The dopamine circuit as a reward-taxis navigation system

Mapping Intimacies ◽

10.1101/2021.04.15.439955 ◽

2021 ◽

Author(s):

Omer Karin ◽

Uri Alon

Keyword(s):

Logarithmic Derivative ◽

Mechanistic Model ◽

Bacterial Chemotaxis ◽

Experimental Tests ◽

Matching Law ◽

Complex Case ◽

Level Function ◽

Quantitative Understanding ◽

Midbrain Dopamine ◽

Fold Change Detection

AbstractResearch on certain circuits in simple organisms, such as bacterial chemotaxis, has enabled the formulation of mathematical design principles, leading to ever more precise experimental tests, catalyzing quantitative understanding. It would be important to map these principles to the far more complex case of a vertebrate behavioral circuit. Here, we provide such a mapping for the midbrain dopamine system. Dopamine transmission plays a key role in learning, motivation, and movement, but its systems-level function is not fully understood. We develop a minimal mechanistic model of the dopamine circuit based on physiological and behavioral data, and show that it can be mapped mathematically to the bacterial chemotaxis circuit. Just as chemotaxis robustly climbs attractant gradients, the dopamine circuit performs ‘reward-taxis’ where the attractant is the expected value of reward. The reward-taxis mechanism is based on a circuit feature called fold-change detection, where the circuit outputs the temporal logarithmic derivative of expected reward. The model can explain the general matching law, in which the ratio of responses to concurrent rewards goes as the reward ratio to the power β. It provides an accurate mechanistic value for β as the average gain/baseline ratio of the dopaminergic neurons. Reward-taxis provides testable etiologies for specific dopamine-related disorders.

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A New Dynamic Wettability-Alteration Model for Oil-Wet Cores During Surfactant-Solution Imbibition

SPE Journal ◽

10.2118/153329-pa ◽

2013 ◽

Vol 18 (05) ◽

pp. 818-828 ◽

Cited By ~ 7

Author(s):

M. Hosein Kalaei ◽

Don W. Green ◽

G. Paul Willhite

Keyword(s):

Experimental Data ◽

Oil Recovery ◽

History Matching ◽

Mechanistic Model ◽

Surfactant Solution ◽

Experimental Tests ◽

Quantitative Agreement ◽

Wettability Alteration ◽

Porous Rock ◽

Surfactant Solutions

Summary Wettability modification of solid rocks with surfactants is an important process and has the potential to recover oil from reservoirs. When wettability is altered by use of surfactant solutions, capillary pressure, relative permeabilities, and residual oil saturations change wherever the porous rock is contacted by the surfactant. In this study, a mechanistic model is described in which wettability alteration is simulated by a new empirical correlation of the contact angle with surfactant concentration developed from experimental data. This model was tested against results from experimental tests in which oil was displaced from oil-wet cores by imbibition of surfactant solutions. Quantitative agreement between the simulation results of oil displacement and experimental data from the literature was obtained. Simulation of the imbibition of surfactant solution in laboratory-scale cores with the new model demonstrated that wettability alteration is a dynamic process, which plays a significant role in history matching and prediction of oil recovery from oil-wet porous media. In these simulations, the gravity force was the primary cause of the surfactant-solution invasion of the core that changed the rock wettability toward a less oil-wet state.

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Robust and Efficient Feedrate Selection for 3-Axis Machining

10.1115/imece2000-2369 ◽

2000 ◽

Author(s):

Jeffrey G. Hemmett ◽

Barry K. Fussell ◽

Robert B. Jerard

Keyword(s):

End Milling ◽

Research Effort ◽

Mechanistic Model ◽

Geometric Model ◽

Experimental Tests ◽

Cnc Machining ◽

Sculptured Surface ◽

Surface Machining ◽

Sculptured Surface Machining ◽

Selection Of

Abstract This research effort is focused on improving the efficiency of CNC machining by automatic computer selection of feedrate for 3-axis sculptured surface machining. A feedrate process planner for complex sculptured end milling cuts is described and the geometric model of ball end milling is developed in detail. For each tool move, the geometric model calculates the cut geometry, and a mechanistic model is used along with a maximum allowable cutting force to determine a desired feedrate. The results are written into the part program resulting in a file with optimized feedrates. Experimental tests on a sculptured surface demonstrate the robustness and efficiency of the algorithms in maintaining a desired force.

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Matching Behavior as a Tradeoff Between Reward Maximization and Demands on Neural Computation

F1000Research ◽

10.12688/f1000research.6574.1 ◽

2015 ◽

Vol 4 ◽

pp. 147 ◽

Cited By ~ 3

Author(s):

Jan Kubanek ◽

Lawrence H. Snyder

Keyword(s):

Optimal Strategy ◽

Mechanistic Model ◽

Matching Law ◽

Neural Computation ◽

Comparison Function ◽

Strict Sense ◽

Neural Noise ◽

General Law ◽

Value Coding ◽

Foraging Task

When faced with a choice, humans and animals commonly distribute their behavior in proportion to the frequency of payoff of each option. Such behavior is referred to as matching and has been captured by the matching law. However, matching is not a general law of economic choice. Matching in its strict sense seems to be specifically observed in tasks whose properties make matching an optimal or a near-optimal strategy. We engaged monkeys in a foraging task in which matching was not the optimal strategy. Over-matching the proportions of the mean offered reward magnitudes that would yield more reward than matching, yet, surprisingly, the animals almost exactly matched them. To gain insight into this phenomenon, we modeled the animals' decision-making using a mechanistic model. The model accounted for the animals' macroscopic and microscopic choice behavior. When the models' three parameters were not constrained to mimic the monkeys' behavior, the model over-matched the reward proportions and in doing so, harvested substantially more reward than the monkeys. This optimized model revealed a marked bottleneck in the monkeys' choice function that compares the value of the two options. The model featured a very steep value comparison function relative to that of the monkeys. The steepness of the value comparison function had a profound effect on the earned reward and on the level of matching. We implemented this value comparison function through responses of simulated biological neurons. We found that due to the presence of neural noise, steepening the value comparison requires an exponential increase in the number of value-coding neurons. Matching may be a compromise between harvesting satisfactory reward and the high demands placed by neural noise on optimal neural computation.

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Matching Behavior as a Tradeoff Between Reward Maximization and Demands on Neural Computation

F1000Research ◽

10.12688/f1000research.6574.2 ◽

2015 ◽

Vol 4 ◽

pp. 147

Author(s):

Jan Kubanek ◽

Lawrence H. Snyder

Keyword(s):

Optimal Strategy ◽

Mechanistic Model ◽

Matching Law ◽

Neural Computation ◽

Comparison Function ◽

Strict Sense ◽

Neural Noise ◽

General Law ◽

Value Coding ◽

Foraging Task

When faced with a choice, humans and animals commonly distribute their behavior in proportion to the frequency of payoff of each option. Such behavior is referred to as matching and has been captured by the matching law. However, matching is not a general law of economic choice. Matching in its strict sense seems to be specifically observed in tasks whose properties make matching an optimal or a near-optimal strategy. We engaged monkeys in a foraging task in which matching was not the optimal strategy. Over-matching the proportions of the mean offered reward magnitudes would yield more reward than matching, yet, surprisingly, the animals almost exactly matched them. To gain insight into this phenomenon, we modeled the animals' decision-making using a mechanistic model. The model accounted for the animals' macroscopic and microscopic choice behavior. When the models' three parameters were not constrained to mimic the monkeys' behavior, the model over-matched the reward proportions and in doing so, harvested substantially more reward than the monkeys. This optimized model revealed a marked bottleneck in the monkeys' choice function that compares the value of the two options. The model featured a very steep value comparison function relative to that of the monkeys. The steepness of the value comparison function had a profound effect on the earned reward and on the level of matching. We implemented this value comparison function through responses of simulated biological neurons. We found that due to the presence of neural noise, steepening the value comparison requires an exponential increase in the number of value-coding neurons. Matching may be a compromise between harvesting satisfactory reward and the high demands placed by neural noise on optimal neural computation.

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