scholarly journals Bump attractor dynamics underlying stimulus integration in perceptual estimation tasks

2021 ◽  
Author(s):  
Jose M. Esnaola-Acebes ◽  
Alex Roxin ◽  
Klaus Wimmer
Keyword(s):  
Network Dynamics ◽  
Network Models ◽  
Relative Strength ◽  
Initial State ◽  
Sensory Stimuli ◽  
Ring Model ◽  
Oriented Stimulus ◽  
Attractor Dynamics ◽  
Sensory Evidence ◽  
Perceptual Estimation

AbstractPerceptual decision and continuous stimulus estimation tasks involve making judgments based on accumulated sensory evidence. Network models of evidence integration usually rely on competition between neural populations each encoding a discrete categorical choice. By design, these models do not maintain information of the integrated stimulus (e.g. the average stimulus direction in degrees) that is necessary for a continuous perceptual judgement. Here, we show that the continuous ring attractor network can integrate a stimulus feature such as orientation and track the stimulus average in the phase of its activity bump. We reduced the network dynamics of the ring model to a two-dimensional equation for the amplitude and the phase of the bump. Interestingly, these reduced equations are nearly identical to an optimal integration process for computing the running average of the stimulus orientation. They differ only in the intrinsic dynamics of the amplitude, which affects the temporal weighting of the sensory evidence. Whether the network shows early (primacy), uniform or late (recency) weighting depends on the relative strength of sensory stimuli compared to the amplitude of the bump and on the initial state of the network. The specific relation between the internal network dynamics and the sensory inputs can be modulated by changing a single parameter of the model, the global excitatory drive. We show that this can account for the heterogeneity of temporal weighting profiles observed in humans integrating a stream of oriented stimulus frames. Our findings point to continuous attractor dynamics as a plausible mechanism underlying stimulus integration in perceptual estimation tasks.

scholarly journals Flexible categorization in perceptual decision making

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Network Models ◽  
Drift Diffusion Model ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Sensory Evidence ◽  
Winner Take All

AbstractPerceptual decisions rely on accumulating sensory evidence. This computation has been studied using either drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both models can account for a large amount of data, it remains unclear whether their dynamics are qualitatively equivalent. Here we show that in the attractor model, but not in the drift diffusion model, an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision states. The increase in the number of transitions leads to a crossover between weighting mostly early evidence (primacy) to weighting late evidence (recency), a prediction we validate with psychophysical data. Between these two limiting cases, we found a novel flexible categorization regime, in which fluctuations can reverse initially-incorrect categorizations. This reversal asymmetry results in a non-monotonic psychometric curve, a distinctive feature of the attractor model. Our findings point to correcting decision reversals as an important feature of perceptual decision making.

scholarly journals Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

2011 ◽  
Vol 105 (2) ◽  
pp. 757-778 ◽  
Author(s):  
Malte J. Rasch ◽  
Klaus Schuch ◽  
Nikos K. Logothetis ◽  
Wolfgang Maass
Keyword(s):  
Network Model ◽  
Network Models ◽  
Cortical Network ◽  
Inhibitory Neurons ◽  
Sensory Stimuli ◽  
Natural Stimuli ◽  
Cortical Areas ◽  
Model Response ◽  
Spiking Activity

A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N -methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network models.

scholarly journals Flexible categorization in perceptual decision making

2020 ◽  
Author(s):  
Genís Prat-Ortega ◽  
Klaus Wimmer ◽  
Alex Roxin ◽  
Jaime de la Rocha
Keyword(s):  
Decision Making ◽  
Large Body ◽  
Network Models ◽  
Perceptual Decision Making ◽  
Drift Diffusion ◽  
Perceptual Decision ◽  
Attractor Dynamics ◽  
Attractor Model ◽  
Winner Take All ◽  
The Brain

AbstractPerceptual decisions require the brain to make categorical choices based on accumulated sensory evidence. The underlying computations have been studied using either phenomenological drift diffusion models or neurobiological network models exhibiting winner-take-all attractor dynamics. Although both classes of models can account for a large body of experimental data, it remains unclear to what extent their dynamics are qualitatively equivalent. Here we show that, unlike the drift diffusion model, the attractor model can operate in different integration regimes: an increase in the stimulus fluctuations or the stimulus duration promotes transitions between decision-states leading to a crossover between weighting mostly early evidence (primacy regime) to weighting late evidence (recency regime). Between these two limiting cases, we found a novel regime, which we name flexible categorization, in which fluctuations are strong enough to reverse initial categorizations, but only if they are incorrect. This asymmetry in the reversing probability results in a non-monotonic psychometric curve, a novel and distinctive feature of the attractor model. Finally, we show psychophysical evidence for the crossover between integration regimes predicted by the attractor model and for the relevance of this new regime. Our findings point to correcting transitions as an important yet overlooked feature of perceptual decision making.

scholarly journals Modeling Network Dynamics

2020 ◽  
pp. 145-171
Keyword(s):  
Communication Networks ◽  
Random Network ◽  
Network Dynamics ◽  
Network Models ◽  
Random Networks ◽  
Social Phenomena ◽  
Disease Propagation ◽  
The Social ◽  
Wide Range ◽  
Model Dynamics

Social relationships and the social networks over these relationships do not occur arbitrarily. However, the random networks dealt with in this chapter are important tools for modeling the networks of these systems. The authors use random networks to understand and to model dynamics regarding the whole social structure. Random network models became the topic of several studies independently from social network analysis in the 1950s. These models were used in the analysis of a wide range of social and non-social phenomena, from electrical and communication networks to the speed and manner of disease propagation. This chapter explores the modeling network dynamics of random networks.

Underdetermination and Perceptual Skepticism

2021 ◽  
pp. 15-34
Author(s):  
Michael Bergmann
Keyword(s):  
Relative Strength ◽  
Evil Demon ◽  
Sensory Evidence ◽  
New Evil Demon Problem ◽  
Underdetermination Argument

This chapter motivates narrowing the book’s focus to a particular kind of argument for perceptual skepticism (the underdetermination argument) and to two main kinds of response to it (inferential anti-skepticism and noninferential anti-skepticism). The first half of the chapter sets aside skeptical arguments (e.g. closure-based arguments) and responses to them (e.g. contextualism, contrastivism, and closure-denial) that overestimate skepticism’s appeal by taking for granted that we don’t know that skeptical hypotheses are false. It also sets aside disjunctivist and “knowledge first” responses to skepticism, both of which underestimate skepticism’s appeal by rejecting the intuitions supporting the New Evil Demon Problem. The second half of the chapter highlights the relative strength of underdetermination arguments for perceptual skepticism, according to which our sensory evidence underdetermines the truth of our perceptual beliefs based on it. This underdetermination problem requires us to be able to infer the likely truth of our perceptual beliefs via good arguments from our sensory evidence, if our perceptual beliefs are to be justified. Given that we aren’t able to make such inferences, the underdetermination argument concludes that our perceptual beliefs aren’t justified. The inferential anti-skeptic’s response insists that we are able to make such inferences. The noninferential anti-skeptic’s response says that, despite the underdetermination problem, our perceptual beliefs can be justified even if we aren’t able to infer their likely truth via good arguments from our sensory evidence.

scholarly journals Toward Improving Diagnostic Strategies in Chronic Disorders of Consciousness: An Overview on the (Re-)Emergent Role of Neurophysiology

2020 ◽  
Vol 10 (1) ◽  
pp. 42 ◽  
Author(s):  
Luana Billeri ◽  
Serena Filoni ◽  
Emanuele Francesco Russo ◽  
Simona Portaro ◽  
David Militi ◽  
...  
Keyword(s):  
Resting State ◽  
Minimally Conscious State ◽  
Chronic Phase ◽  
Network Models ◽  
Diagnostic Procedures ◽  
Conscious State ◽  
Brain Network ◽  
Disorder Of Consciousness ◽  
Sensory Stimuli ◽  
Diagnostic Strategies

The differential diagnosis of patients with Disorder of Consciousness (DoC), in particular in the chronic phase, is significantly difficult. Actually, about 40% of patients with unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) are misdiagnosed. Indeed, only advanced paraclinical approaches, including advanced EEG analyses, can allow achieving a more reliable diagnosis, that is, discovering residual traces of awareness in patients with UWS (namely, functional Locked-In Syndrome (fLIS)). These approaches aim at capturing the residual brain network models, at rest or that may be activated in response to relevant stimuli, which may be appropriate for awareness to emerge (despite their insufficiency to generate purposeful motor behaviors). For this, different brain network models have been studied in patients with DoC by using sensory stimuli (i.e., passive tasks), probing response to commands (i.e., active tasks), and during resting-state. Since it can be difficult for patients with DoC to perform even simple active tasks, this scoping review aims at summarizing the current, innovative neurophysiological examination methods in resting state/passive modality to differentiate and prognosticate patients with DoC. We conclude that the electrophysiologically-based diagnostic procedures represent an important resource for diagnosis, prognosis, and, therefore, management of patients with DoC, using advance passive and resting state paradigm analyses for the patients who lie in the “greyzones” between MCS, UWS, and fLIS.

scholarly journals EIGENMODE OF THE DECISION-BY-MAJORITY PROCESS IN COMPLEX NETWORKS

2008 ◽  
Vol 11 (04) ◽  
pp. 565-579
Author(s):  
MAKOTO UCHIDA ◽  
SUSUMU SHIRAYAMA
Keyword(s):  
Complex Networks ◽  
Network Models ◽  
Ising Models ◽  
Final States ◽  
Zero Temperature ◽  
Opinion Formation ◽  
Initial State ◽  
Final State ◽  
Numerical Studies ◽  
Eigenmode Analysis

The nature of the dynamics of opinion formation or zero-temperature Ising models modeled as a decision-by-majority process in complex networks is investigated using eigenmode analysis. The Hamiltonian of the system is defined and estimated by eigenvectors of the adjacency matrix constructed from several network models. The rule of the process is assumed to be equivalent to the minimization of the Hamiltonian. The initial and final states of the dynamics are decomposed on the basis of the eigenvectors. The process and the eigenmodes are analyzed by numerical studies. We show that the magnitude of the coefficient for the largest eigenvector at the initial states is the key determinant for the resulting dynamics. We thus prove that the final state of the dynamics can be estimated by the eigenmodes of the initial state.

scholarly journals Using confidence inferred from pupil-size to dissect perceptual task-strategy: support for a bounded decision-formation process

2018 ◽  
Author(s):  
Katsuhisa Kawaguchi ◽  
Stephane Clery ◽  
Paria Pourriahi ◽  
Lenka Seillier ◽  
Ralf Haefner ◽  
...  
Keyword(s):  
Pupil Size ◽  
Neuronal Response ◽  
Perceptual Task ◽  
Neural Basis ◽  
Decision Confidence ◽  
Attractor Dynamics ◽  
Task Strategy ◽  
Decision Area ◽  
Sensory Evidence ◽  
Account Due

During perceptual decisions subjects often rely more strongly on early rather than late sensory evidence even in tasks when both are equally informative about the correct decision. This early psychophysical weighting has been explained by an integration-to-bound decision process, in which the stimulus is ignored after the accumulated evidence reaches a certain bound, or confidence level. Here, we derive predictions about how the average temporal weighting of the evidence depends on a subject’s decision-confidence in this model. To test these predictions empirically, we devised a method to infer decision-confidence from pupil size in monkeys performing a disparity discrimination task. Our animals’ data confirmed the integration-to-bound predictions, with different internal decision-bounds accounting for differences between animals. However, the data could not be explained by two alternative accounts for early psychophysical weighting: attractor dynamics either within the decision area or due to feedback to sensory areas, or a feedforward account due to neuronal response adaptation. This approach also opens the door to using confidence more broadly when studying the neural basis of decision-making.

scholarly journals Formulation of Pruning Maps with Rhythmic Neural Firing

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1247
Author(s):  
Feng-Sheng Tsai ◽  
Yi-Li Shih ◽  
Chin-Tzong Pang ◽  
Sheng-Yi Hsu
Keyword(s):  
Network Dynamics ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Oscillatory Activity ◽  
Control Network ◽  
Neural Function ◽  
Network System ◽  
Neural Firing ◽  
Dynamical Network ◽  
Neuronal Connections

Rhythmic neural firing is thought to underlie the operation of neural function. This triggers the construction of dynamical network models to investigate how the rhythms interact with each other. Recently, an approach concerning neural path pruning has been proposed in a dynamical network system, in which critical neuronal connections are identified and adjusted according to the pruning maps, enabling neurons to produce rhythmic, oscillatory activity in simulation. Here, we construct a sort of homomorphic functions based on different rhythms of neural firing in network dynamics. Armed with the homomorphic functions, the pruning maps can be simply expressed in terms of interactive rhythms of neural firing and allow a concrete analysis of coupling operators to control network dynamics. Such formulation of pruning maps is applied to probe the consolidation of rhythmic patterns between layers of neurons in feedforward neural networks.

scholarly journals From the Periphery to the Core: Information Brokerage in an Evolving Network

2018 ◽  
Author(s):  
Bo Yan ◽  
Yiping Liu ◽  
Jiamou Liu ◽  
Yijin Cai ◽  
Hongyi Su ◽  
...  
Keyword(s):  
Network Dynamics ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Models ◽  
Relationship Building ◽  
Superior Performance ◽  
Alternative Methods ◽  
Real World Datasets ◽  
And Performance ◽  
Interpersonal Ties

Interpersonal ties are pivotal to individual efficacy, status and performance in an agent society.This paper explores three important and interrelated themes in social network theory: the center/periphery partition of the network; network dynamics; and social integration of newcomers. We tackle the question: How would a newcomer harness information brokerage to integrate into a dynamic network going from periphery to center? We model integration as the interplay between the newcomer and the dynamics network and capture information brokerage using a process of relationship building. We analyze theoretical guarantees for the newcomer to reach the center through tactics; proving that a winning tactic always exists for certain types of network dynamics. We then propose three tactics and show their superior performance over alternative methods on four real-world datasets and four network models. In general, our tactics place the newcomer to the center by adding very few new edges on dynamic networks with ~14000 nodes.

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