BhauNeuroplasticity
THE ORGANIZATION OF BEHAVIOR…is summarized by the phrase above. Also known as the Cell Assembly theory, this model was introduced by Donald Hebb in the book titled The Organization of Behavior. This fundamental principle,“Neurons that fire together, wire together; and neurons out of sync, fail to link,”is a simple and elegant framework to contextualize Associative Learning and understand Neuroplasticity - the brain’s capacity for Re-Organization.Zooming in to the brain……we can find the basic ...
BhauBandwidth and Liquidity
H/t to Liquidity Wizard at Tokemak for outlining how Liquidity is Bandwidth in Web3, and BanklessHQ for surfacing the alpha. In Web2, network bandwidth refers to the maximum rate of information/data transfer across a given a path – the bit rate (bit/s). In Web3, information is money, so bandwidth entails transactions per second with respect to the movement of Value. Therefore, bandwidth is additionally characterized by an economic component – Liquidity. Thin liquidity is akin to dial-up, whil...
BhauPsychiatry 3.0
Highlights:Psychiatry 3.0 has both therapeutic and diagnostic applicationsMaps onto pre-existing frameworksRobust evidence baseValue proposition is risk minimizedThe notion of a Psychiatry 3.0 was mentioned in an article about “Jolting brain circuits with electricity…”The metaphor is useful:Psychiatry 1.0: Psychodynamic Psychotherapy and Psychoanalysis, subsequently continuing and/or forking into related specialized schools of thoughts. E.g., Humanistic psychology, Gestalt psychology, Behavio...
Web3 | Decentralization | Trust | Psych3
BhauNeuroplasticity
THE ORGANIZATION OF BEHAVIOR…is summarized by the phrase above. Also known as the Cell Assembly theory, this model was introduced by Donald Hebb in the book titled The Organization of Behavior. This fundamental principle,“Neurons that fire together, wire together; and neurons out of sync, fail to link,”is a simple and elegant framework to contextualize Associative Learning and understand Neuroplasticity - the brain’s capacity for Re-Organization.Zooming in to the brain……we can find the basic ...
BhauBandwidth and Liquidity
H/t to Liquidity Wizard at Tokemak for outlining how Liquidity is Bandwidth in Web3, and BanklessHQ for surfacing the alpha. In Web2, network bandwidth refers to the maximum rate of information/data transfer across a given a path – the bit rate (bit/s). In Web3, information is money, so bandwidth entails transactions per second with respect to the movement of Value. Therefore, bandwidth is additionally characterized by an economic component – Liquidity. Thin liquidity is akin to dial-up, whil...
BhauPsychiatry 3.0
Highlights:Psychiatry 3.0 has both therapeutic and diagnostic applicationsMaps onto pre-existing frameworksRobust evidence baseValue proposition is risk minimizedThe notion of a Psychiatry 3.0 was mentioned in an article about “Jolting brain circuits with electricity…”The metaphor is useful:Psychiatry 1.0: Psychodynamic Psychotherapy and Psychoanalysis, subsequently continuing and/or forking into related specialized schools of thoughts. E.g., Humanistic psychology, Gestalt psychology, Behavio...
Web3 | Decentralization | Trust | Psych3
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Note: I am not a mathematician nor a physicist. I make observations, understand concepts, and I apply what I learn. This leads me to recognize patterns.
Like how a vending machine removes the need for a vendor employee, Smart Contracts can replace intermediaries in many industries.
Smart Contracts transform the relationship between Labor and Capital. Abstract concepts like these can be understood by analogy and metaphor.
In the 1950s, John Nash described how high-speed computers of the future would scale to implement what he called “Parallel Control.” The outlined structure was comprised of a communication network, “an apparatus analogous to an automatic telephone system.”
This machine would have an elaborate configuration of component parts such as input & output units, memory units, and logical units. “The basic idea is simple. Instead of having a single unit sequencing operations of the machine in series…the idea is to decentralize control with several different control units capable of directing various simultaneous operations and interrelating them when appropriate.”
Citing numerous advantages of such a system: Speed of Computation, Expansibility, Self-Maintenance, and a certain Programmable “Intelligence,” the paper summarizes how commands would interact with addresses. Nash seemingly outlines a blueprint consistent with a description of Ethereum, and goes on to conclude that the human brain “is a highly parallel setup. It has to be.”
Fast forwarding to the 21st century in the world of computational neuroscience, the Nervous System has been found to continuously engage in a process of “message passing.” This perspective is illuminated by the Free Energy Principle and Active Inference framework.
The aim of this “game of telephone” is to minimize Prediction Errors, or Surprise, about the future. “We are not simply making sense of our sensations; we have to actively create our sensorium.” As depicted above, this game is played out within each neuronal micro-interaction. It also unfolds at the macro level with the Nervous System, as we continuously interact with the world through Action-Perception Loops.
In this framework, the systems are cybernetic and they process inputs & outputs. Thus, every neuron functions as a unit that is computing inputs into outputs – it is executing logic. Ascending Prediction Errors that are carried forward are complemented by retrograde descending Predictions.
In the simplest sense, neurons are executing logic. Binary 0s and 1s are integrated as de-polarization and hyper-polarization. These signals are first registered at the boundary between us and the outside – the Markov Blanket. These inputs enter our Nervous System through the five senses.
These electrical 0s and 1s are transduced into intracellular signals that cascade in a multitude of directions to accommodate demand. E.g., increase ATP synthesis; modulate cytoskeletal infrastructure; up or down-regulate neurotransmitter production at the terminal end of dendrites; modify gene transcription; etc.
Simultaneously, electrical energy travels down the length of axons as Action Potentials, racing through the nodes of Ranvier to arrive at the pre-synaptic end.
Here at the pre-synaptic terminal, neurotransmitters are released and the signals are predictively coded again.
This happens on the order of micro and milli seconds, as information is distributed throughout a network of 86 billion neurons. The output is a thoroughly sophisticated simulation of reality - an Active Inference of the dynamic universe.
Throughout this perpetually unfolding process, Neurotransmitters exhibit properties of Money. Neurotransmitter exchange, then, appears to simulate an accounting system between the interacting neurons.
What might be the purpose?
From the pre-synaptic neuron’s perspective, to relay information: “this number of charges entered our cell, and we used them to complete tasks x, y, and z; and here are our neurotransmitters as a token of this proof.” This internal “labor” is expensed with “capital” in the form of Neurotransmitters that travel across the synapse to exert their action by binding to corresponding receptors.
Why might this be accepted by the post-synaptic neuron? From the post-synaptic neuron’s perspective: “these neurotransmitters are accepted as the proof-of-work you performed internally to minimize your free energy (ascending prediction error), and you are the most proximal thing to us that will help us to minimize our own free energy as well. Besides if you lie, cheat, or in good faith happen to be wrong or partially correct, then we have distributed consensus with other neurons to check this since we’re all updating our databases in real time. Retrograde neurotransmission can be enacted by chemical messengers like endocannabinoids (descending prediction). We may elect to allocate a similar capital reserve of neurotransmitters at our pre-synaptic terminals (upstream ascending prediction errors carried forward).”
The exchange of Neurotransmitters begins to resemble the transference of utility from one “ledger” to another.
This makes sense when we contextualize the Active Inference framework:
“...everything that we perceive, do, or plan is in the compass of one existential imperative - self-evidencing.”
In this way, the individual Markov Blanket for each neuron expands to encompass both neurons, and all other neurons in the Nervous System. The divergences in the neurons’ Action-Perception loops are minimized. Free Energy is more minimized; more minimized because optimality is elusive.
A peer-to-peer agreement on the mutual allocation of labor and capital appears to be conducted with distributed consensus. Evidently, scale is unlocked without the interfering presence of value-extracting middlemen.
Discrepancy – Uncertainty – between neurons is contained further by stabilizing the “price” of these neurotransmitters. I.e., the amount of charge carried forward by each neurotransmitter binding to its corresponding receptor is generally specified and therefore predictable. E.g., Glutamate binding to NMDA receptor will trigger the release of calcium ions into the neuron, leading to depolarization and potentially, Action Potentials. The confidence ~ certainty ~ in this fact that the simultaneous binding of glutamate and glycine to the NMDA receptor will reliably induce a +2-mV change (for instance) in the resting membrane potential, appears to embody an important mechanism for the Nervous System to systemically minimize Risk and Prediction Errors.
If more unit charges are demanded, neurons will produce more Neurotransmitters by way of Neuroplasticity. Neurons that fire together, wire together; neurons out of sync, fail to link. Long-Term Potentiation (LTP) strengthens the synapse of neurons, inducing an increased capacity and probability of Neurotransmitter vesicle release ~ stronger electrical responses to stimuli. The opposite process is Long-Term Depression (LTD). These processes drive learning and memory.
In this game of message passing, the game theoretic solution entails modulation of neurotransmitter supply to accommodate efficiency in the transactional economy. Money is “lubrication which enables the efficient transfer of utility,” wrote Nash. Price stability and value storage is therefore emergent, as this too promotes free energy minimization; i.e., uncertainty pertaining to error and expectation units in predictive coding.
Something similar happens as nodes on the Ethereum network interact with each other. The nodes communicate by transacting the digital asset ETH. In addition to the accounting of balances, there is distributed consensus around the logic executed within the Ethereum Virtual Machine (EVM). There appears to be a transference of utility.
The accounting of balances – value exchange – without an intermediary is a feature of public blockchains that was pioneered by Bitcoin. On Ethereum, this peer-to-peer characteristic has been designated as Trustlessness.
The network is trustless because participants don’t really need to know the other participants, so long as they adhere to the rules of the game; the consensus algorithm – Proof-of-Work (PoW) on Bitcoin, and Proof-of-Stake (PoS) on Ethereum.
On Ethereum, trustless value exchange is coupled with trustless execution of logic in the EVM. The execution of this logic is an agreement between two or more parties, and the agreements are enforced by computer programs - these are known as Smart Contracts. A Smart Contract resembles a digital vending machine that does not need a vendor employee.
Ethereum Smart Contracts enforce agreement regarding the individual and collective capital allocation of ETH expensed towards the “labor performed” ~ logic executed, in the EVM. E.g., EIP-1559 automatic fee burn accompanying every transaction.
What kind of labor can be performed by Smart Contracts? The kind of labor that changes the world for the better.
Smart Contracts already uphold an alternative decentralized finance system which is free of rent-seeking intermediaries, open to all (permission-less & censorship resistant), and credibly neutral (enabled by the transparency of public blockchain transactions; e.g., etherscan.io). Quite frankly, these programs have replaced the work of stockbrokers, market makers, loan officers, and related middlemen.
On Ethereum, Information is Value, ETH is Capital, and Smart Contract function is Labor. The relationship between labor and capital is that of real-time auditing and expensing of capital, programmatically linked with the performance of labor and its verification on a transparent public ledger. Today, these programs execute logic that enables collateralized debt positions (CDPs), lending & borrowing, automated market makers (AMMs), decentralized exchanges (DEXs), liquidity pools (LPs), perpetual future swaps, on-chain options, yield-splitting, self-repaying loans, and numerous innovations growing daily. Tomorrow, Smart Contracts will unlock a world wherein we can all be rewarded with capital for the performance of everyday labors. With middlemen removed from the equation, inputs like healthy eating, exercising, and sleeping can be configured to materialize mathematical concepts like value surplus for the network. The collectively created value can be redistributed back to those who contributed, as it is today to those who provide liquidity on DEXs like Uniswap in DeFi.
In the Nervous System, Information is Value, Neurotransmitters are Capital, and Predictive Coding is Labor. Evolutionary design has iteratively sophisticated the mechanisms for minimizing inefficiencies associated with pairing labor & capital in large-scale commerce. I am therefore keenly exploring the behaviors of elastic supply digital assets in DeFi. Like Neurotransmitters that increase/decrease in supply to accommodate neuroplasticity demand through mechanisms such as LTP or LTD, these assets maintain a stable price by transforming their price volatility into supply volatility through a mechanism known as Rebase. And more recently, a new primitive known as Tranche has offered even more ways to minimize uncertainty and promote price stability. These systems provide zero-liquidations and protection from cascading market-wide unwindings.
Again it must be emphasized, middlemen/3rd parties are absent in both systems. Smart Contracts transform the relationship between labor & capital, and enable the Markov Blanket to expand and instill Trust amongst network participants (nodes and neurons). The network itself becomes the “3rd party.”
At this time, onboarding the world to web3 with Decentralized Identity (DID) can expand the Markov Blanket of Trust amongst people and communities. E.g., in healthcare, identity credentialing is conducted by 3rd parties who engage in rent-seeking behaviors and value extraction. Combining DID with elastic DeFi infrastructure that will facilitate efficient transferences of utility for the systems of tomorrow, seems like a natural next step.
Most of the reference material was sourced from Ethereum.org and the works of John Nash et al. and Karl Friston et al. Namely:
Traditional Contracts and the Violation of Justice in Medicine Value of Trustlessness Moneyness of ETH and Neurotransmitters Neuroscience of Ethereum: Active Inference Zero Liquidation Loans Mechanized Vaults and Inflation Resistance AMPL Fundamentals AMPL-SPOT Risk Risk of SPOT
Note: I am not a mathematician nor a physicist. I make observations, understand concepts, and I apply what I learn. This leads me to recognize patterns.
Like how a vending machine removes the need for a vendor employee, Smart Contracts can replace intermediaries in many industries.
Smart Contracts transform the relationship between Labor and Capital. Abstract concepts like these can be understood by analogy and metaphor.
In the 1950s, John Nash described how high-speed computers of the future would scale to implement what he called “Parallel Control.” The outlined structure was comprised of a communication network, “an apparatus analogous to an automatic telephone system.”
This machine would have an elaborate configuration of component parts such as input & output units, memory units, and logical units. “The basic idea is simple. Instead of having a single unit sequencing operations of the machine in series…the idea is to decentralize control with several different control units capable of directing various simultaneous operations and interrelating them when appropriate.”
Citing numerous advantages of such a system: Speed of Computation, Expansibility, Self-Maintenance, and a certain Programmable “Intelligence,” the paper summarizes how commands would interact with addresses. Nash seemingly outlines a blueprint consistent with a description of Ethereum, and goes on to conclude that the human brain “is a highly parallel setup. It has to be.”
Fast forwarding to the 21st century in the world of computational neuroscience, the Nervous System has been found to continuously engage in a process of “message passing.” This perspective is illuminated by the Free Energy Principle and Active Inference framework.
The aim of this “game of telephone” is to minimize Prediction Errors, or Surprise, about the future. “We are not simply making sense of our sensations; we have to actively create our sensorium.” As depicted above, this game is played out within each neuronal micro-interaction. It also unfolds at the macro level with the Nervous System, as we continuously interact with the world through Action-Perception Loops.
In this framework, the systems are cybernetic and they process inputs & outputs. Thus, every neuron functions as a unit that is computing inputs into outputs – it is executing logic. Ascending Prediction Errors that are carried forward are complemented by retrograde descending Predictions.
In the simplest sense, neurons are executing logic. Binary 0s and 1s are integrated as de-polarization and hyper-polarization. These signals are first registered at the boundary between us and the outside – the Markov Blanket. These inputs enter our Nervous System through the five senses.
These electrical 0s and 1s are transduced into intracellular signals that cascade in a multitude of directions to accommodate demand. E.g., increase ATP synthesis; modulate cytoskeletal infrastructure; up or down-regulate neurotransmitter production at the terminal end of dendrites; modify gene transcription; etc.
Simultaneously, electrical energy travels down the length of axons as Action Potentials, racing through the nodes of Ranvier to arrive at the pre-synaptic end.
Here at the pre-synaptic terminal, neurotransmitters are released and the signals are predictively coded again.
This happens on the order of micro and milli seconds, as information is distributed throughout a network of 86 billion neurons. The output is a thoroughly sophisticated simulation of reality - an Active Inference of the dynamic universe.
Throughout this perpetually unfolding process, Neurotransmitters exhibit properties of Money. Neurotransmitter exchange, then, appears to simulate an accounting system between the interacting neurons.
What might be the purpose?
From the pre-synaptic neuron’s perspective, to relay information: “this number of charges entered our cell, and we used them to complete tasks x, y, and z; and here are our neurotransmitters as a token of this proof.” This internal “labor” is expensed with “capital” in the form of Neurotransmitters that travel across the synapse to exert their action by binding to corresponding receptors.
Why might this be accepted by the post-synaptic neuron? From the post-synaptic neuron’s perspective: “these neurotransmitters are accepted as the proof-of-work you performed internally to minimize your free energy (ascending prediction error), and you are the most proximal thing to us that will help us to minimize our own free energy as well. Besides if you lie, cheat, or in good faith happen to be wrong or partially correct, then we have distributed consensus with other neurons to check this since we’re all updating our databases in real time. Retrograde neurotransmission can be enacted by chemical messengers like endocannabinoids (descending prediction). We may elect to allocate a similar capital reserve of neurotransmitters at our pre-synaptic terminals (upstream ascending prediction errors carried forward).”
The exchange of Neurotransmitters begins to resemble the transference of utility from one “ledger” to another.
This makes sense when we contextualize the Active Inference framework:
“...everything that we perceive, do, or plan is in the compass of one existential imperative - self-evidencing.”
In this way, the individual Markov Blanket for each neuron expands to encompass both neurons, and all other neurons in the Nervous System. The divergences in the neurons’ Action-Perception loops are minimized. Free Energy is more minimized; more minimized because optimality is elusive.
A peer-to-peer agreement on the mutual allocation of labor and capital appears to be conducted with distributed consensus. Evidently, scale is unlocked without the interfering presence of value-extracting middlemen.
Discrepancy – Uncertainty – between neurons is contained further by stabilizing the “price” of these neurotransmitters. I.e., the amount of charge carried forward by each neurotransmitter binding to its corresponding receptor is generally specified and therefore predictable. E.g., Glutamate binding to NMDA receptor will trigger the release of calcium ions into the neuron, leading to depolarization and potentially, Action Potentials. The confidence ~ certainty ~ in this fact that the simultaneous binding of glutamate and glycine to the NMDA receptor will reliably induce a +2-mV change (for instance) in the resting membrane potential, appears to embody an important mechanism for the Nervous System to systemically minimize Risk and Prediction Errors.
If more unit charges are demanded, neurons will produce more Neurotransmitters by way of Neuroplasticity. Neurons that fire together, wire together; neurons out of sync, fail to link. Long-Term Potentiation (LTP) strengthens the synapse of neurons, inducing an increased capacity and probability of Neurotransmitter vesicle release ~ stronger electrical responses to stimuli. The opposite process is Long-Term Depression (LTD). These processes drive learning and memory.
In this game of message passing, the game theoretic solution entails modulation of neurotransmitter supply to accommodate efficiency in the transactional economy. Money is “lubrication which enables the efficient transfer of utility,” wrote Nash. Price stability and value storage is therefore emergent, as this too promotes free energy minimization; i.e., uncertainty pertaining to error and expectation units in predictive coding.
Something similar happens as nodes on the Ethereum network interact with each other. The nodes communicate by transacting the digital asset ETH. In addition to the accounting of balances, there is distributed consensus around the logic executed within the Ethereum Virtual Machine (EVM). There appears to be a transference of utility.
The accounting of balances – value exchange – without an intermediary is a feature of public blockchains that was pioneered by Bitcoin. On Ethereum, this peer-to-peer characteristic has been designated as Trustlessness.
The network is trustless because participants don’t really need to know the other participants, so long as they adhere to the rules of the game; the consensus algorithm – Proof-of-Work (PoW) on Bitcoin, and Proof-of-Stake (PoS) on Ethereum.
On Ethereum, trustless value exchange is coupled with trustless execution of logic in the EVM. The execution of this logic is an agreement between two or more parties, and the agreements are enforced by computer programs - these are known as Smart Contracts. A Smart Contract resembles a digital vending machine that does not need a vendor employee.
Ethereum Smart Contracts enforce agreement regarding the individual and collective capital allocation of ETH expensed towards the “labor performed” ~ logic executed, in the EVM. E.g., EIP-1559 automatic fee burn accompanying every transaction.
What kind of labor can be performed by Smart Contracts? The kind of labor that changes the world for the better.
Smart Contracts already uphold an alternative decentralized finance system which is free of rent-seeking intermediaries, open to all (permission-less & censorship resistant), and credibly neutral (enabled by the transparency of public blockchain transactions; e.g., etherscan.io). Quite frankly, these programs have replaced the work of stockbrokers, market makers, loan officers, and related middlemen.
On Ethereum, Information is Value, ETH is Capital, and Smart Contract function is Labor. The relationship between labor and capital is that of real-time auditing and expensing of capital, programmatically linked with the performance of labor and its verification on a transparent public ledger. Today, these programs execute logic that enables collateralized debt positions (CDPs), lending & borrowing, automated market makers (AMMs), decentralized exchanges (DEXs), liquidity pools (LPs), perpetual future swaps, on-chain options, yield-splitting, self-repaying loans, and numerous innovations growing daily. Tomorrow, Smart Contracts will unlock a world wherein we can all be rewarded with capital for the performance of everyday labors. With middlemen removed from the equation, inputs like healthy eating, exercising, and sleeping can be configured to materialize mathematical concepts like value surplus for the network. The collectively created value can be redistributed back to those who contributed, as it is today to those who provide liquidity on DEXs like Uniswap in DeFi.
In the Nervous System, Information is Value, Neurotransmitters are Capital, and Predictive Coding is Labor. Evolutionary design has iteratively sophisticated the mechanisms for minimizing inefficiencies associated with pairing labor & capital in large-scale commerce. I am therefore keenly exploring the behaviors of elastic supply digital assets in DeFi. Like Neurotransmitters that increase/decrease in supply to accommodate neuroplasticity demand through mechanisms such as LTP or LTD, these assets maintain a stable price by transforming their price volatility into supply volatility through a mechanism known as Rebase. And more recently, a new primitive known as Tranche has offered even more ways to minimize uncertainty and promote price stability. These systems provide zero-liquidations and protection from cascading market-wide unwindings.
Again it must be emphasized, middlemen/3rd parties are absent in both systems. Smart Contracts transform the relationship between labor & capital, and enable the Markov Blanket to expand and instill Trust amongst network participants (nodes and neurons). The network itself becomes the “3rd party.”
At this time, onboarding the world to web3 with Decentralized Identity (DID) can expand the Markov Blanket of Trust amongst people and communities. E.g., in healthcare, identity credentialing is conducted by 3rd parties who engage in rent-seeking behaviors and value extraction. Combining DID with elastic DeFi infrastructure that will facilitate efficient transferences of utility for the systems of tomorrow, seems like a natural next step.
Most of the reference material was sourced from Ethereum.org and the works of John Nash et al. and Karl Friston et al. Namely:
Traditional Contracts and the Violation of Justice in Medicine Value of Trustlessness Moneyness of ETH and Neurotransmitters Neuroscience of Ethereum: Active Inference Zero Liquidation Loans Mechanized Vaults and Inflation Resistance AMPL Fundamentals AMPL-SPOT Risk Risk of SPOT
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