cholinergic or noradrenergic stimulation in neocortex or thalamus). Here we show that population responses in the barrel cortex consist of a robust signal driven by the onset of the whisker protraction followed by a whisking retraction signal that emerges during low frequency whisking on a surface. The whisking movement onset signal is suppressed by increasing whisking frequency, is controlled by cortical synaptic inhibition, is suppressed during cortical activation states, is little affected by whisking on a surface, and is ubiquitous in ventroposterior medial (VPM) thalamus, barrel cortex, and superior colliculus. The whisking retraction signal codes the duration of the preceding whisker protraction, is present in thalamocortical networks but not in superior colliculus, and is robust during cortical activation; a state associated with natural exploratory whisking. The expression of different whisking signals in forebrain and midbrain may define the sensory processing abilities of those sensorimotor circuits. Whisking related signals in the barrel cortex are controlled by network states that are set by neuromodulators.

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Evolution of Neocortex for Sensory Processing

Oxford Research Encyclopedia of Neuroscience ◽

10.1093/acrefore/9780190264086.013.111 ◽

2019 ◽

Author(s):

Jon H. Kaas

Keyword(s):

Sensory Processing ◽

Cortical Neurons ◽

Pyramidal Neurons ◽

Motor Behavior ◽

Single Layer ◽

Small Region ◽

Inhibitory Neurons ◽

Dorsal Cortex ◽

Structural Differentiation ◽

Cortical Areas

The neocortex is a part of the forebrain of mammals that is an innovation of mammal-like “reptilian” synapsid ancestors of early mammals. This neocortex emerged from a small region of dorsal cortex that was present in earlier ancestors and is still found in the forebrain of present-day reptiles. Instead of the thick structure of six layers of cells (five layers) and fibers (one layer) of neocortex of mammals, the dorsal cortex was characterized by a single layer of pyramidal neurons and a scattering of small, largely inhibitory neurons. In reptiles, the dorsal cortex is dominated by visual inputs, with outputs that relate to behavior and memory. The thicker neocortex of six layers in early mammals was already divided into a number of functionally specialized zones called cortical areas that were predominantly sensory in function, while relating to important aspects of motor behavior via subcortical projections. These early sensorimotor areas became modified in various ways as different branches of the mammalian radiation evolved, and neocortex often increased in size and the number of cortical areas, likely by the process of specializations within areas that subdivided areas. At least some areas, perhaps most, subdivided in another way by evolving two or more alternating types of small regions of different functional specializations, now referred to as cortical modules or columns. The specializations within and across cortical areas included those in the sizes of neurons and the extents of their processes, the dendrites and axons, and thus connections with other neurons. As a result, the neocortex of present-day mammals varies greatly within and across phylogenetically related groups (clades), while retaining basic features of organization from early ancestral mammals. In a number of present-day (extant) mammals, brains are relatively small and have little neocortex, with few areas and little structural differentiation, thus resembling early mammals. Other small mammals with little neocortex have specialized some part via selective enlargement and structural modifications to promote certain sensory abilities. Other mammals have a neocortex that is moderately to greatly expanded, with more cortical areas directly related to sensory processing and cognition and memory. The human brain is extreme in this way by having more neocortex in proportion to the rest of the brain, more cortical neurons, and likely more cortical areas.

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Integrating Flexible Normalization into Midlevel Representations of Deep Convolutional Neural Networks

Neural Computation ◽

10.1162/neco_a_01226 ◽

2019 ◽

Vol 31 (11) ◽

pp. 2138-2176 ◽

Cited By ~ 2

Author(s):

Luis Gonzalo Sánchez Giraldo ◽

Odelia Schwartz

Keyword(s):

Neural Networks ◽

Visual Cortex ◽

Convolutional Neural Networks ◽

Neural Responses ◽

Spatial Normalization ◽

Deep Convolutional Neural Networks ◽

Cortical Areas ◽

Classical Receptive Field ◽

Divisive Normalization ◽

Spatial Dependencies

Deep convolutional neural networks (CNNs) are becoming increasingly popular models to predict neural responses in visual cortex. However, contextual effects, which are prevalent in neural processing and in perception, are not explicitly handled by current CNNs, including those used for neural prediction. In primary visual cortex, neural responses are modulated by stimuli spatially surrounding the classical receptive field in rich ways. These effects have been modeled with divisive normalization approaches, including flexible models, where spatial normalization is recruited only to the degree that responses from center and surround locations are deemed statistically dependent. We propose a flexible normalization model applied to midlevel representations of deep CNNs as a tractable way to study contextual normalization mechanisms in midlevel cortical areas. This approach captures nontrivial spatial dependencies among midlevel features in CNNs, such as those present in textures and other visual stimuli, that arise from tiling high-order features geometrically. We expect that the proposed approach can make predictions about when spatial normalization might be recruited in midlevel cortical areas. We also expect this approach to be useful as part of the CNN tool kit, therefore going beyond more restrictive fixed forms of normalization.

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Theory of cortical function

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1619788114 ◽

2017 ◽

Vol 114 (8) ◽

pp. 1773-1782 ◽

Cited By ~ 81

Author(s):

David J. Heeger

Keyword(s):

Sensory Processing ◽

Brain Area ◽

Neural Nets ◽

Oscillatory Activity ◽

Neural Responses ◽

Cortical Function ◽

Computational Framework ◽

Prior Expectation ◽

Wide Range ◽

The Brain

Most models of sensory processing in the brain have a feedforward architecture in which each stage comprises simple linear filtering operations and nonlinearities. Models of this form have been used to explain a wide range of neurophysiological and psychophysical data, and many recent successes in artificial intelligence (with deep convolutional neural nets) are based on this architecture. However, neocortex is not a feedforward architecture. This paper proposes a first step toward an alternative computational framework in which neural activity in each brain area depends on a combination of feedforward drive (bottom-up from the previous processing stage), feedback drive (top-down context from the next stage), and prior drive (expectation). The relative contributions of feedforward drive, feedback drive, and prior drive are controlled by a handful of state parameters, which I hypothesize correspond to neuromodulators and oscillatory activity. In some states, neural responses are dominated by the feedforward drive and the theory is identical to a conventional feedforward model, thereby preserving all of the desirable features of those models. In other states, the theory is a generative model that constructs a sensory representation from an abstract representation, like memory recall. In still other states, the theory combines prior expectation with sensory input, explores different possible perceptual interpretations of ambiguous sensory inputs, and predicts forward in time. The theory, therefore, offers an empirically testable framework for understanding how the cortex accomplishes inference, exploration, and prediction.

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Increased influence of periphery on central visual processing in humans during walking

10.1101/382093 ◽

2018 ◽

Author(s):

Liyu Cao ◽

Barbara Händel

Keyword(s):

Sensory Processing ◽

Visual Processing ◽

Sensory Information ◽

Human Perception ◽

Neural Responses ◽

Natural Behaviour ◽

Visual Areas ◽

Sensory Information Processing ◽

Visual Cortical ◽

Behavioural Evidence

AbstractCognitive processes are almost exclusively investigated under highly controlled settings while voluntary body movements are suppressed. However, recent animal work suggests differences in sensory processing between movement states by showing drastically changed neural responses in early visual areas between locomotion and stillness. Does locomotion also modulate visual cortical activity in humans and what are its perceptual consequences? Here, we present converging neurophysiological and behavioural evidence that walking leads to an increased influence of peripheral stimuli on central visual input. This modulation of visual processing due to walking is encompassed by a change in alpha oscillations, which is suggestive of an attentional shift to the periphery during walking. Overall, our study shows that strategies of sensory information processing can differ between movement states. This finding further demonstrates that a comprehensive understanding of human perception and cognition critically depends on the consideration of natural behaviour.

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