Biological learning

…experiments have shown that also learning is metabolically costly. We will present estimates of this cost and introduce a general model of this cost, and compare it to costs in computers. Next, we turn to a supervised artificial network setting and explore a number of strategies that cansave energy need for plasticity. Either by modifying the objective function, by restricting plasticity, or by using less costly transient forms of plasticity. Finally, we will disc

Deep reinforcement learning (RL) has successfully trained machines to excel in video games, board games, and robotics. It has been argued that together, deep RL and robotics can be used to study biological learning by virtue of being embodied intelligence in the real world (Tan et al., 2021). In fact, RL itself was originally formulated to model animal behavior (Sutton & Barto, 1998). Here we go one step further and directly interface deep RL agents with a living neural network: that of the nematode C. elegans. We present a hybrid deep RL - C. elegans closed-loop computational system where