Abstract:
Part I. Sequence learning with hidden neurons in spiking neural networks
Storing and recalling spiking sequences is a general problem the brain needs to solve. It is however unclear what type of biologically plausible learning rule is suited to learn a wide class of spatio-temporal activity patterns in a robust way. We consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper-bound on the Kullback-Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with Spike-Timing Dependent Plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. The learning rule for synapses that target hidden neurons is modulated by a global factor that can be seen as an internally computed reward signal.
Part II. Forgetting in the fruit fly: bug or feature?
Recent experiments revealed that the fruit fly Drosophila melanogaster has a dedicated mechanism for forgetting: blocking the G-protein Rac leads to slower and activating Rac to faster forgetting. This active form of forgetting lacks a satisfactory functional explanation. We investigated optimal decision making for an agent adapting to a stochastic environment where a stimulus may switch between being indicative of reward or punishment. Like Drosophila, an optimal agent shows forgetting with a rate that is linked to the time scale of changes in the environment. Moreover, to reduce the odds of missing future reward, an optimal agent may trade the risk of immediate pain for information gain and thus forget faster after aversive conditioning. A simple neuronal network reproduces these features. Our model supports the view that forgetting is adaptive rather than a consequence of limitations of the memory system.
Part I. Sequence learning with hidden neurons in spiking neural networks
Storing and recalling spiking sequences is a general problem the brain needs to solve. It is however unclear what type of biologically plausible learning rule is suited to learn a wide class of spatio-temporal activity patterns in a robust way. We consider a recurrent network of stochastic spiking neurons composed of both visible and hidden neurons. We derive a generic learning rule that is matched to the neural dynamics by minimizing an upper-bound on the Kullback-Leibler divergence from the target distribution to the model distribution. The derived learning rule is consistent with Spike-Timing Dependent Plasticity in that a presynaptic spike preceding a postsynaptic spike elicits potentiation while otherwise depression emerges. The learning rule for synapses that target hidden neurons is modulated by a global factor that can be seen as an internally computed reward signal.
Part II. Forgetting in the fruit fly: bug or feature?
Recent experiments revealed that the fruit fly Drosophila melanogaster has a dedicated mechanism for forgetting: blocking the G-protein Rac leads to slower and activating Rac to faster forgetting. This active form of forgetting lacks a satisfactory functional explanation. We investigated optimal decision making for an agent adapting to a stochastic environment where a stimulus may switch between being indicative of reward or punishment. Like Drosophila, an optimal agent shows forgetting with a rate that is linked to the time scale of changes in the environment. Moreover, to reduce the odds of missing future reward, an optimal agent may trade the risk of immediate pain for information gain and thus forget faster after aversive conditioning. A simple neuronal network reproduces these features. Our model supports the view that forgetting is adaptive rather than a consequence of limitations of the memory system.
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