Topic: frontal cortex

Many decisions under uncertainty entail dynamic belief updating: multiple pieces of evidence informing about the state of the environment are accumulated across time to infer the environmental state, and choose a corresponding action. Traditionally, this process has been conceptualized as a linear and perfect (i.e., without loss) integration of sensory information along purely feedforward sensory-motor pathways. Yet, natural environments can undergo hidden changes in their state, which requires a non-linear accumulation of decision evidence that strikes a tradeoff between stability and flexibi

Nuo Li (Baylor College of Medicine, USA) shares novel insights into coordinated interhemispheric large-scale neural network activity underpinning short-term memory in mice. Relevant techniques covered include: simultaneous multi-regional recordings using multiple 64-channel H probes during head-fixed behavior in mice. simultaneous optogenetics and population recording. analysis of population recordings to infer interactions between brain regions. Reference: Chen G, Kang B, Lindsey J, Druckmann S, Li N, (2021). Modularity and robustness of frontal cortex networks. Cell, 184(14):3717-3730.

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. Th

Many everyday decisions are based on both external cues and internal hunches. How does the brain put these together? We addressed this question in mice trained to make decisions based on sensory stimuli and on past events. While mice made these decisions, we causally probed the roles of cortical areas and recorded from thousands of neurons throughout the brain, with an emphasis on frontal cortex. The results are not what we thought based on textbook notions of how the brain works. This talk is based on work led by Nick Steinmetz, Peter Zatka-Haas, Armin Lak, and Pip Coen, in the laboratory I s

According to the theory of predictive processing, expectations modulate neural activity so as to optimize the processing of sensory inputs expected in the current environment. While there is accumulating evidence that the brain indeed operates under this principle, most of the attention has been placed on mechanisms that rely on static coding properties of neurons. The potential contribution of dynamical features, such as those reflected in the evolution of neural population dynamics, has thus far been overlooked. In this talk, I will present evidence for a novel mechanism for predictive proce

The Potts associative network, a neurally-informed generalization of the Hopfield model, can serve as a simplified representation of global cortical dynamics. Considering memories to be sparsely distributed patterns of local cortical activity, we have studied latching dynamics – the largely random hopping through global attractor states driven by adaptation – as a regime potentially underlying complex cognition such as language production and mind wandering. In a recent study, we have differentiated a Potts associative network into two parts, representing frontal and posterior cortices, to cru