PT  - JOURNAL ARTICLE
AU  - Haroon Anwar
AU  - Simon Caby
AU  - Salvador Dura-Bernal
AU  - David D’Onofrio
AU  - Daniel Hasegan
AU  - Matt Deible
AU  - Sara Grunblatt
AU  - George L Chadderdon
AU  - Cliff C Kerr
AU  - Peter Lakatos
AU  - William W Lytton
AU  - Hananel Hazan
AU  - Samuel A Neymotin
TI  - Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning
AID  - 10.1101/2021.07.29.454361
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2021.07.29.454361
4099  - http://biorxiv.org/content/early/2022/02/04/2021.07.29.454361.short
4100  - http://biorxiv.org/content/early/2022/02/04/2021.07.29.454361.full
AB  - Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance.Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time.Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward.Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.Competing Interest StatementThe authors have declared no competing interest.