Modulation for emergent networks: Serotonin and dopamine

Weng, Juyang; Paslaski, Stephen; Daly, James E.; VanDam, Courtland; Brown, Jacob D.

doi:10.1016/j.neunet.2012.11.008

Cited by 19 publications

(10 citation statements)

References 29 publications

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“…In a model inspired by serotonergic neuromodulation related to punishment or harm, Weng and colleagues constructed a neural model where artificial serotonin levels regulated stress or pain in two different tasks (Weng et al, 2013 ). The first was a visual recognition task that investigated how such a system can learn visual cues via a teacher that only provides punishments and reward signals.…”

Section: Models Of Serotonin Neuromodulationmentioning

confidence: 99%

Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments

Avery

Krichmar

2017

Front. Neural Circuits

159

137

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Neuromodulatory systems, including the noradrenergic, serotonergic, dopaminergic, and cholinergic systems, track environmental signals, such as risks, rewards, novelty, effort, and social cooperation. These systems provide a foundation for cognitive function in higher organisms; attention, emotion, goal-directed behavior, and decision-making derive from the interaction between the neuromodulatory systems and brain areas, such as the amygdala, frontal cortex, hippocampus, and sensory cortices. Given their strong influence on behavior and cognition, these systems also play a key role in disease states and are the primary target of many current treatment strategies. The fact that these systems interact with each other either directly or indirectly, however, makes it difficult to understand how a failure in one or more systems can lead to a particular symptom or pathology. In this review, we explore experimental evidence, as well as focus on computational and theoretical models of neuromodulation. Better understanding of neuromodulatory systems may lead to the development of novel treatment strategies for a number of brain disorders.

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Section: Models Of Serotonin Neuromodulationmentioning

confidence: 99%

Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments

Avery

Krichmar

2017

Front. Neural Circuits

159

137

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“…The WWNs have versions that are motivated, such as pain avoidance and pleasure seeking, so that its learning does not need to be supervised [9]. The learned tasks include object recognition under reinforcement learning and autonomous foraging (wandering around) in the presence of a friend and an enemy.…”

Section: Resultsmentioning

confidence: 99%

Brain as an Emergent Finite Automaton: A Theory and Three Theorems

Weng¹

2015

IJIS

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This paper models a biological brain-excluding motivation (e.g., emotions)-as a Finite Automaton in Developmental Network (FA-in-DN), but such an FA emerges incrementally in DN. In artificial intelligence (AI), there are two major schools: symbolic and connectionist. Weng 2011 [1] proposed three major properties of the Developmental Network (DN) which bridged the two schools: 1) From any complex FA that demonstrates human knowledge through its sequence of the symbolic inputs-outputs, a Developmental Program (DP) incrementally develops an emergent FA inside DN through naturally emerging image patterns of the symbolic inputs-outputs of the FA. The DN learning from the FA is incremental, immediate and error-free; 2) After learning the FA, if the DN freezes its learning but runs, it generalizes optimally for infinitely many inputs and actions based on the neuron's inner-product distance, state equivalence, and the principle of maximum likelihood; 3) After learning the FA, if the DN continues to learn and run, it "thinks" optimally in the sense of maximum likelihood conditioned on its limited computational resource and its limited past experience. This paper gives an overview of the FA-in-DN brain theory and presents the three major theorems and their proofs.

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“…The WWNs have versions that are motivated, such as pain avoidance and pleasure seeking, so that its learning does not need to be supervised [41]. The learned tasks include object recognition under reinforcement learning and autonomous foraging (wandering around) in the presence of a friend and an enemy.…”

Section: Resultsmentioning

confidence: 99%

“…[41] proposed a model for how reinforcement learning is carried out in such emergent brain circuits through two types of transmitter systemsserotonin and dopamine. Wang et al 2011 [27] presented a model about how individual neurons use two other types of transmitter systems -acetylcholine and norepinephrine -to automatically estimate uncertainty and novelty so that each neuron can decide where it gets inputs from.…”

Section: Introductionmentioning

confidence: 99%

Brains as naturally emerging turing machines

Weng

2015

2015 International Joint Conference on Neural Networks (IJCNN)

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Abstract-It has been shown that a Developmental Network (DN) can learn any Finite Automaton (FA) [29] but FA is not a general purpose automaton by itself. This theoretical paper presents that the controller of any Turing Machine (TM) is equivalent to an FA. It further models a motivation-free brainexcluding motivation e.g., emotions -as a TM inside a grounded DN -DN with the real world. Unlike a traditional TM, the TM-in-DN uses natural encoding of input and output and uses emergent internal representations. In Artificial Intelligence (AI) there are two major schools, symbolism and connectionism. The theoretical result here implies that the connectionist school is at least as powerful as the symbolic school also in terms of the general-purpose nature of TM. Furthermore, any TM simulated by the DN is grounded and uses natural encoding so that the DN autonomously learns any TM directly from natural world without a need for a human to encode its input and output. This opens the door for the DN to fully autonomously learn any TM, from a human teacher, reading a book, or real world events. The motivated version of DN [31] further enables a DN to go beyond action-supervised learning -so as to learn based on pain-avoidance, pleasure seeking, and novelty seeking [31].

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Modulation for emergent networks: Serotonin and dopamine

Cited by 19 publications

References 29 publications

Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments

Neuromodulatory Systems and Their Interactions: A Review of Models, Theories, and Experiments

Brain as an Emergent Finite Automaton: A Theory and Three Theorems

Brains as naturally emerging turing machines

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