Precis of Vigor: Neuroeconomics of movement control

2020 ◽  
pp. 1-10
Author(s):  
Reza Shadmehr ◽  
Alaa A. Ahmed
Keyword(s):  
Decision Making ◽  
New York ◽  
Neural Circuits ◽  
Movement Control ◽  
Brain Structures ◽  
The Other ◽  
Neural Basis ◽  
Hand Control ◽  
Measured Activity ◽  
The Brain

Abstract Why do we run toward people we love, but only walk toward others? Why do people in New York seem to walk faster than other cities? Why do our eyes linger longer on things we value more? There is a link between how the brain assigns value to things, and how it controls our movements. This link is an ancient one, developed through shared neural circuits that on one hand teach us how to value things, and on the other hand control the vigor with which we move. As a result, when there is damage to systems that signal reward, like dopamine and serotonin, that damage not only affects our mood and patterns of decision making, but how we move. In this book, we first ask why in principle evolution should have developed a shared system of control between valuation and vigor. We then focus on the neural basis of vigor, synthesizing results from experiments that have measured activity in various brain structures and neuromodulators, during tasks in which animals decide how patiently they should wait for reward, and how vigorously they should move to acquire it. Thus, the way we move unmasks one of our well-guarded secrets: how much we value the thing we are moving toward.

scholarly journals Consciousness, decision making, and volition: freedom beyond chance and necessity

2021 ◽  
Author(s):  
Hans Liljenström
Keyword(s):  
Decision Making ◽  
Free Will ◽  
Brain Activity ◽  
Brain Structures ◽  
Complex Process ◽  
Neural Information ◽  
Neural Information Processing ◽  
Stochastic Population Model ◽  
The Brain

AbstractWhat is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what “you” decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world.

The Emerging Standard Neurobiological Model of Decision Making

2018 ◽  
Author(s):  
Shih-Wei Wu ◽  
Paul W. Glimcher
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Neural Activity ◽  
First Century ◽  
The Other ◽  
Neoclassical Economic ◽  
Internal Representations ◽  
Neurobiological Model ◽  
Neoclassical Economic Theory ◽  
The Brain

The standard neurobiological model of decision making has evolved, since the turn of the twenty-first century, from a confluence of economic, psychological, and neurosci- entific studies of how humans make choices. Two fundamental insights have guided the development of this model during this period, one drawn from economics and the other from neuroscience. The first derives from neoclassical economic theory, which unambiguously demonstrated that logically consistent choosers behave “as if” they had some internal, continuous, and monotonic representation of the values of any choice objects under consideration. The second insight derives from neurobiological studies suggesting that the brain can both represent, in patterns of local neural activity, and compare, by a process of interneuronal competition, internal representations of value associated with different choices.

scholarly journals Robust parallel decision-making in neural circuits with nonlinear inhibition

2020 ◽  
Vol 117 (41) ◽  
pp. 25505-25516
Author(s):  
Birgit Kriener ◽  
Rishidev Chaudhuri ◽  
Ila R. Fiete
Keyword(s):  
Decision Making ◽  
Neural Circuits ◽  
Fine Tuning ◽  
Neural Computing ◽  
Hick’S Law ◽  
Noisy Input ◽  
Finite Memory ◽  
Winner Take All ◽  
The Brain ◽  
Take All

An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes∼N⁡log(N)time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick’s law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick’s law may be a symptom of near-optimal parallel decision-making with noisy input.

Minding the Emotional Thermostat

2018 ◽  
Author(s):  
Bryan T. Denny ◽  
Kevin N. Ochsner
Keyword(s):  
Social Cognitive ◽  
Cognitive Strategies ◽  
Brain Structures ◽  
Affective Neuroscience ◽  
Affect Dysregulation ◽  
Neural Basis ◽  
Future Directions ◽  
Regulation Of Emotion ◽  
The Brain

This chapter takes a social cognitive affective neuroscience approach to describe the processes and systems to give rise to emotion and the volitional control of emotion. It provides a detailed description of the processes that underlie the regulation of emotion. It introduces and synthesizes the brain structures involved in emotion processing and regulation. There is a particular focus on the role of the ventrolateral, dorsolateral and dorsomedial prefrtonal cortex, amgydala, ventral striatum and insula, and on cognitive strategies such as reappraisal. It provides a critical framework for understanding the underlying behavioral and neural basis for the affect dysregulation observed across personality disorders, and summarizes future directions for this area of investigation.

scholarly journals Crossed activation of thoracic trunk motoneurons by medullary reticulospinal neurons

2019 ◽  
Vol 122 (6) ◽  
pp. 2601-2613
Author(s):  
Brandon K. LaPallo ◽  
Andrea Giorgi ◽  
Marie-Claude Perreault
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Brain Stem ◽  
Neural Circuits ◽  
Single Cells ◽  
The Other ◽  
Medullary Reticular Formation ◽  
Functional Connections ◽  
Cord Injury ◽  
The Brain

Activation of contralateral muscles by supraspinal neurons, or crossed activation, is critical for bilateral coordination. Studies in mammals have focused on the neural circuits that mediate cross activation of limb muscles, but the neural circuits involved in crossed activation of trunk muscles are still poorly understood. In this study, we characterized functional connections between reticulospinal (RS) neurons in the medial and lateral regions of the medullary reticular formation (medMRF and latMRF) and contralateral trunk motoneurons (MNs) in the thoracic cord (T7 and T10 segments). To do this, we combined electrical microstimulation of the medMRF and latMRF and calcium imaging from single cells in an ex vivo brain stem-spinal cord preparation of neonatal mice. Our findings substantiate two spatially distinct RS pathways to contralateral trunk MNs. Both pathways originate in the latMRF and are midline crossing, one at the level of the spinal cord via excitatory descending commissural interneurons (reticulo-commissural pathway) and the other at the level of the brain stem (crossed RS pathway). Activation of these RS pathways may enable different patterns of bilateral trunk coordination. Possible implications for recovery of trunk function after stroke or spinal cord injury are discussed. NEW & NOTEWORTHY We identify two spatially distinct reticulospinal pathways for crossed activation of trunk motoneurons. Both pathways cross the midline, one at the level of the brain stem and the other at the level of the spinal cord via excitatory commissural interneurons. Jointly, these pathways provide new opportunities for repair interventions aimed at recovering trunk functions after stroke or spinal cord injury.

Dynamical Foundations of the Neural Circuit for Bayesian Decision Making

2009 ◽  
Vol 102 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Kenji Morita
Keyword(s):  
Decision Making ◽  
Bayesian Inference ◽  
Neural Circuits ◽  
Neural Circuit ◽  
Bayesian Decision ◽  
Biophysical Modeling ◽  
Perceptual Decision Making ◽  
Basic Ideas ◽  
Bayesian Decision Making ◽  
The Brain

On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.

scholarly journals Effect of afobazole and levodopa on the activity of proline-specific proteinases and adenosine deaminase in blood serum and brain structures of rats with experimental Parkinson's syndrome induced by systemic administration of rotenone

2017 ◽  
Vol 63 (6) ◽  
pp. 565-569
Author(s):  
E.A. Ivanova ◽  
N.N. Zolotov ◽  
I.G. Kapitsa ◽  
V.F. Pozdnev ◽  
E.A. Valdman ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Blood Serum ◽  
Adenosine Deaminase ◽  
Intraperitoneal Administration ◽  
Brain Structures ◽  
The Other ◽  
Parkinson's Syndrome ◽  
Parkinson’S Syndrome ◽  
Increased Activity ◽  
The Brain

Rats with experimental Parkinson’s syndrome induced by seven-day intraperitoneal administration of rotenone at a dose of 2.75 mg/kg have an increased activity of prolylendopeptidase (EC 3.4.21.26, PREP) in blood serum and a decreased activity of adenosine deaminase (EC 3.5.4.4, ADA) in serum and in the prefrontal cortex. PREP and ADA activity in other brain structures (in the striatum, hypothalamus and hippocampus) did not change; dipeptidyl peptidase IV activity (EC 3.4.14.5, DPP-4, CD26) also remained constant in serum and in all the brain structures investigated. Afobazole and levodopa, which exhibit antiparkinsonian activity in this model of Parkinson’s syndrome, decrease elevated PREP activity in serum and increase reduced ADA activity in the prefrontal cortex of rats with the experimental pathology. Meanwhile, treatment with the study drugs was associated with a decrease of ADA activity in the other brain structures.

scholarly journals Retinal outputs depend on behavioural state

2019 ◽  
Author(s):  
Sylvia Schröder ◽  
Nicholas A. Steinmetz ◽  
Michael Krumin ◽  
Marius Pachitariu ◽  
Matteo Rizzi ◽  
...  
Keyword(s):  
Optic Tract ◽  
Brain Structures ◽  
Operating Mode ◽  
Behavioural State ◽  
Retinal Axons ◽  
Calcium Indicator ◽  
Retinal Input ◽  
Measured Activity ◽  
Subcortical Brain Structures ◽  
The Brain

AbstractThe operating mode of the visual system depends on behavioural states such as arousal1,2. This dependence is seen both in primary visual cortex3–7 (V1) and in subcortical brain structures receiving direct retinal input4,8. Here we show that this effect arises as early as in the output of the retina. We first measured activity in a region that receives retinal projections9, the superficial superior colliculus (sSC), and found that this activity strongly depended on behavioural state. This modulation was not mediated by feedback inputs from V1 as it was immune to V1 inactivation. We then used Neuropixels probes10 to record activity in the optic tract, and we found some retinal axons whose activity significantly varied with arousal, even in darkness. To characterize these effects on a larger sample of retinal outputs, we imaged the activity of retinal boutons11,12 in sSC during behaviour using a calcium indicator. The activity of these boutons correlated with arousal as strongly as that of sSC neurons, and this correlation persisted also during darkness. These results reveal a novel property of retinal function in mice, which could be observed only during behaviour: retinal outputs are modulated by behavioural state before they reach the rest of the brain.

scholarly journals Neural Circuits Trained with Standard Reinforcement Learning Can Accumulate Probabilistic Information during Decision Making

2017 ◽  
Vol 29 (2) ◽  
pp. 368-393 ◽  
Author(s):  
Nils Kurzawa ◽  
Christopher Summerfield ◽  
Rafal Bogacz
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Neural Activity ◽  
Neural Circuits ◽  
Likelihood Ratios ◽  
Sensory Inputs ◽  
Probabilistic Information ◽  
Learning Rules ◽  
Log Likelihood ◽  
The Brain

Much experimental evidence suggests that during decision making, neural circuits accumulate evidence supporting alternative options. A computational model well describing this accumulation for choices between two options assumes that the brain integrates the log ratios of the likelihoods of the sensory inputs given the two options. Several models have been proposed for how neural circuits can learn these log-likelihood ratios from experience, but all of these models introduced novel and specially dedicated synaptic plasticity rules. Here we show that for a certain wide class of tasks, the log-likelihood ratios are approximately linearly proportional to the expected rewards for selecting actions. Therefore, a simple model based on standard reinforcement learning rules is able to estimate the log-likelihood ratios from experience and on each trial accumulate the log-likelihood ratios associated with presented stimuli while selecting an action. The simulations of the model replicate experimental data on both behavior and neural activity in tasks requiring accumulation of probabilistic cues. Our results suggest that there is no need for the brain to support dedicated plasticity rules, as the standard mechanisms proposed to describe reinforcement learning can enable the neural circuits to perform efficient probabilistic inference.

scholarly journals Hippocampal-entorhinal transformations in abstract frames of reference

2018 ◽  
Author(s):  
Raphael Kaplan ◽  
Karl J Friston
Keyword(s):  
Decision Making ◽  
Metric Spaces ◽  
The Other ◽  
Frames Of Reference ◽  
Absolute Distance ◽  
Everyday Activity ◽  
Spatial Coordinates ◽  
The Stranger ◽  
The Brain

AbstractKnowing how another’s preferences relate to our own is a central aspect of everyday decision-making, yet how the brain performs this transformation is unclear. Here, we ask whether the putative role of the hippocampal-entorhinal system in transforming relative and absolute spatial coordinates during navigation extends to transformations in abstract decision spaces. During fMRI scanning, subjects learned a stranger’s preference for an everyday activity – relative to one of three personally known individuals – and subsequently decided how the stranger’s preference relates to the other two individuals’ preferences. We found that entorhinal cortex/subiculum signals exhibited reference frame-sensitive responses to the absolute distance between the ratings of the stranger and the familiar choice options. In contrast, striatal signals increased when accurately determining the ordinal position of choice options in relation to the stranger. Paralleling its role in navigation, these data implicate the entorhinal/subicular region in assimilating relatively coded knowledge within abstract metric spaces.

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