Persistent storage capability impairs decision making in a biophysical network model

Standage, Dominic; Paré, Martin

doi:10.1016/j.neunet.2011.05.004

Cited by 22 publications

(31 citation statements)

References 79 publications

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“…Stronger non-selective input produced faster, less accurate decisions in the model. Furman and Wang (2008) did not show network activity under the different non-selective input rates, but it is clear from other modeling studies that the slope of network activity is higher (lower) with stronger (weaker) recurrent dynamics, corresponding to speed (accuracy) emphasis (e.g., Wong and Wang, 2006; Standage and Pare, 2011). Notably, the baseline rates of target-in and target-out movement neurons in the electrophysiological study by Heitz and Schall (2012) were higher (lower) under speed (accuracy) conditions, consistent with the modulation of local-circuit dynamics by a spatially non-selective signal.…”

Section: Three General Mechanistic Hypotheses On the Satmentioning

confidence: 99%

“…Importantly, electrophysiological recordings from neurons responsive to a visual target that is not chosen on a given trial (target-out neurons) typically show a much lower rate of activity than target-in neurons prior to choice selection (e.g., Roitman and Shadlen, 2002; Thomas and Pare, 2007; Bollimunta and Ditterich, 2011; Ding and Gold, 2012). Taken together, increasing activity by target-in neurons and suppressed activity by target-out neurons have been interpreted as revealing competitive interactions between neural decision variables (Usher and McClelland, 2001; Wang, 2002; Albantakis and Deco, 2009; Standage and Pare, 2011). In competing accumulator models, each accumulator can be thought of as a population of neurons responsive to one of the alternatives, where the weight of subtraction corresponds to the strength of inhibition between these populations (Figure 2B).…”

Section: The Bounded Integration Frameworkmentioning

confidence: 99%

“…There are several possibilities, such as spatially non-selective excitation of excitatory neurons (Furman and Wang, 2008; Standage et al, 2013) or the conductance strength of excitatory recurrent synapses (Wong and Wang, 2006; Standage and Pare, 2011). Furman and Wang (2008) used the first of these mechanisms in simulations of an RDM task with a biophysically-based local-circuit model.…”

Section: Three General Mechanistic Hypotheses On the Satmentioning

confidence: 99%

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On the neural implementation of the speed-accuracy trade-off

2014

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Decisions are faster and less accurate when conditions favor speed, and are slower and more accurate when they favor accuracy. This phenomenon is referred to as the speed-accuracy trade-off (SAT). Behavioral studies of the SAT have a long history, and the data from these studies are well characterized within the framework of bounded integration. According to this framework, decision makers accumulate noisy evidence until the running total for one of the alternatives reaches a bound. Lower and higher bounds favor speed and accuracy respectively, each at the expense of the other. Studies addressing the neural implementation of these computations are a recent development in neuroscience. In this review, we describe the experimental and theoretical evidence provided by these studies. We structure the review according to the framework of bounded integration, describing evidence for (1) the modulation of the encoding of evidence under conditions favoring speed or accuracy, (2) the modulation of the integration of encoded evidence, and (3) the modulation of the amount of integrated evidence sufficient to make a choice. We discuss commonalities and differences between the proposed neural mechanisms, some of their assumptions and simplifications, and open questions for future work. We close by offering a unifying hypothesis on the present state of play in this nascent research field.

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Section: Three General Mechanistic Hypotheses On the Satmentioning

confidence: 99%

Section: The Bounded Integration Frameworkmentioning

confidence: 99%

Section: Three General Mechanistic Hypotheses On the Satmentioning

confidence: 99%

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On the neural implementation of the speed-accuracy trade-off

2014

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“…11B). A decaying function was used to mimic visual signals from visual area neurons (Standage and Paré 2011;Trappenberg et al 2001;Wong et al 2007). Figure 11, C and D, show the output responses of the accumulator units (x 1 and x 2 ).…”

Section: Computer Simulationsmentioning

confidence: 99%

Separate evaluation of target facilitation and distractor suppression in the activity of macaque lateral intraparietal neurons during visual search

Nishida

Tanaka

Ogawa

2013

Journal of Neurophysiology

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Nishida S, Tanaka T, Ogawa T. Separate evaluation of target facilitation and distractor suppression in the activity of macaque lateral intraparietal neurons during visual search. J Neurophysiol 110: 2773-2791. First published September 25, 2013 doi:10.1152/jn.00360.2013.-During visual search, neurons in the lateral intraparietal area (LIP) discriminate the target from distractors by exhibiting stronger activation when the target appears within the receptive field than when it appears outside the receptive field. It is generally thought that such target-discriminative activity is produced by the combination of target-related facilitation and distractor-related suppression. However, little is known about how the target-discriminative activity is constituted by these two types of neural modulation. To address this issue, we recorded activity from LIP of monkeys performing a visual search task that consisted of target-present and target-absent trials. Monkeys had to make a saccade to a target in the target-present trials, whereas they had to maintain fixation in the target-absent trials, in which only distractors were presented. By introducing the activity from the latter trials as neutral activity, we were able to separate the target-discriminative activity into targetrelated elevation and distractor-related reduction components. We found that the target-discriminative activity of most LIP neurons consisted of the combination of target-related elevation and distractorrelated reduction or only target-related elevation. In contrast, targetdiscriminative activity composed of only distractor-related reduction was observed for very few neurons. We also found that, on average, target-related elevation was stronger and occurred earlier compared with distractor-related reduction. Finally, we consider possible underlying mechanisms, including lateral inhibitory interactions, responsible for target-discriminative activity in visual search. The present findings provide insight into how neuronal modulations shape targetdiscriminative activity during visual search. visual search; electrophysiology; lateral intraparietal area; nonhuman primate; saccade IN A VISUAL SEARCH, the target must be discriminated from distractors to direct covert attention and/or an overt saccade toward the target locus. Initial visual activity in the lateral intraparietal area (LIP), a crucial area involved in visual search (Liu et al. 2010;Wardak et al. 2002), does not discriminate the target. However, later activity involving stronger discharge rates when the target appears within the receptive field (targetrelated activity) than when it appears outside the receptive field (distractor-related activity) does discriminate the target (Balan et al. 2008;Buschman and Miller 2007;Ipata et al. 2006;Ogawa and Komatsu 2009;Thomas and Paré 2007). It is generally thought that such target-distractor discriminative activity is produced by the facilitation of target-related activity and the suppression of distractor-related activity. However, no systematic study h...

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“…In the simplest case, the decay of persistent activity could be fit with an exponential, and the value of this time constant of decay would explain the time constant of saturation in the dots-task responses. In relevant work from a visual search paradigm, NMDA receptors (which have a distinctively long time course) have been implicated in neural temporal integration (Shen et al, 2010; see also Standage and Paré (2011) for associated modeling). It is likely that cellular mechanisms such as NMDA receptors are critical within a recurrent network architecture (Wang, 2002).…”

Section: Implementation Issuesmentioning

confidence: 99%

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

Huk¹,

Meister²

2012

Front. Integr. Neurosci.

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A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP). In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1) empirical questions not yet answered by existing data; (2) implementation issues related to how neural circuits could actually implement the mechanisms suggested by both extracellular neurophysiology and psychophysics; and (3) ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general “encoding-decoding framework” that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

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Persistent storage capability impairs decision making in a biophysical network model

Cited by 22 publications

References 79 publications

On the neural implementation of the speed-accuracy trade-off

On the neural implementation of the speed-accuracy trade-off

Separate evaluation of target facilitation and distractor suppression in the activity of macaque lateral intraparietal neurons during visual search

Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

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