scholarly journals Residual dynamics resolves recurrent contributions to neural computation

2021 ◽  
Author(s):  
Aniruddh R Galgali ◽  
Maneesh Sahani ◽  
Valerio Mante
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Neural Circuit ◽  
Neural Population ◽  
Perceptual Decision Making ◽  
Neural Recordings ◽  
Fine Grained ◽  
Neural Computations ◽  
Population Trajectory ◽  
The Mean

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents significant challenges. Here, we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals, i.e. trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque pre-frontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time-dependent, but consistently stable, and implies that pronounced rotational structure in PFC trajectories during saccades are driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation, and suggest a path towards fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.

scholarly journals Changes-of-mind in the absence of new post-decision evidence

2019 ◽  
Author(s):  
Nadim A. A. Atiya ◽  
Arkady Zgonnikov ◽  
Martin Schoemann ◽  
Stefan Scherbaum ◽  
Denis O’Hora ◽  
...  
Keyword(s):  
Discrimination Task ◽  
Neural Circuit ◽  
Response Times ◽  
Neural Population ◽  
Perceptual Decision Making ◽  
Top Down ◽  
Decision Uncertainty ◽  
Motion Discrimination ◽  
Initial Decision ◽  
Sensory Evidence

AbstractDecisions are occasionally accompanied by changes-of-mind. While considered a hallmark of cognitive flexibility, the mechanisms underlying changes-of-mind remain elusive. Previous studies on perceptual decision making have focused on changes-of-mind that are primarily driven by the accumulation of additional noisy sensory evidence after the initial decision. In a motion discrimination task, we demonstrate that changes-of-mind can occur even in the absence of additional evidence after the initial decision. Unlike previous studies of changes-of-mind, the majority of changes-of-mind in our experiment occurred in trials with prolonged initial response times. This suggests a distinct mechanism underlying such changes. Using a neural circuit model of decision uncertainty and change-of-mind behaviour, we demonstrate that this phenomenon is associated with top-down signals mediated by an uncertainty-monitoring neural population. Such a mechanism is consistent with recent neurophysiological evidence showing a link between changes-of-mind and elevated top-down neural activity. Our model explains the long response times associated with changes-of-mind through high decision uncertainty levels in such trials, and accounts for the observed motor response trajectories. Overall, our work provides a computational framework that explains changes-of-mind in the absence of new post-decision evidence.Authors SummaryWe used limited availability of sensory evidence during a standard motion discrimination task, and demonstrated that changes-of-mind could occur long after sensory information was no longer available. Unlike previous studies, our experiment further indicated that changes-of-mind were strongly linked to slow response time. We used a reduced version of a previously developed neural computational model of decision uncertainty and change-of-mind to account for these experimental observations. Importantly, our model showed that the replication of these experimental results required a strong link between change-of-mind and high decision uncertainty (i.e. low decision confidence), supporting the notion that change-of-mind are related to decision uncertainty or confidence.

scholarly journals Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making

2017 ◽  
Vol 1 (2) ◽  
pp. 166-191 ◽  
Author(s):  
Mohsen Alavash ◽  
Christoph Daube ◽  
Malte Wöstmann ◽  
Alex Brandmeyer ◽  
Jonas Obleser
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Network Dynamics ◽  
Neural Oscillations ◽  
Perceptual Decision Making ◽  
Beta Band ◽  
Perceptual Decision ◽  
Large Scale Network ◽  
Scale Network ◽  
Decision Speed

Perceptual decisions vary in the speed at which we make them. Evidence suggests that translating sensory information into perceptual decisions relies on distributed interacting neural populations, with decision speed hinging on power modulations of the neural oscillations. Yet the dependence of perceptual decisions on the large-scale network organization of coupled neural oscillations has remained elusive. We measured magnetoencephalographic signals in human listeners who judged acoustic stimuli composed of carefully titrated clouds of tone sweeps. These stimuli were used in two task contexts, in which the participants judged the overall pitch or direction of the tone sweeps. We traced the large-scale network dynamics of the source-projected neural oscillations on a trial-by-trial basis using power-envelope correlations and graph-theoretical network discovery. In both tasks, faster decisions were predicted by higher segregation and lower integration of coupled beta-band (∼16–28 Hz) oscillations. We also uncovered the brain network states that promoted faster decisions in either lower-order auditory or higher-order control brain areas. Specifically, decision speed in judging the tone sweep direction critically relied on the nodal network configurations of anterior temporal, cingulate, and middle frontal cortices. Our findings suggest that global network communication during perceptual decision-making is implemented in the human brain by large-scale couplings between beta-band neural oscillations.

Dynamical Foundations of the Neural Circuit for Bayesian Decision Making

2009 ◽  
Vol 102 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Kenji Morita
Keyword(s):  
Decision Making ◽  
Bayesian Inference ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Bayesian Decision ◽  
Biophysical Modeling ◽  
Perceptual Decision Making ◽  
Basic Ideas ◽  
Bayesian Decision Making ◽  
The Brain

On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.

Na–K–Li geochemistry of the Prestige pluton in the Slave Province of the Canadian Shield

1982 ◽  
Vol 19 (3) ◽  
pp. 540-554 ◽  
Author(s):  
Ralph Kretz ◽  
Diane Garrett ◽  
Robert G. Garrett
Keyword(s):  
Large Scale ◽  
Country Rock ◽  
Biotite Granite ◽  
Small Scale ◽  
Fine Grained ◽  
Slave Province ◽  
The Mean ◽  
Physical And Chemical ◽  
Western Half ◽  
The Prestige

In the southwestern Slave Province (Canadian Precambrian Shield), a cluster of 14 muscovite–biotite granite plutons dated at about 2.6 × 109 years was emplaced into a thick succession of Archean greenstone, graywacke, and argillite known as the Yellowknife Supergroup. One of the medium-sized plutons (the Prestige pluton), with an outcrop area of 14 km2, consists of equal portions of quartz, plagioclase, and potash feldspar, and minor muscovite, biotite, and apatite. The presence of muscovite, andalusite, and sillimanite in the metamorphic aureole indicates that the pluton was emplaced at a depth of about 9 km (2.5 kbar (250 MPa)) and a temperature of about 600 °C. The texture is complex, as shown especially by muscovite, which occurs as large crystals, as small oriented inclusions in plagioclase, and as fine-grained aggregates along grain boundaries.The mean density of the Prestige granite is 2.641 g cm−3, which is less than that of the country rock by a factor of 0.96. The mean alkali content is 2.5 wt.% Na, 4.3 wt.% K, and 700 ppm Li (80 samples). Na and K are normally distributed; Li is strongly skewed. Analysis of variance shows that 50–80% of the element variability occurs on a small scale (within 0.25 km2 cells). Some of this variability was possibly produced by chemical transport reactions such as:[Formula: see text]which may also account for some of the textural complexity.Large-scale trends within the Prestige pluton could be detected for K and Li but not for Na. Thus the western half is relatively poor in K, and the narrow margin of the pluton is relatively rich in Li. These trends may be attributed to inhomogeneity within the granite prior to emplacement or to a large-scale migration of alkalies that occurred during the formation of the associated pegmatite dikes.Virtually all of the physical and chemical data that are available for the Prestige pluton are consistent with a model that supposes the granite body was in a totally crystalline, but plastic, condition while it migrated to higher crustal levels, in response to buoyant forces.

scholarly journals An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability

2018 ◽  
Author(s):  
Ziqiang Wei ◽  
Hidehiko Inagaki ◽  
Nuo Li ◽  
Karel Svoboda ◽  
Shaul Druckmann
Keyword(s):  
Decision Making ◽  
Brain Function ◽  
Premotor Cortex ◽  
Neural Correlates ◽  
Neural Population ◽  
Behavioral Variability ◽  
Perceptual Decision Making ◽  
Population Activity ◽  
Long Time ◽  
Dynamical Nature

AbstractAnimals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is understanding the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g. decision making), neuronal heterogeneity and signal to noise difficulties. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics was able to reveal previously unrecognized structure in the organization of population activity. This structure was similar on error and correct trials, suggesting what may be the underlying circuit mechanisms, was able to predict multiple aspects of behavioral variability and revealed long time-scale modulation of population activity.

scholarly journals Inhibition and Excitation Shape Activity Selection: Effect of Oscillations in a Decision-Making Circuit

2019 ◽  
Vol 31 (5) ◽  
pp. 870-896 ◽  
Author(s):  
Thomas Bose ◽  
Andreagiovanni Reina ◽  
James A. R. Marshall
Keyword(s):  
Decision Making ◽  
Neural Circuit ◽  
Selection Effect ◽  
Mechanistic Study ◽  
Nonlinear Feedback ◽  
Oscillatory Phase ◽  
Neural Computations ◽  
Underlying Mechanisms ◽  
Decision Circuit ◽  
And Function

Decision making is a complex task, and its underlying mechanisms that regulate behavior, such as the implementation of the coupling between physiological states and neural networks, are hard to decipher. To gain more insight into neural computations underlying ongoing binary decision-making tasks, we consider a neural circuit that guides the feeding behavior of a hypothetical animal making dietary choices. We adopt an inhibition motif from neural network theory and propose a dynamical system characterized by nonlinear feedback, which links mechanism (the implementation of the neural circuit and its coupling to the animal's nutritional state) and function (improving behavioral performance). A central inhibitory unit influences evidence-integrating excitatory units, which in our terms correspond to motivations competing for selection. We determine the parameter regime where the animal exhibits improved decision-making behavior and explain different behavioral outcomes by making the link between accessible states of the nonlinear neural circuit model and decision-making performance. We find that for given deficits in nutritional items, the variation of inhibition strength and ratio of excitation and inhibition strengths in the decision circuit allows the animal to enter an oscillatory phase that describes its internal motivational state. Our findings indicate that this oscillatory phase may improve the overall performance of the animal in an ongoing foraging task and underpin the importance of an integrated functional and mechanistic study of animal activity selection.

scholarly journals Inhibition and excitation shape activity selection: effect of oscillations in a decision-making circuit

2018 ◽  
Author(s):  
Thomas Bose ◽  
Andreagiovanni Reina ◽  
James A.R. Marshall
Keyword(s):  
Decision Making ◽  
Neural Circuit ◽  
Reproductive Value ◽  
Mechanistic Study ◽  
Perceptual Decision Making ◽  
Substantial Impact ◽  
Oscillatory Phase ◽  
Model Animal ◽  
Foraging Decisions ◽  
Overall Performance

AbstractDecision-making is a complex task and requires adaptive mechanisms that facilitate efficient behaviour. Here, we consider a neural circuit that guides the behaviour of an animal in ongoing binary choice tasks. We adopt an inhibition motif from neural network theory and propose a dynamical system characterized by nonlinear feedback, which links mechanism (the implementation of the neural circuit) and function (increasing reproductive value). A central inhibitory unit influences evidence-integrating excitatory units, which in our terms correspond to motivations competing for selection. We determine the parameter regime where the animal exhibits improved decision-making behaviour, and explain different behavioural outcomes by making the link between bifurcation analysis of the nonlinear neural circuit model and decision-making performance. We find that the animal performs best if it tunes internal parameters of the neural circuit in accordance with the underlying bifurcation structure. In particular, variation of inhibition strength and excitation-over-inhibition ratio have a crucial effect on the decision outcome, by allowing the animal to break decision deadlock and to enter an oscillatory phase that describes its internal motivational state. Our findings indicate that this oscillatory phase may improve the overall performance of the animal in an ongoing foraging task. Our results underpin the importance of an integrated functional and mechanistic study of animal activity selection.Author summaryOrganisms frequently select activities, which relate to economic, social and perceptual decision-making problems. The choices made may have substantial impact on their lives. In foraging decisions, for example, animals aim at reaching a target intake of nutrients; it is generally believed that a balanced diet improves reproductive success, yet little is known about the underlying mechanisms that integrate nutritional needs within the brain. In our study, we address this coupling between physiological states and a decision-making circuit in the context of foraging decisions. We consider a model animal that has the drive to eat or drink. The motivation to select and perform one of these activities (i.e. eating or drinking), is processed in artificial neuronal units that have access to information on how hungry and thirsty the animal is at the point it makes the decision. We show that inhibitory and excitatory mechanisms in the neural circuit shape ongoing binary decisions, and we reveal under which conditions oscillating motivations may improve the overall performance of the animal. Our results indicate that inefficient or pathological decision-making may originate from suboptimal modulation of excitation and inhibition in the neurobiological network.

scholarly journals Spectral signature and behavioral consequence of spontaneous shifts of pupil-linked arousal in human

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ella Podvalny ◽  
Leana E King ◽  
Biyu J He
Keyword(s):  
Decision Making ◽  
Resting State ◽  
Pupil Size ◽  
Large Scale ◽  
Spectral Power ◽  
Brain Activity ◽  
Spectral Signature ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Pupil Constriction

Arousal levels perpetually rise and fall spontaneously. How markers of arousal - pupil size and frequency content of brain activity - relate to each other and influence behavior in humans is poorly understood. We simultaneously monitored magnetoencephalography and pupil in healthy volunteers at rest and during a visual perceptual decision-making task. Spontaneously varying pupil size correlates with power of brain activity in most frequency bands across large-scale resting-state cortical networks. Pupil size recorded at prestimulus baseline correlates with subsequent shifts in detection bias (c) and sensitivity (d'). When dissociated from pupil-linked state, prestimulus spectral power of resting state networks still predicts perceptual behavior. Fast spontaneous pupil constriction and dilation correlate with large-scale brain activity as well but not perceptual behavior. Our results illuminate the relation between central and peripheral arousal markers and their respective roles in human perceptual decision-making.

scholarly journals Prioritized neural computations of socially-relevant signals during perceptual decision-making

2019 ◽  
Author(s):  
E. Meaux ◽  
M. El Zein ◽  
R. Mennella ◽  
V. Wyart ◽  
J. Grèzes
Keyword(s):  
Decision Making ◽  
Neural Coding ◽  
Brain Regions ◽  
Detection Sensitivity ◽  
Perceptual Decision Making ◽  
Eeg Activity ◽  
Behavioral Paradigm ◽  
Neural Computations ◽  
Band Limited ◽  
Socially Relevant

AbstractSocially-relevant signals benefit from prioritized processing, from initial orientation to behavioral choice elaboration. Yet it remains unclear whether such prioritized processing engages specific or similar neural computations as the processing of non-social cues during decision-making. Here, we developed a novel behavioral paradigm in which participants performed two different detection tasks on the same, two-dimensional visual stimuli. We presented morphed facial displays of emotion (from neutral to angry) on top of a morphed colored background (from grey to violet). Participants reported the presence or absence of either emotion (anger) or color (violet) in the stimulus, while ignoring the other task-irrelevant dimension. Importantly, we equalized detection sensitivity across dimensions using an adaptive titration procedure. Computational modeling of electroencephalographic (EEG) activity first revealed that premotor EEG activity scales with the amount of perceptual evidence earlier, around 150ms, when the decision concerns emotion rather than color. Second, participant choice was decoded earlier during emotion (260ms) than color decisions in band-limited EEG power in the same premotor regions. Third, these two effects varied across participants as a function of their social anxiety. Together, these findings indicate that emotion cues benefit from a prioritized neural coding in action-selective brain regions, further supporting their motivational value.

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