Alternative time representation in dopamine models.
Dopaminergic neuron activity has been modeled during learning and appetitive behavior, most commonly using the temporal-difference (TD) algorithm. However, a proper representation of elapsed time and of the exact task is usually required for the model to work. Most models use timing elements such as delay-line representations of time that are not biologically realistic for intervals in the range of seconds. The interval-timing literature provides several alternatives. One of them is that timing could emerge from general network dynamics, instead of coming from a dedicated circuit. Here, we present a general rate-based learning model based on long short-term memory (LSTM) networks that learns a time representation when needed. Using a naïve network learning its environment in conjunction with TD, we reproduce dopamine activity in appetitive trace conditioning with a constant CS-US interval, including probe trials with unexpected delays. The proposed model learns a representation of the environment dynamics in an adaptive biologically plausible framework, without recourse to delay lines or other special-purpose circuits. Instead, the model predicts that the task-dependent representation of time is learned by experience, is encoded in ramp-like changes in single-neuron activity distributed across small neural networks, and reflects a temporal integration mechanism resulting from the inherent dynamics of recurrent loops within the network. The model also reproduces the known finding that trace conditioning is more difficult than delay conditioning and that the learned representation of the task can be highly dependent on the types of trials experienced during training. Finally, it suggests that the phasic dopaminergic signal could facilitate learning in the cortex.
Groupe de Recherche sur le Système Nerveux Central (FRSQ), Université de Montréal, Montréal, Canada. francois.rivest@mail.mcgill.ca
François
F
Rivest
John F
JF
Kalaska
Yoshua
Y
Bengio
D000200:Action Potentials; D000465:Algorithms; D001479:Basal Ganglia / Q000502:physiology; D001921:Brain / Q000502:physiology*; D002540:Cerebral Cortex / Q000502:physiology; D003198:Computer Simulation; D003214:Conditioning, Classical / Q000502:physiology; D004298:Dopamine / Q000378:metabolism*; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology; D009483:Neuropsychological Tests; D013997:Time Factors; D013998:Time Perception / Q000502:physiology*
D004298:Dopamine