Action Control
action control
Assistant Professor Nelson Totah
Brief project description: We all know what it feels like to stop ourselves just before making a mistake. How do we stop ourselves from making mistakes? This project will elucidate how the brain detects and stops in-progress mistakes. The postdoctoral scientist will have the opportunity to study single unit spiking and local field potentials in rats during near-mistake movements, which have only been previously studied using EEG in humans. The lab uses a state-of-the-art head-fixed rat-on-a-treadmill paradigm to measure near-mistake behaviors. Rats are trained to run when they see one stimulus and remain immobile when they see another. Near-mistakes occur when the rat initiates an incorrect running response, but realizes their mistake and stops before crossing a response threshold (running distance). Recordings from the anterior cingulate cortex, motor cortex, subthalamic nucleus, and globus pallidus will be used to describe how neural circuits enable response conflict detection and engage immediate action inhibition, as well as adjustments to future behavior after a near-mistake. Optogenetics will be used to link these behavioral neurophysiology findings to the structural connectivity of individual anterior cingulate neurons. Job specifics: • Prior experience with optogenetics experiments is highly beneficial. • Being a capable MATLAB programmer is a strong benefit, but there is also room to improve your programming skills. • Experience with LFP analyses (e.g., cross-region spike-field phase locking) is beneficial. • 5 years (40,800 EUR starting salary with annual raises) funded by the Academy of Finland. • Start date is flexible. The earliest is 1 November 2020. Resources in the lab: • Head-fixed rat-on-a-treadmill behavior with locomotion and pupil size tracking during complex cognitive tasks using visual, auditory, and whisker deflection sensory stimuli • Ultra-flexible, ultra-thin (1 um) multi-electrode probes (with collaborators) • Neuropixels and silicon probe recordings during head-fixed behavior • Active collaborations with computational neuroscientists Resources in Helsinki Institute of Life Science: • AAV Vector, Lenti Virus Vector, and CRISPR/Cas9 Cores • Drug Discovery Unit • Electron Microscopy Core • Small animal SPECT-CT If interested, contact Nelson Totah at nelson.totah@helsinki.fi with your CV and a motivation letter.
Learning in pain: probabilistic inference and (mal)adaptive control
Pain is a major clinical problem affecting 1 in 5 people in the world. There are unresolved questions that urgently require answers to treat pain effectively, a crucial one being how the feeling of pain arises from brain activity. Computational models of pain consider how the brain processes noxious information and allow mapping neural circuits and networks to cognition and behaviour. To date, they have generally have assumed two largely independent processes: perceptual and/or predictive inference, typically modelled as an approximate Bayesian process, and action control, typically modelled as a reinforcement learning process. However, inference and control are intertwined in complex ways, challenging the clarity of this distinction. I will discuss how they may comprise a parallel hierarchical architecture that combines pain inference, information-seeking, and adaptive value-based control. Finally, I will discuss whether and how these learning processes might contribute to chronic pain.
Prefrontal orchestration: Cortical networks for rodent action control
FENS Forum 2024