Assistant Professor - Department of Neuroscience Ph.D., University of Groningen, The Netherlands, 2001 One Baylor Plaza Baylor College of Medicine Houston TX, 77030 Smith Medical Research Bldg Room: T115D Telephone: 713-798-8407 Email: wjma@cpu.bcm.edu Web:neuro.bcm.edu/malab

Sensory information received by the brain is typically uncertain (for instance because of poor signal quality or ambiguity in the world), yet it must constantly be manipulated to generate accurate, task-relevant behavior. In our laboratory, we use a combination of human behavioral experiments, mathematical models of behavior, neural network models, and collaborations with electrophysiologists, investigate how the brain represents, processes, and remembers uncertain information. This is critical for understanding the relationship between neural activity and behavior.

We work in the areas of visual perception, decision-making, visual attention, visual short-term memory, and multisensory perception. We have a particular interest in perceptual behaviors of intermediate complexity, such as determining whether a target object is present in a scene (visual search) or detecting a change between two successive scenes (a short-term memory task). The models we use are probabilistic: they key idea is that human and animal observers perform probabilistic inference on noisy observations.

We have found that humans perform near-optimally in many mid-level visual tasks. We have modeled the neural basis of such near-optimality, generating predictions that have subsequently been confirmed in monkey physiology laboratories. We have also introduced a new theory of short-term memory, in which memory limitations are described in terms of quality instead of quantity.

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Van den Berg R, Shin H, Chou WC, George R, Ma WJ (2012), Variability in encoding precision accounts for visual short-term memory limitations. Proceedings of the National Academy of Sciences, 109 (22), 8780-5.

Beck JM, Ma WJ, Pitkow X, Latham PE, Pouget A (2012), Not noisy, just wrong: the role of suboptimal inference in behavioral variability. Neuron 74 (1), 30-9.

Van den Berg R, Vogel M, Josic K, Ma WJ (2012), Optimal inference of sameness. Proceedings of the National Academy of Sciences 109 (8), 3178-83.

Ma WJ, Pouget A (2008), Linking neurons to behavior in multisensory perception: a Ma WJ, Navalpakkam V, Beck JM, Van den Berg R, Pouget A (2011), Near-optimal visual search: behavior and neural basis. Nature Neuroscience 14, 783-90.

Beck JM, Ma WJ, Kiani R, Hanks TD, Churchland AK, Roitman JD, Shadlen MN, Latham, PE, and Pouget A (2008), Bayesian decision making with probabilistic population codes. Neuron 60, 1142-5.

Ma WJ, Beck JM, Latham PE, Pouget A (2006), Bayesian inference with probabilistic population codes. Nature Neuroscience 9, 1432-8.

Wilken P, Ma WJ (2004), A detection theory account of change detection. Journal of Vision 4, 1120-35, http://journalofvision.org/4/12/11/, doi:10.1167/4.12.11.

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• Hongsup Shin (Neuroscience)

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Optimal integration of two sensory cues using probabilistic population codes. The cues evoke activity in input populations, denoted by vectors r1 and r2, and indicated by green and blue dots. Neurons are ordered by their preferred stimulus. A simple linear combination of input population patterns of activity, r3 = W1r1 + W2r2 (shown in red dots), guarantees optimal cue integration, if neural variability is so-called Poisson-like. This includes independent Poisson variability, but also allows for correlated variability. The dialogue boxes show the probability distributions over the stimulus encoded in the populations on a single trial. Optimal cue integration is characterized by a multiplication of probability distributions over the stimulus, p(s|r3) µ p(s|r1)p(s|r2). W1 and W2 are synaptic weight matrices which depend on the statistics of the input populations, but do not have to be adjusted over trials. This computation can also be implemented using biologically realistic neurons. For more information, see Ma et al., Nature Neuroscience 2006.

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