Real-cyber hybrid neural network for predicting neural circuits in mouse decision-making
Ueoka, Y.; Maeda, H.; Wang, S.; Funamizu, A.
Show abstract
A major challenge in neuroscience is to elucidate how the brain processes sensory input to generate behavior, especially given the difficulty to measure neural activity across the whole brain. To address this limitation, previous studies have used artificial neural networks (ANNs) and modeled decision-making circuits in the brain. Here, we developed a real-cyber hybrid neural network (HNN) that directly input the activity of real neurons to an ANN, imposing biological constraints on the model. Using spike inputs from cortical or subcortical regions, the HNN predicted body movements of head-fixed mice during a task better than the ANN did. This improvement arose not only from the added mouse neuronal activity, but from the activity generated within the HNN. The generated activity potentially included unrecorded neuronal activity from mice, which was difficult for the ANN. We propose that HNNs develop brain-like activity to predict animal movements and compensate for unrecorded neural activity.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Conserved structures of neural activity in sensorimotor cortex of freely moving rats allow cross-subject decoding 97%
- Existing function in primary visual cortex is not perturbed by new skill acquisition of a non-matched sensory task 96%
- Temporal Dynamics of Nucleus Accumbens Neurons in Male Mice During Reward Seeking 96%
Similar papers in this journal
- Network-aware self-supervised learning enables high-content phenotypic screening for genetic modifiers of neuronal activity dynamics 95%
- Sagittarius: Extrapolating Heterogeneous Time-Series Gene Expression Data 94%
- INSCT: Integrating millions of single cells using batch-aware triplet neural networks 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.