Receptor indirection
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The constraints are described. 
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Receptor structural hierarchy and indirection

Summary
Read Montague explores how brains make decisions.  In particular he explains how:
Why Choose This Book?
In Read Montague's book 'Why Choose This Book?' he explains that in order to survive, mobile creatures must be able to value the resources available to them and the choices they can make to acquire those resources.  An animal traveling in one direction forgoes resources that lie in the opposite direction.  Hence Montague argues that every movement (of eyes are major sensors in primates, based on opsins deployed in the retina & especially fovea, signalling the visual system: Superior colliculi, Thalamus (LGN), Primary visual cortex; and indirectly the amygdala.  They also signal [social] emotional state to other people.  And they have implicit censorious power with pictures of eyes encouraging people within their view to act more honorably.  Eyes are poor scanners and use a saccade to present detail slowly to the fovea.  The eye's optical structures and retina are supported by RPE.  Eyes do not connect to the brain through the brain stem and so still operate in locked-in syndrome.  Evo-devo shows eyes have deep homology.  High pressure within the eye can result in glaucoma.  Genetic inheritance can result in retinoblastoma.  Age is associated with AMD. 
, flagella, a set of whip shaped structures (flagellum) which can be actively moved. 
etc. ...) equates to a choice.  Montague says competition has selected animals that have succeeded in valuing their intended actions quickly and accurately. 

Complex goals don't have to be defined explicitly, but rather are defined
Rather than oppose the direct thrust of some environmental flow agents can improve their effectiveness with indirect responses.  This page explains how agents are architected to do this and discusses some examples of how it can be done. 
indirectly
through the modulation of associations of sensory inputs, and predicted situation by guidance signals. 

Guidance signals
Guidance signals are error signals that tell the system how to adjust when deviations from the goal state occur.  The deviations from the goal state provide a terrific model for the concept of desires, which inform the system how to adjust in order to move closer to achieving the goal state. 

Those movements which are over-and-done in less time than it takes for the feedback loop to operate require Plan-ahead. 

Evolved Plan-ahead
Natural selection for one of the ballistic movements (hammering, clubbing, and throwing) could evolve a
Representing state in emergent entities is essential but difficult.  Various structures are used to enhance the rate and scope of state transitions.  Examples are discussed. 
plan-ahead serial buffer
for hand-arm commands that would benefit other ballistic movements as well. 

The existence of a simulation system allows for predictions of what could have been.  Montague suggests this is central to the brains learning strategies.  By valuing the results of the simulated scenarios the "best" current strategy can be selected. 

The brain seeks goals by following the advice of critic signals (guidance signals) that report the deviation of our actual experience from our internally represented goals.  The internal goals are compared to real or imagined (simulated) experience to produce ongoing criticism in the form of identifiable critic signals. 

There are separate brain mechanisms that use this criticism to adjust behavior or to learn (store information).  The guidance signals are generated continuously. 

Montague suggests that the brain ignores a lot of the criticism that is generated. 

The critic signals are in two parts:
  1. A piece representing immediate feedback - which assigns a value to each state of the creature. 
  2. A piece representing long term judgments about the future - a stored value function that represents a judgment about the long term value of each state. 
The brain has been able to reuse an old reward-chasing system but with abstract ideas plugged in to the reward slot.  This allows ideas to act as rewards. 

The brain's critic signals do not have a perfect bird's eye view of what must be done.  They use stored memories, rich models of similar problems and then make educated guesses about the value of possible future actions. 

Critic systems broadcast these error signals to multiple regions of the brain using long neural connections, a network of interconnected neurons which perform signalling, modeling and control functions.  In Cajal's basic neural circuits the signalling is unidirectional.  He identified three classes of neurons in the circuits:
  • Sensory, Interneurons, Motor; which are biochemically distinct and suffer different disease states. 
.  The recipients map the
Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
The participation of agents in flows is introduced and some implications of this are outlined. 
agents
' states to its actions.  At least three functions are fed:
  • Goal selection,
  • Learning and
  • Decision making.
The critic signals are abstract.  They deal with different goals and actions using the same signalling.  It is the context that implies the meaning.  A key critic signal in the brain is the dopamine system. 
The Dopamine system
Like other neuromodulatory systems the Dopamine is a synaptic signal supporting generalized goal-directed behavior & anticipation of reward.  Its significance is that the receptors that detect the signal are of the slow acting type and are used to alter (modulate) the response of fast acting dopaminergic neural circuits in which the receptors are deployed (LTP).  The signal detects significant changes including predictions of models and actual results which differ unexpectedly.  The dopamine network architecture is designed to signal the possibility of any type of reward: Norm violation punishment, Winning a lottery, & Misfortune of an envied competitor.  Dopamine signalling:
  • Rescales continuously to accommodate the range of intensity offered by different stimuli.  So dopamine's responses to any reward habituate.  GABA is released by some tegmental neurons to induce habituation. 
  • Reflects the anticipation of reward.  It supports establishment of a relationship between a signal, working for a reward and obtaining the reward, but subsequently dopamine is mainly released encouraging the work, right after the signal supporting anticipation of the reward.  Anticipation requires learning and is reflected in hippocampus activity.  That explains context dependent cravings.  And the learning architecture means reliable cues become rewarding.  The accumbens supports willpower.  And dopamine
  • Promotes goal-oriented behavior needed to obtain & likely to achieve the reward - through the dopamine projections to the prefrontal cortex.  That makes dopamine central to:
    • Motivation.  This binding fails in depression - due to stress and in anxiety - due to signals from the amygdala.  
    • The prefrontal cortex's mesocortically stimulated support for willpower to act to delay rewards.  To sustain work for delayed rewards additional dopamine is released based on the length of the delay and the rewards uncertainty (modelled in the dorsolateral prefrontal cortex - which promotes the long term and the ventromedial prefrontal cortex - which promotes the short term) and the anticipated size of the reward (modelled in the accumbens).  Impulsiveness in ADHD is reflected in abnormal dopamine processing.  Addictive drugs bias the dopamine network towards impulsiveness.  
  • Is lowered by certain gene variants which induce: less dopamine in the synapse, fewer receptors, lower responsiveness of receptors; associated with (as tiny effects in hugely varying social scenarios): sensation seeking, risk taking, attentional problems, extroversion; where:
    • The receptor D4's gene shows high variability.  The D47R form is relatively unresponsive to dopamine.  
    • Dopamine is degraded by COMT.  The COMT gene includes a variant which is highly efficient reducing dopamine signalling but with complicating gene/environment interactions.  
    • Dopamine is removed from the synapse by a reuptake transporter DAT. 
system:
  1. Acts relatively slowly
  2. Broadcasts signals to many areas of the brain. 
Schultz showed that the dopamine system sends a critic signal to diverse functions within the brain interested in rewards.  The signal is encoded as a change in frequency of the transmissions.  When the frequency changes recipient functions associate the -ve. or +ve. signal with their current state/actions.  Reinforcement learning occurs because the dopamine systems outputs tell the recipients that the reward from their function is:
  • Same rate - as expected
  • Faster bursts - reward more than expected
  • Slower rate - Reward less than expected.
Hence, when dopamine system signals become very weak, as in Parkinson's disease corresponds to the breakdown of certain interneurons in the brain.  It is not fully understood why this occurs.  Dopamine system neuron breakdown generates the classical symptoms of tremors and rigidity.  In some instances an uncommon LRRK2 gene mutation confers a high risk of Parkinson's disease.  In rare cases Italian and Greek families are impacted in their early forties and fifties resulting from a single letter mutation in alpha-synuclein which alters the alpha-synuclein protein causing degeneration in the substantia nigra.  But poisoning from MPTP has also been shown to destroy dopamine system neurons.  Paraquat has also been linked to Parkinson's disease.  Parkinson's disease does not directly kill many sufferers.  But it impacts swallowing which encourages development of pneumonia through inhaling or aspirating food.  And it undermines balance which can increase the possibility of falls.  Dememtia can also develop. 
, the higher functions do not recognize that there are salient changes.  In effect the functions stay the same since they are not hearing that there is reason to change.  This results in the apparent freezing of the motor functions (stiffness) typical of these sufferers. 

Context is the key to the dopamine system.  Montague argues that this relates to the way a person's brain "frames the situation". 

Montague argues that a "superpower", the ability of brains to take an idea and make it the driver of their decision processes - however adversely this may impact their actual survival, is enabled by the dopamine architecture.  Ideas act as reward signals from the point of view of the prediction error systems.  He explains "Ideas play the role of reward signals fed to the dopamine neurons, specialized eukaryotic cells include channels which control flows of sodium and potassium ions across the massively extended cell membrane supporting an electro-chemical wave which is then converted into an outgoing chemical signal transmission from synapses which target nearby neuron or muscle cell receptors.  Neurons are supported by glial cells.  Neurons include a:
  • Receptive element - dendrites
  • Transmitting element - axon and synaptic terminals
  • Highly variable DNA schema using transposons. 
, which use information from other brain regions to predict this new 'idea reward.'  In the process, the ongoing output of the dopamine neurons becomes an error signal that tells the rest of the brain how to adjust its decisions and learning to acquire this 'idea reward'.  Rapid changes in dopamine delivery guide learning and decision-making so as to acquire this 'idea-reward.'  As long as the idea can maintain its status as a 'reward', the rest of the brain adjusts itself to learn about this reward just as it would adjust itself to learn about a new source of food, water..." 

For example the brain can redeploy foraging in the pursuit of cognitive is the ability to orchestrate thought and action in accordance with internal goals according to Princeton's Jonathan Cohen. 
innovation.  Foraging fields for food becomes foraging a mental storehouse for new ideas. 

Montague explains that the innovation process must be tightly controlled since it could end up in an endless loop.  Hence he argues there will be self-limiting mechanisms, and constraints on what candidate can gain access and become a reward.  In drug addiction, anorexia etc. these limits are overcome by the power of reinforcement. 

Complex adaptive system
This page introduces the complex adaptive system (CAS) theory frame.  The theory is positioned relative to the natural sciences.  It catalogs the laws and strategies which underpin the operation of systems that are based on the interaction of emergent agents. 
John Holland's framework for representing complexity is outlined.  Links to other key aspects of CAS theory discussed at the site are presented. 
(CAS) theory
applies to how networks of cells cooperate to build systems like the whole human being to capture resource rich environmental niches.  The
This page looks at schematic structures and their uses.  It discusses a number of examples:
  • Schematic ideas are recombined in creativity. 
  • Similarly designers take ideas and rules about materials and components and combine them. 
  • Schematic Recipes help to standardize operations. 
  • Modular components are combined into strategies for use in business plans and business models. 

As a working example it presents part of the contents and schematic details from the Adaptive Web Framework (AWF)'s operational plan. 

Finally it includes a section presenting our formal representation of schematic goals. 
Each goal has a series of associated complex adaptive system (CAS) strategy strings. 
These goals plus strings are detailed for various chess and business examples. 
strategic nature
of
Plans are interpreted and implemented by agents.  This page discusses the properties of agents in a complex adaptive system (CAS). 
It then presents examples of agents in different CAS.  The examples include a computer program where modeling and actions are performed by software agents.  These software agents are aggregates. 
The participation of agents in flows is introduced and some implications of this are outlined. 
agents
is replicated within each cell and within the
This page discusses the mechanisms and effects of emergence underpinning any complex adaptive system (CAS).  Key research is reviewed. 
emergent
Plans emerge in complex adaptive systems (CAS) to provide the instructions that agents use to perform actions.  The component architecture and structure of the plans is reviewed. 
schematic structure
,
Agents use sensors to detect events in their environment.  This page reviews how these events become signals associated with beneficial responses in a complex adaptive system (CAS).  CAS signals emerge from the Darwinian information model.  Signals can indicate decision summaries and level of uncertainty. 
signals
and
The agents in complex adaptive systems (CAS) must model their environment to respond effectively to it.  Samuel modeling is described as an approach. 
models
of the brain.  The brain becomes an
This page reviews the catalytic impact of infrastructure on the expression of phenotypic effects by an agent.  The infrastructure reduces the cost the agent must pay to perform the selected action.  The catalysis is enhanced by positive returns. 
infrastructure amplifier
based on
This page discusses the effect of the network on the agents participating in a complex adaptive system (CAS).  Small world and scale free networks are considered. 
network effects
.  The evolutionary emergence of the brain ensures that it develops in phases: 
  1. Emotions are low level agents distributed across the brain and body which associate, via the amygdala and rich club hubs, important environmental signals with encoded high speed sensors, and distributed programs of action to model: predict, prioritize guidance signals, select and respond effectively, coherently and rapidly to the initial signal.  The majority of emotion centered brain regions interface to the midbrain through the hypothalamus.  The most accessible signs of emotions are the hard to control and universal facial expressions.  Emotions provide prioritization for conscious access given that an animal has only one body, but possibly many cells, with which to achieve its highest level goals.  Because of this emotions clash with group goals and are disparaged by the powerful.  Evolutionary psychology argues evolution shaped human emotions during the long period of hunter-gatherer existence in the African savanna.  Human emotions are universal and include: Anger, Appreciation of natural beauty, Disgust, Fear, Gratitude, Grief, Guilt, Happiness, Honor, Jealousy, Liking, Love, Rage, Romantic love, Lust for revenge, Passion, Sadness, Self-control, Shame, Sympathy, Surprise; and the sham emotions and distrust induced by reciprocal altruism.   provide an example of early, well integrated agency, where evolutionarily selected key signals are rapidly sensed and relayed throughout the
    This page reviews the implications of reproduction initially generating a single child cell.  The mechanism and resulting strategic options are discussed. 
    organism
    .  Muscle groups respond rapidly to ensure preparation and action. 
  2. Models can then develop representing feelings are models including ratings of situations which are evolutionarily associated with emotions encoded in neural circuits.  .  The ratings from these models can be associated with situations where particular emotions have been stimulated during evolutionarily recorded experience. 
  3. Cognitive planning activities can develop which leverage the feelings to select between alternative strategies. 
  4. The architecture is abstract and hence as Montague explains is able to be re-applied to new decision making areas.   
Montague provides an insightful illustration of the emergence of generalized agency in humans.  


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This page looks at schematic structures and their uses.  It discusses a number of examples:
  • Schematic ideas are recombined in creativity. 
  • Similarly designers take ideas and rules about materials and components and combine them. 
  • Schematic Recipes help to standardize operations. 
  • Modular components are combined into strategies for use in business plans and business models. 

As a working example it presents part of the contents and schematic details from the Adaptive Web Framework (AWF)'s operational plan. 

Finally it includes a section presenting our formal representation of schematic goals. 
Each goal has a series of associated complex adaptive system (CAS) strategy strings. 
These goals plus strings are detailed for various chess and business examples. 
Strategy
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This page uses an example to illustrate how:
  • A business can gain focus from targeting key customers,
  • Business planning activities performed by the whole organization can build awareness, empowerment and coherence. 
  • A program approach can ensure strategic alignment. 
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