Computational role of structure in neural activity and connectivity

Dynamic coding and sequential integration of multiple reward attributes by primate amygdala neurons

The value of visual stimuli guides learning, decision-making, and motivation. Although stimulus values often depend on multiple attributes, how neurons extract and integrate distinct value components from separate cues remains unclear. Here we recorded the activity of amygdala neurons while two male monkeys viewed sequential cues indicating the probability and magnitude of expected rewards. Amygdala neurons frequently signaled reward probability in an abstract, stimulus-independent code that generalized across cue formats. While some probability-coding neurons were insensitive to magnitude information, signaling 'pure' probability rather than value, many neurons showed biphasic responses that signaled probability and magnitude in a dynamic (temporally-patterned) and flexible (reversible) value code. Specific amygdala neurons integrated these reward attributes into risk signals that quantified the variance of expected rewards, distinct from value. Population codes were accurate, mutually transferable between value components, and expressed differently across amygdala nuclei. Our findings identify amygdala neurons as a substrate for the sequential integration of multiple reward attributes into value and risk.

Understanding cognitive processes across spatial scales of the brain

Cognition arises from neural operations at multiple spatial scales, from individual neurons to large-scale networks. Despite extensive research on coding principles and emergent cognitive processes across brain areas, investigation across scales has been limited. Here, we propose ways to test the idea that different cognitive processes emerge from distinct information coding principles at various scales, which collectively give rise to complex behavior. This approach involves comparing brain-behavior associations and the underlying neural geometry across scales, alongside an investigation of global and local scale interactions. Bridging findings across species and techniques through open science and collaborations is essential to comprehensively understand the multiscale brain and its functions.

Recurrent Connectivity Shapes Spatial Coding in Hippocampal CA3 Subregions

Stable and flexible neural representations of space in the hippocampus are crucial for navigating complex environments. However, how these distinct representations emerge from the underlying local circuit architecture remains unknown. Using two-photon imaging of CA3 subareas during active behavior, we reveal opposing coding strategies within specific CA3 subregions, with proximal neurons demonstrating stable and generalized representations and distal neurons showing dynamic and context-specific activity. We show in artificial neural network models that varying the recurrence level causes these differences in coding properties to emerge. We confirmed the contribution of recurrent connectivity to functional heterogeneity by characterizing the representational geometry of neural recordings and comparing it with theoretical predictions of neural manifold dimensionality. Our results indicate that local circuit organization, particularly recurrent connectivity among excitatory neurons, plays a key role in shaping complementary spatial representations within the hippocampus.

Modular representations emerge in neural networks trained to perform context-dependent tasks

The brain has large-scale modular structure in the form of brain regions, which are thought to arise from constraints on connectivity and the physical geometry of the cortical sheet. In contrast, experimental and theoretical work has argued both for and against the existence of specialized sub-populations of neurons (modules) within single brain regions. By studying artificial neural networks, we show that this local modularity emerges to support context-dependent behavior, but only when the input is low-dimensional. No anatomical constraints are required. We also show when modular specialization emerges at the population level (different modules correspond to orthogonal subspaces). Modularity yields abstract representations, allows for rapid learning and generalization on novel tasks, and facilitates the rapid learning of related contexts. Non-modular representations facilitate the rapid learning of unrelated contexts. Our findings reconcile conflicting experimental results and make predictions for future experiments.

A biologically inspired computational model of human ventral temporal cortex

Our minds represent miscellaneous objects in the physical world metaphorically in an abstract and complex high-dimensional object space, which is implemented in a two-dimensional surface of the ventral temporal cortex (VTC) with topologically organized object selectivity. Here we investigated principles guiding the topographical organization of object selectivities in the VTC by constructing a hybrid Self-Organizing Map (SOM) model that harnesses a biologically inspired algorithm of wiring cost minimization and adheres to the constraints of the lateral wiring span of human VTC neurons. In a series of in silico experiments with functional brain neuroimaging and neurophysiological single-unit data from humans and non-human primates, the VTC-SOM predicted the topographical structure of fine-scale category-selective regions (face-, tool-, body-, and place-selective regions) and the boundary in large-scale abstract functional maps (animate vs. inanimate, real-word small-size vs. big-size, central vs. peripheral), with no significant loss in functionality (e.g., categorical selectivity and view-invariant representations). In addition, when the same principle was applied to V1 orientation preferences, a pinwheel-like topology emerged, suggesting the model's broad applicability. In summary, our study illustrates that the simple principle of wiring cost minimization, coupled with the appropriate biological constraint of lateral wiring span, is able to implement the high-dimensional object space in a two-dimensional cortical surface.