Probabilistic population codes for Bayesian decision making

By Jeffrey M. Beck, Wei Ji Ma, Roozbeh Kiani, Timothy D. Hanks, Anne K. Churchland, J. Roitman, Michael N. Shadlen, Peter E. Latham, Alexandre Pouget. Neuron, 2008

674 citations recorded by the source corpus.

When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity, and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal’s performance. We present experimental evidence consistent with this prediction, and discuss other predictions applicable to more general settings.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d65cbace88e7f5285bb07787e95fc3a47b5c0a33

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Citations in the corpus

Perceptual decision making in less than 30 milliseconds (2010, neutral): “…time to a specific color-sensitive area, such as the retina, or area V4, and that it also remains agnostic regarding the biophysical origin of this time scale, i.e., whether it results from slow sensory transduction or from internal noise in more central circuits25,26,36,38, for instance.”
Neural computations underlying arbitration between model-based and model-free learning (2013, methodology): “The notion that the model-based system uses a computationally rich Bayesian inference mechanism to generate reliability estimates is feasible given that this system appears to depend on a large extent of cortex to facilitate its implementation, including parts of parietal cortex that have previously”
Extracting information from neuronal populations: information theory and decoding approaches (2009, background): “It has been shown that populations of neurons can not only compute the most likely stimulus — or the optimal response — but also estimate probability distribution”
Neural computations associated with goal-directed choice. (2010, background): “In a series of recent papers [63 ,64] they have shown that as long as neurons follow approximately a Poisson-like distribution of spike counts, it is possible to make fully optimal Bayesian perceptual decisions using simple linear integration of neural activity, even in the presence of a large numbe”
Changing your mind: a computational mechanism of vacillation (2009, neutral): “They are naturally viewed as an extension of signal detection theory and Bayesian inference to streams of data over time 4,11 .”
Improved probabilistic inference as a general learning mechanism with action video games (2010, background): “…a natural way to compute a posterior distribution (particularly when the coherence varies over trials), we took advantage of a recent neural model of probabilistic decision making [16] in which the state of the network encodes the posterior distribution over choices given the evidence (Figure 2A).”
Neural basis of reinforcement learning and decision making. (2012, background): “Computational studies have demonstrated that a network of neurons with recurrent excitation and lateral inhibition can perform temporal integration of noisy sensory inputs and produce a signal corresponding to an optimal action (Wang 2002, Lo & Wang, 2006, Beck et al. 2008, Furman & Wang 2008).”
Probabilistic vs. non-probabilistic approaches to the neurobiology of perceptual decision-making. (2012, background): “A further advantage of this approach is that – once the decision boundary has been crossed – the most likely stimulus value can be decoded optimally with a separate line attractor network [46 ,58,59].”
How Do Expectations Shape Perception? (2018, background): “While it may seem natural to implement priors with a feedback process, it should be noted that Bayesian inference can be performed in a purely feedforward manner [109,110].”
Intention, action planning, and decision making in parietal-frontal circuits. (2009, background): “In fact, most decision-making models contain a competition between potential plans (Wang, 2008; Beck et al., 2008).”
Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation (2017, methodology): “Future efforts could harness our framework to guide construction of finer-grained Bayesian models incorporating temporal dynamics or candidate neural network implementations (Beck et al., 2008; Fiser, Berkes, Orbán, & Lengyel, 2010; Insabato et al., 2010; Ma & Jazayeri, 2014; Pasquali et al., 2010; ”