Emerson F. Harkin

The paper analyzes serotonergic neuron activity during a dynamic Pavlovian task to estimate reward history and its relation to behavior.

This paper proposes a theory that serotonin neurons encode predictive value signals, explaining various serotonergic responses better than existing theories.

The paper uses an augmented integrate-and-fire model to show that the dorsal raphe network encodes both input intensity and its temporal derivative, with the derivative dominating during rapid input increases.

The paper identifies a slow serotonergic recurrent inhibitory network in the dorsal raphe nucleus that generates non-linear dynamics and winner-take-all computations, affecting reward-conditioned behavior.

The paper investigates cell-type specific responses to associative learning in the primary motor cortex using in vivo imaging in mice, finding differential modifications in neuronal activity patterns.

This paper proposes parallel and recurrent cascade models to capture sub-cellular neural computation and integrates them into artificial neural networks for complex tasks.