Probabilistic reasoning by neurons

By Tianming Yang, Michael N. Shadlen. Nature, 2007

534 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3fbed905bf17b4cc7b16a9350e07fd0fa4807af5

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In Cognitive Neuroscience

Citations in the corpus

Diffusion Decision Model: Current Issues and History (2016, background): “The stimuli have included random dots [44], colored lights [92] and patches [93], line segments [94], sinusoidal gratings [95], clicks and visual pulses [52], and geometric shapes [96], with interelement intervals ranging from a few tens of milliseconds to several hundred milliseconds or longer.”
Attention, learning and the value of information (2012, result): “Similar results are obtained in tasks that manipulate the desirability of a target using different methods, for example by controlling the relative magnitude, probability or delay of its expected reward (Bernacchia et al., 2011; Louie et al., 2011; Sugrue et al., 2004; Yang and Shadlen, 2007).”
Neural mechanisms for interacting with a world full of action choices. (2010, methodology): “…which include the lateral intraparietal area (LIP) (Dorris & Glimcher 2004, Gold & Shadlen 2007, Platt & Glimcher 1999, Sugrue et al. 2004, Yang & Shadlen 2007), the FEF (Coe et al. 2002, Schall & Bichot 1998), and the superior colliculus (Basso & Wurtz 1998, Carello & Krauzlis 2004,…”
The Neurobiology of Decision: Consensus and Controversy (2009, unknown): “…(or decrease) the firing rate of neurons within the frontal-parietal maps associated with that saccade (Dorris and Glimcher, 2004; Janssen and Shadlen, 2005; Kim et al., 2008; Leon and Shadlen, 1999; Leon and Shadlen, 2003; Sugrue et al., 2004; Wallis and Miller, 2003; Yang and Shadlen, 2007).”
Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes (2015, background): “However, neurons apparently encoding probabilistic variables, computed from sensory input, have been found (Yang & Shadlen, 2007) and Soltani and Wang (2010) have shown how neurons may learn to compute such variables from input using biologically plausible mechanisms.”
Dedicated and intrinsic models of time perception. (2008, background): “More generally, some of the behavioral effects attributed to intrinsic mechanisms probably are related to processes not directly involved in representing temporal information (see Box 2). As noted above, activity in LIP neurons that is predictive of psychophysical performance might reflect intrinsic”
The Origin of Behavior (2011, unknown): “Although the specific biological manifestations of behavior are beyond the scope of our analysis, recent imaging and neurophysiological studies of decisionmaking under uncertainty in humans and primates (Breiter, Aharon, Kahneman, Anders, and Shizgat, 2001; Smith, Dickhaut, McCabe, and Pardo, 2002; ”
Human Neuroscience (unknown): “…our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability theory. neuronally (cf. Yang and Shadlen, 2007; Beck et al., 2008; Deneve, 2008).”
Decision theory, reinforcement learning, and the brain (2008, background): “A test of LIP’s report of an integration process with more discretely controlled cues showed promising, but partial results (Yang and Shadlen, 2007), with extreme values of the summed log likelihood ratios failing to be quite correctly represented.”
Neuronal Reward and Decision Signals: From Theories to Data. (2015, background): “Through its generality, such a model would also fit economic decision processes during which LIP neurons code the log likelihood ratio decision variable in a sequential probability ratio test (643), which constitutes an efficient decision mechanism (619).”
Attention, Intention, and Priority in the Parietal Lobe (2010, background): “…process in estimating time (Janssen & Shadlen 2005, Leon & Shadlen 2003, Maimon & Assad 2006), the direction of motion of a noisy stimulus (Churchland et al. 2008, Roitman & Shadlen 2002, Shadlen & Newsome 2001), confidence (Kiani & Shadlen 2009), and probabilistic reasoning (Yang & Shadlen 2007).”
Intention, action planning, and decision making in parietal-frontal circuits. (2009, background): “Additional evidence for a role of LIP and PRR in decision making is the finding that they encode the expected value of the reward for a movement (Platt and Glimcher, 1999; Sugrue et al., 2004; Musallam et al., 2004; Yang and Shadlen, 2007) (Figure 7).”