Twenty-Five Lessons from Computational Neuromodulation

Neural processing faces three rather different, and perniciously tied, communication problems. First, computation is radically distributed, yet point-to-point interconnections are limited. Second, the bulk of these connections are semantically uniform, lacking differentiation at their targets that could tag particular sorts of information. Third, the brain's structure is relatively fixed, and yet different sorts of input, forms of processing, and rules for determining the output are appropriate under different, and possibly rapidly changing, conditions. Neuromodulators address these problems by their multifarious and broad distribution, by enjoying specialized receptor types in partially specific anatomical arrangements, and by their ability to mold the activity and sensitivity of neurons and the strength and plasticity of their synapses. Here, I offer a computationally focused review of algorithmic and implementational motifs associated with neuromodulators, using decision making in the face of uncertainty as a running example.