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Moskovitz, T.

Publications and source records attributed to Moskovitz, T..

2 recordsLinked to original sources

Action prediction error: a value-free dopaminergic teaching signal that drives stable learning

Animals choice behavior is characterized by two main tendencies: taking actions that led to rewards and repeating past actions. Theory suggests these strategies may be reinforced by different types of dopaminergic teaching signals: reward prediction error (RPE) to reinforce value-based associations and movement-based action prediction errors to reinforce value-free repetitive associations. Here we use an auditory-discrimination task in mice to show that movement-related dopamine activity in the tail of the striatum encodes the hypothesized action prediction error signal. Causal manipulations reveal that this prediction error serves as a value-free teaching signal that supports learning by reinforcing repeated associations. Computational modeling and experiments demonstrate that action prediction errors alone cannot support reward-guided learning but when paired with the RPE circuitry they serve to consolidate stable sound-action associations in a value-free manner. Together we show that there are two types of dopaminergic prediction errors that work in tandem to support learning.

neuroscience↗

Understanding the functional and structural differences across excitatory and inhibitory neurons

One of the most fundamental organizational principles of the brain is the separation of excitatory (E) and inhibitory (I) neurons. In addition to their opposing effects on post-synaptic neurons, E and I cells tend to differ in their selectivity and connectivity. Although many such differences have been characterized experimentally, it is not clear why they exist in the first place. We studied this question in an artificial neural network equipped with multiple E and I cell types. We found that a deep convolutional recurrent network trained to perform an object classification task was able to capture salient distinctions between E and I neurons. We explored the necessary conditions for the network to develop distinct selectivity and connectivity across cell types. We found that neurons that project to higher-order areas will have greater stimulus selectivity, regardless of whether they are excitatory or not. Sparser connectivity is required for higher selectivity, but only when the recurrent connections are excitatory. These findings demonstrate that the differences observed across E and I neurons are not independent, and can be explained using a smaller number of factors.

neuroscience↗