Spatially modulated grid cells has been recently found in the rat secondary visual cortex (V2) during activation navigation. However, the computational mechanism and functional significance of V2 grid cells remain unknown, and a theory-driven conceptual model for experimentally observed visual grids is missing. To address the knowledge gap and make experimentally testable predictions, here we trained a biologically-inspired excitatory-inhibitory recurrent neural network (E/I-RNN) to perform a two-dimensional spatial navigation task with multisensory (e.g., velocity, acceleration, and visual) input. We found grid-like responses in both excitatory and inhibitory RNN units, and these grid responses were robust with respect to the choices of spatial cues, dimensionality of visual input, activation function, and network connectivity. Dimensionality reduction analysis of population responses revealed a low-dimensional torus-like manifold and attractor, showing the stability of grid patterns with respect to new visual input, new trajectory and relative speed. We found that functionally similar receptive fields with strong excitatory-to-excitatory connection appeared within fully connected as well as structurally connected networks, suggesting a link between functional grid clusters and structural network. Additionally, multistable torus-like attractors emerged with increasing sparsity in inter- and intra-subnetwork connectivity. Finally, irregular grid patterns were found in a convolutional neural network (CNN)-RNN architecture while performing a visual sequence recognition task. Together, our results suggest new computational mechanisms of V2 grid cells in both spatial and non-spatial tasks.
The mediodorsal (MD) thalamus is a critical partner for the prefrontal cortex (PFC) in cognitive flexibility. Animal experiments have shown that the MD enhances prefrontal signal-to-noise ratio (SNR) in decision making under uncertainty. However, the computational mechanisms of this cognitive process remain unclear. Here we use performance-optimized computational models to dissect these mechanisms. We find that the inclusion of an MD-like feedforward module increases robustness to sensory noise and enhances working memory maintenance in the recurrent PFC network performing a context-dependent decision-making task. Incorporating genetically identified thalamocortical pathways that regulate signal amplification and noise reduction further improves performance and replicates key neurophysiological findings of neuronal tuning. Our model reveals a key computational mechanism of context-invariant, cell-type specific regulation of sensory uncertainty in a task-phase specific manner. Additionally, it makes experimentally testable predictions that connect disrupted thalamocortical connectivity with classical theories of prefrontal excitation-inhibition (E/I) imbalance and dysfunctional inhibitory cell types.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.