Temporal dynamics of neural activity in the error trial

In various regions of the brain, neurons discriminate sensory stimuli by decreasing the similarity between ambiguous input patterns. Here, we examine whether this process of pattern separation may drive the rapid discrimination of visual motion stimuli in the lateral intraparietal area (LIP). Starting with a simple mean-rate population model that captures neuronal activity in LIP, we show that overlapping input patterns can be reformatted dynamically to give rise to separated patterns of neuronal activity. The population model predicts that a key ingredient of pattern separation is the presence of heterogeneity in the response of individual units. Furthermore, the model proposes that pattern separation relies on heterogeneity in the temporal dynamics of neural activity and not merely in the mean firing rates of individual neurons over time. We confirm these predictions in recordings of macaque LIP neurons and show that the accuracy of pattern separation is a strong predictor of behavioral performance. Overall, results propose that LIP relies on neuronal pattern separation to facilitate decision-relevant discrimination of sensory stimuli. NEW & NOTEWORTHY A new hypothesis is proposed on the role of the lateral intraparietal (LIP) region of cortex during rapid decision making. This hypothesis suggests that LIP alters the representation of ambiguous inputs to reduce their overlap, thus improving sensory discrimination. A combination of computational modeling, theoretical analysis, and electrophysiological data shows that the pattern separation hypothesis links neural activity to behavior and offers novel predictions on the role of LIP during sensory discrimination.

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Sensorimotor Locus of the Buildup Activity in Monkey Lateral Intraparietal Area Neurons

Journal of Neurophysiology ◽

10.1152/jn.00733.2009 ◽

2010 ◽

Vol 103 (5) ◽

pp. 2664-2674 ◽

Cited By ~ 1

Author(s):

Joonkoo Park ◽

Jun Zhang

Keyword(s):

Neural Activity ◽

Time Course ◽

Temporal Dynamics ◽

Neuronal Firing ◽

Firing Activity ◽

Motor Representation ◽

Lateral Intraparietal Area ◽

Response Mapping ◽

Stimulus Response ◽

Dot Motion

A study in 2002 using a random-dot motion-discrimination paradigm showed that an information accumulation model with a threshold-crossing mechanism can account for activity of the lateral intraparietal area (LIP) neurons. Here, mathematical techniques were applied to the same dataset to quantitatively address the sensory versus motor representation of the neuronal activity during the time course of a trial. A technique based on Signal Detection Theory was applied to provide indices to quantify how neuronal firing activity is responsible for encoding the stimulus or selecting the response at the behavioral level. Additionally, a statistical model based on Poisson regression was used to provide an orthogonal decomposition of the neural activity into stimulus, response, and stimulus-response mapping components. The temporal dynamics of the sensorimotor locus of the LIP activity indicated that there is no stimulus-response mapping-specific neuronal firing activity throughout a trial; the neural activity toward the saccadic onset reflects the development of the motor representation, and the neural activity in the beginning of a trial contains little, if any, information about the sensory representation. Sensorimotor analysis on individual neurons also showed that the neuronal activation, as a population, represent pending saccadic direction and carry little information about the direction of the motion stimulus.

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Stimulus dependence of theta rhythmic activity in primate V1 and its potential relevance for visual perception

10.1101/2021.11.30.470367 ◽

2021 ◽

Author(s):

Prasakti Tenri Fanyiwi ◽

Beshoy Agayby ◽

Ricardo Kienitz ◽

Marcus Haag ◽

Michael C. Schmid

Keyword(s):

Visual Perception ◽

Visual Stimulus ◽

Neural Activity ◽

Temporal Dynamics ◽

Reaction Times ◽

Rhythmic Activity ◽

Peak Frequency ◽

Theta Oscillations ◽

Theta Power ◽

Fixational Eye Movements

AbstractA growing body of psychophysical research reports theta (3-8 Hz) rhythmic fluctuations in visual perception that are often attributed to an attentional sampling mechanism arising from theta rhythmic neural activity in mid- to high-level cortical association areas. However, it remains unclear to what extent such neuronal theta oscillations might already emerge at early sensory cortex like the primary visual cortex (V1), e.g. from the stimulus filter properties of neurons. To address this question, we recorded multi-unit neural activity from V1 of two macaque monkeys viewing a static visual stimulus with variable sizes, orientations and contrasts. We found that among the visually responsive electrode sites, more than 50 % showed a spectral peak at theta frequencies. Theta power varied with varying basic stimulus properties. Within each of these stimulus property domains (e.g. size), there was usually a single stimulus value that induced the strongest theta activity. In addition to these variations in theta power, the peak frequency of theta oscillations increased with increasing stimulus size and also changed depending on the stimulus position in the visual field. Further analysis confirmed that this neural theta rhythm was indeed stimulus-induced and did not arise from small fixational eye movements (microsaccades). When the monkeys performed a detection task of a target embedded in a theta-generating visual stimulus, reaction times also tended to fluctuate at the same theta frequency as the one observed in the neural activity. The present study shows that a highly stimulus-dependent neuronal theta oscillation can be elicited in V1 that appears to influence the temporal dynamics of visual perception.

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Spatio-temporal dynamics of reach-related neural activity for visual and somatosensory targets

NeuroImage ◽

10.1016/j.neuroimage.2009.05.028 ◽

2009 ◽

Vol 47 (4) ◽

pp. 1767-1777 ◽

Cited By ~ 16

Author(s):

Pierre-Michel Bernier ◽

Borís Burle ◽

Thierry Hasbroucq ◽

Jean Blouin

Keyword(s):

Neural Activity ◽

Temporal Dynamics ◽

Spatio Temporal

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Mountains and Valleys: Binocular Rivalry and the Flow of Experience

10.31231/osf.io/pvk3u ◽

2017 ◽

Author(s):

Diego Cosmelli ◽

Evan Thompson

Keyword(s):

Experimental Study ◽

Experimental Research ◽

Binocular Rivalry ◽

Neural Activity ◽

Temporal Dynamics ◽

Conscious Experience ◽

Ongoing Activity ◽

Temporal Aspects ◽

Multistable Perception ◽

Temporal Flow

Binocular rivalry provides a useful situation for studying the relation between the temporal flow of conscious experience and the temporal dynamics of neural activity. After proposing a phenomenological framework for understanding temporal aspects of consciousness, we review experimental research on multistable perception and binocular rivalry, singling out various methodological, theoretical, and empirical aspects of this research relevant to studying the flow of experience. We then review an experimental study from our group explicitly concerned with relating the temporal dynamics of rivalrous experience to the temporal dynamics of cortical activity. Drawing attention to the importance of dealing with ongoing activity and its inherent changing nature at both phenomenological and neurodynamical levels, we argue that the notions of recurrence and variability are pertinent to understanding rivalry in particular and the flow of experience in general.

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Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008330 ◽

2020 ◽

Vol 16 (11) ◽

pp. e1008330

Author(s):

Marcus A. Triplett ◽

Zac Pujic ◽

Biao Sun ◽

Lilach Avitan ◽

Geoffrey J. Goodhill

Keyword(s):

Spontaneous Activity ◽

Neural Activity ◽

Calcium Imaging ◽

Temporal Dynamics ◽

Population Level ◽

Imaging Data ◽

Evoked Activity ◽

Zebrafish Optic Tectum ◽

Low Dimensional ◽

Mouse Visual Cortex

The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.

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Characterizing the spatio-temporal dynamics of behavior-related neural activity during human visual object perception

Journal of Vision ◽

10.1167/17.10.1341 ◽

2017 ◽

Vol 17 (10) ◽

pp. 1341

Author(s):

Radoslaw Cichy ◽

Nikolaus Kriegeskorte ◽

Jasper van den Bosch ◽

Kamila Jozwik ◽

Ian Charest

Keyword(s):

Neural Activity ◽

Temporal Dynamics ◽

Object Perception ◽

Visual Object ◽

Spatio Temporal

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A spatiotemporal complexity architecture of human brain activity

10.1101/2021.06.04.446948 ◽

2021 ◽

Author(s):

Stephan Krohn ◽

Nina von Schwanenflug ◽

Leonhard Waschke ◽

Amy Romanello ◽

Martin Gell ◽

...

Keyword(s):

Human Brain ◽

Neural Activity ◽

Large Scale ◽

Temporal Dynamics ◽

Brain Activity ◽

Brain Regions ◽

Brain Organization ◽

Functional Connections ◽

Signal Complexity ◽

The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

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Temporal dynamics of neural activity in motor execution and inhibition processing

European Journal of Neuroscience ◽

10.1111/ejn.12889 ◽

2015 ◽

Vol 41 (11) ◽

pp. 1448-1458 ◽

Cited By ~ 12

Author(s):

Hiroki Nakata ◽

Kiwako Sakamoto ◽

Yukiko Honda ◽

Ryusuke Kakigi

Keyword(s):

Neural Activity ◽

Temporal Dynamics ◽

Motor Execution

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Low-Dimensional Spatio-Temporal Dynamics Underlie Cortex-Wide Neural Activity

10.1101/2020.01.05.895177 ◽

2020 ◽

Cited By ~ 1

Author(s):

Camden J. MacDowell ◽

Timothy J. Buschman

Keyword(s):

Sensory Processing ◽

Neural Activity ◽

Calcium Imaging ◽

Temporal Dynamics ◽

Dorsal Cortex ◽

Dynamic Flow ◽

Efficient Control ◽

Spatio Temporal ◽

Low Dimensional ◽

The Brain

AbstractCognition arises from the dynamic flow of neural activity through the brain. To capture these dynamics, we used mesoscale calcium imaging to record neural activity across the dorsal cortex of awake mice. We found that the large majority of variance in cortex-wide activity (∼75%) could be explained by a limited set of ∼14 ‘motifs’ of neural activity. Each motif captured a unique spatio-temporal pattern of neural activity across the cortex. These motifs generalized across animals and were seen in multiple behavioral environments. Motif expression differed across behavioral states and specific motifs were engaged by sensory processing, suggesting the motifs reflect core cortical computations. Together, our results show that cortex-wide neural activity is highly dynamic, but that these dynamics are restricted to a low-dimensional set of motifs, potentially to allow for efficient control of behavior.

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