Bee foraging in uncertain environments using predictive hebbian learning

By P. R. Montague, Peter S. Dayan, Christophe Person, Terrence J. Sejnowski. 1995

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WRITING MEMORIES WITH LIGHT-ADDRESSABLE REINFORCEMENT CIRCUITRY (2009, background): “A variety of neuromorphic circuits for computing such signals have been proposed (Houk et al., 1995; Montague et al., 1995; Schultz et al., 1997; Wörgötter and Porr, 2005), but these remain speculative until actual biological examples are solved.”
Reinforcement Learning: An Introduction (1998, background): “Montague et al. (1995) pointed out that dopamine likely plays a similar role in the vertebrate brain.”
Computational Models of Neuromodulation (1998, methodology): “The same approach has been used elsewhere (Montague, Dayan, Person, & Sejnowski, 1995) to show how an identiﬁed interneuron in the honeybee brain, VUMmx1, could predict reward values of spatial location during foraging.”
Dopamine neurons can represent context-dependent prediction error. (2004, unknown): “In the second contextual task, a rewardDominey, 1995; Montague et al., 1995; Nakahara et al.,indicating light stimulus was presented with the prob2001; Suri and Schultz, 1998). ability that, while fixed overall, was incremented as a However, the exact correspondence between DA refunction of the…”
SPIKING NEURON MODELS Single Neurons , Populations , Plasticity (2002, background): “This term has been termed differential -Hebbian (Roberts, 1999; Xie and Seung, 2000) and plays a certain role in the context of conditioning and reinforcement learning (Montague et al., 1995; Rao and Sejnowski, 2001).”
Evolutionary and Computational Advantages of Neuromodulated Plasticity (2008, methodology): “Belonging to this category, a study in (Montague et al., 1995) employs a multiplicative modulatory effect similar to that in (Abbott, 1990) to simulate reward-based learning in a foraging bee.”
Understanding dopamine and reinforcement learning: The dopamine reward prediction error hypothesis (2011, background): “(28, 29) realized that this makes sense from the point of view of a TD class—and in this case, a Bush and Mosteller (23, 24) class—reward prediction error.”
Actor-critic models of the basal ganglia: new anatomical and computational perspectives (2002, background): “Similar to Montague et al. (1995), the output of the model unit P quite accurately captures the essence of the activity patterns of midbrain dopaminergic neurons in primates and rodents (Montague et al., 1996; Schultz, Dayan, & Montague, 1997), and the corresponding octopaminergic neurons in bees…”
Surprise signals in anterior cingulate cortex: Neuronal encoding of unsigned reward prediction errors driving adjustment in behavior (2011, background): “Although many previous studies have emphasized the importance of reward prediction errors in associative learning (Montague et al., 1995, 1996; Schultz et al., 1997; Fiorillo et al., 2003; Bayer and Glimcher, 2005; Schultz, 2006; Matsumoto and Hikosaka, 2007, 2009), the absolute value of the reward…”
Reinforcement Learning or Active Inference? (2009, background): “From a neurobiological perspective [51–56], it may be that dopamine (encoding mc ) does not encode the prediction error of value but the value of prediction error; i.”
Learning agents for uncertain environments (extended abstract) (1998, background): “There is also neurophysiological evidence that reinforcement learning occurs in bee foraging (Montague et al., 1995) and in songbird vocalization (Doya & Sejnowski, 1995). In this work, it is generally assumed that the reward function is fixed and known. For example, in experiments on bees it is ass”