From Computation to Large-scale Neural Circuitry in Human Belief Updating
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 flexibility in response to change. How this adaptive computation is implemented in the brain has remained unknown. In this talk, I will present an approach that my laboratory has developed to identify evidence accumulation signatures in human behavior and neural population activity (measured with magnetoencephalography, MEG), across a large number of cortical areas. Applying this approach to data recorded during visual evidence accumulation tasks with change-points, we find that behavior and neural activity in frontal and parietal regions involved in motor planning exhibit hallmarks signatures of adaptive evidence accumulation. The same signatures of adaptive behavior and neural activity emerge naturally from simulations of a biophysically detailed model of a recurrent cortical microcircuit. The MEG data further show that decision dynamics in parietal and frontal cortex are mirrored by a selective modulation of the state of early visual cortex. This state modulation is (i) specifically expressed in the alpha frequency-band, (ii) consistent with feedback of evolving belief states from frontal cortex, (iii) dependent on the environmental volatility, and (iv) amplified by pupil-linked arousal responses during evidence accumulation. Together, our findings link normative decision computations to recurrent cortical circuit dynamics and highlight the adaptive nature of decision-related long-range feedback processing in the brain.
Modularity and Robustness of Frontal Cortical Networks
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 pervasive role of visuospatial coding
Historically, retinotopic organisation (the spatial mapping of the retina across the cortical surface) was considered the purview of early regions of visual cortex (V1-V4) only and that anterior, more cognitively involved regions abstracted this information away. The contemporary view is quite different. Here, with Advancing technologies and analysis methods, we see that retinotopic information is not simply thrown away by these regions but rather is maintained to the potential benefit of our broader cognition. This maintenance of visuospatial coding extends not only through visual cortex, but is present in parietal, frontal, medial and subcortical structures involved with coordinating-movements, mind-wandering and even memory. In this talk, I will outline some of the key empirical findings from my own work and the work of others that shaped this contemporary perspective.
A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex
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. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.
Merging of cues and hunches by the mouse cortex
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 share with Kenneth Harris
Predictive processing in the macaque frontal cortex during time estimation
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 processing in the temporal domain which relies on neural population dynamics. I will use recordings from the frontal cortex of macaques trained on a time interval reproduction task and show how neural dynamics can be directly related to animals’ temporal expectations, both in a stationary environment and during learning.
Residual population dynamics as a window into neural computation
Neural activity in frontal and motor cortices can be considered to be the manifestation of a dynamical system implemented by large neural populations in recurrently connected networks. The computations emerging from such population-level dynamics reflect the interaction between external inputs into a network and its internal, recurrent dynamics. Isolating these two contributions in experimentally recorded neural activity, however, is challenging, limiting the resulting insights into neural computations. I will present an approach to addressing this challenge based on response residuals, i.e. variability in the population trajectory across repetitions of the same task condition. A complete characterization of residual dynamics is well-suited to systematically compare computations across brain areas and tasks, and leads to quantitative predictions about the consequences of small, arbitrary causal perturbations.