Probabilistic Population Codes for Bayesian Decision Making Export

@article{citeulike:5008822, abstract = {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.}, author = {Beck, Jeffrey M. and Ma, Wei J. and Kiani, Roozbeh and Hanks, Tim and Churchland, Anne K. and Roitman, Jamie and Shadlen, Michael N. and Latham, Peter E. and Pouget, Alexandre}, citeulike-article-id = {5008822}, citeulike-linkout-0 = {http://www.cell.com/neuron/abstract/S0896-6273(08)00803-9}, day = {26}, keywords = {bayesian, decisionmaking, fun, popluation, spikes}, month = {December}, number = {6}, pages = {1142--1152}, posted-at = {2009-06-29 21:56:40}, priority = {2}, title = {Probabilistic Population Codes for Bayesian Decision Making}, url = {http://www.cell.com/neuron/abstract/S0896-6273(08)00803-9}, volume = {60}, year = {2008} }

TY - JOUR ID - citeulike:5008822 L3 - citeulike-article-id:5008822 N2 - 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. IS - 6 EP - 1152 TI - Probabilistic Population Codes for Bayesian Decision Making VL - 60 SP - 1142 KW - bayesian KW - decisionmaking KW - fun KW - popluation KW - spikes AU - Beck, Jeffrey AU - Ma, Wei AU - Kiani, Roozbeh AU - Hanks, Tim AU - Churchland, Anne AU - Roitman, Jamie AU - Shadlen, Michael AU - Latham, Peter AU - Pouget, Alexandre PY - 2008/12/26/ UR - http://www.cell.com/neuron/abstract/S0896-6273(08)00803-9 ER -