Recently, I’ve wanted a dog. Maybe soon a tenacious little Border Terrier will be by my side. That’s when I’ll decide to become a communist — at least, in my relationship with the dog. My dog will receive pets, toys, shelter, smiles, and food according to his or her needs. I’ll receive according to his ability. You know the saying coined by Marx:
“From each according to his ability, to each according to his needs.”
Marx was speaking strictly in material terms. But in the most efficient, productive, and natural human relationships, we think, act, and produce differently. (The dog analogy isn’t perfect—most dog owners treat their pets far better than communists treat serfs.)
Every piece of material reality first existed as an idea in someone’s mind. Thoughts, intentional or not, form patterns of behavior that repeat, vary, and evolve. With effort, these behavioral patterns can be reshaped—for better or worse. Personal development, at its core, is an exercise in reorganizing learned behavioral patterns, often influenced by others.
With practice, we can change those patterns. But even our self-directed changes often originate in someone else’s idea. In most cases, we need other people to reshape our behavioral patterns. Whether small or large, this process works the same way in organizations. It dissolves Marx’s dichotomy: the division between the individual and the collective, and leaves you with each individual’s contributions.
Instead of an individual’s thoughts and actions being confined to a specific role in the chain of command, organizational learning can create equal opportunity for anyone to increase their skills and earn the opportunity to acquire more resources. That is possible because organizational learning is naturally decentralized in its structure, starting with the individual, and patterned in its effect.
The Nature of Organizational Learning
Organizational learning can arise from any member, in any part of the system. What matters more than hierarchy is whether members recognize and share in that process. Even in centralized structures, organizational learning has no true central authority. It emerges as a patterned network of behaviors—distributed, adaptive, and self-reinforcing.
When we observe behavior, we’re seeing the visible tip of deeper patterns. These patterns, built through repeated interaction, form the invisible scaffolding of the organization itself. As you’ll see, these patterns mirror the brain’s decentralized design.
Early Work on the Behavior of Organization
Ivan Pavlov, a Russian physiologist, is often remembered for the “bell and food” experiment. While studying digestion, he noticed that dogs began to salivate not only when food arrived, but at the sound of the lab assistant’s footsteps.
He then rang a bell before feeding them. After repetition, the dogs salivated at the bell alone. They had learned to associate a neutral stimulus (bell) with an unconditioned one (food).
Later, B.F. Skinner extended this idea. Rather than focusing on the stimulus before a behavior, he studied what came after – consequences. He discovered that reinforcement and punishment shape behavior over time. This became known as operant conditioning (Pavlov’s was classical conditioning).
Command-and-control cultures often rely on the same logic: control processes, reinforce compliance, and then punish deviation. While the design maintains order, it suppresses initiative and innovation. The cost is massive, and it comes in the form of lost revenue and human potential.
When you strip any organization down far enough, you find relationships. These are minds and bodies in interdependence with biological mechanisms working far beyond observable behaviors. This is where Donald Hebb enters the story.
Hebb and the Neural Basis of Learning
Donald Hebb, a Canadian neuropsychologist, was interested in how the mind and body connect. When he examined Pavlov’s and Skinner’s work, he believed something was missing: the biological mechanism behind learning itself [1].
Decades before “cognitive neuroscience” existed, Hebb proposed that behavior could only be understood by studying the nervous system. In The Organization of Behavior (1949), he wrote:
“Behavior is the end-product of the functioning of the nervous system; to understand behavior, one must understand the nervous system.” [2]
He introduced what became one of neuroscience’s most enduring principles:
When a presynaptic neuron repeatedly activates a postsynaptic neuron, the connection between them strengthens.
Though he never said “cells that fire together, wire together,” the phrase summarizes Hebb’s discovery — synaptic plasticity [3,4].
This principle shows that learning occurs locally (between specific neurons), but its effects emerge globally (as memory, skill, and intelligence).
Distributed Learning in the Brain
In the early 2000s, Ori Brafman and Rod A. Beckstrom wrote the book, The Starfish and the Spider: The Unstoppable Power of Leaderless Organizations(2006). In their terms, the book is about “…what happens when there’s no one in charge. It’s about what happens when there’s no hierarchy.”
In their introduction, they reference the slew of studies in neuroscience with the intention of finding the exact cells in the brain where memories are stored. Lo and behold, scientists also wanted to prove the brain had a top-down structure.
Here’s what they wrote:
“Scientists had long assumed that our brains, like other complex machines, had a top-down structure. Surely, in order to store and manage a lifetime of memories, our brains needed a chain of command. The hippocampus is in charge, and neurons, which store specific memories, report up to it. When we recall a memory, our hippocampus, acting like a high-speed computer, retrieves it from a specific neuron…
…In order to prove this theory, the scientists needed to show that certain neurons are activated when we attempt to retrieve a particular memory. Beginning in the 1960s, scientists wired up subjects with electrodes and sensors and showed them pictures of familiar objects. The hope was that each time a subject was presented with a picture, a specific neuron would be triggered. Subjects spent hours staring at photos. These scientists watched and waited for specific neurons to fire. And they waited. And they waited.
Instead of a neat correlation between particular memories and particular neurons, they found a mess. Each time subjects were presented with a picture, many different neurons lit up. What’s more, sometimes the same group of neurons would light up in response to more than one picture…
…An MIT scientist by the name of Jerry Lettvin proposed a solution: the notion that a given memory lives within one cell was just plain wrong. As much as scientists wanted to find hierarchy in the brain, Lettvin argued it just wasn’t there. Lettvin’s theory was that rather than being housed in particular neurons that report to the hippocampus, memory is distributed across various parts of the brain.”
Lettvin’s theory proved true. What appeared to a decentralized mess of neurons firing on the screen was not confusion. It was not chaos. It wasn’t even random even though it may appear to be. While one group of neurons would fire for one memory and then the same for another memory a network was being activated. And that network was not simply data storage. Later on, more research showed that these neural networks were learned patterns.
These patterns are formed in the process called Hebbian Learning, which turns out to be a complex molecular process that we won’t go into. However, there is a simple way to explain this.
Here are the two most important things to know:
- Hebbian Learning is a local mechanism – it operates at individual synapses based on their specific activity patterns.
- That local learning creates global patterns in the form of memories, skills, and cognitive capabilities that emerge from the networks of neurons.
So, in our brains, learning first occurs between cells as a local mechanism. It then gives rise to global patterns: memories, skills, and cognitive capabilities.
Here’s how:
Synaptic Plasticity and Adaptative Learning
In our brains, synaptic plasticity occurs through two actions, not steps, actions. The first is long-term potentiation (LTP). In LTP, synaptic connections are strengthened. The second is long-term depression (LTD), their weakening [5]. Some information and experiences can be added, others can be trimmed. It’s called adaptive learning. The brain continuously refines its representations and patterns. It strengthens those that prove useful, and weakens those that are no longer as relevant.
Distributed Memory Engrams
Modern neuroscience has largely abandoned the concept of finding that “one cell” that stores one specific memory. There isn’t one single neuron that encodes a complete, complex concept. Instead, memories are distributed across neural networks, which form an engram [17].
Research done by Josselyn and Tonegawa describe memory engrams as “functionally connected engram cell ensembles dispersed across multiple brain regions, with each ensemble supporting a component of the overall memory” [7].
This distributed architecture provides several advantages: resilience to damage (loss of individual neurons does not erase memories), efficiency (sparse coding uses only a small percentage of available neurons), and flexibility (overlapping representations allow for generalization and association).
Neurons that are both active during an experience become preferentially connected, forming the engram network. Moreover, the allocation of neurons to engrams is competitive—neurons with higher excitability at the time of encoding are more likely to be recruited [19]. This competitive allocation ensures that the most “ready” neurons participate in encoding, optimizing the use of neural resources.
The way our brains learn, adapt, and store our memories is fundamentally decentralized from start to finish.
It doesn’t take much to recognize that effective organizations mirror the brain: they spread knowledge and skills across their networks instead of relying on a few central figures. This distributed structure makes them more resilient, efficient, and adaptable.
Just as neural connections strengthen through repeated interaction, organizational capabilities grow through collaboration. Likewise, resources naturally flow toward the most capable and available members—similar to how the brain allocates effort to its strongest connections.
Synaptic Plasticity as Model for Organizational Learning, Growth, and Resilience
Organizations, like brains, are living systems that learn, adapt, and evolve through everyday interactions. Neuroscience shows that intelligence and memory do not reside in isolated neurons but in the distributed, and decentralized patterns of their connections. It is a principle that offers a powerful parallel for understanding how resilient organizations grow.
When applied to organizational life, neural plasticity reveals how collective behavior emerges from local interactions, how repeated collaboration strengthens coordination, and how pruning outdated structures preserves adaptability.
The following sections show how decentralized and hybrid organizations can apply and develop the same learning capacity and resilience found in the human brain. It also shows how organizational learning is decentralized at its core.
Patterns Emerge Through Interaction
Neural networks and decentralized organizations naturally emerge. Their capabilities and behaviors come from the interactions of components rather than being designed or directed from above.
In the brain, memories, perceptions, and decisions emerge from the coordinated activity of distributed neural ensembles. No single neuron “decides” or “remembers”; they are spontaneous, system-level phenomena. Similarly, in decentralized and hybrid organizations, strategic direction, innovation, and adaptive responses emerge from the interactions of individuals and teams. No single leader or unit determines organizational behavior; it forms as a result of the multitude of interactions within the network as a whole.
As individuals and teams within organizations interact repeatedly, their connections strengthen. Successful collaborations are reinforced, creating pathways that become easier to activate in the future. Over time, these strengthened connections form the organizational equivalent of neural engrams—stable patterns of coordination and communication that constitute organizational memory and capability. The whole process occurs without central planning or control. Like synaptic plasticity, organizational learning is local(relationships) while producing organizational-wide effects.
Adaptive Strengthening and Pruning
Synaptic Plasticity suggests that effective organizational adaptation requires both strengthening useful connections and eliminating ineffective ones. In practice, this means that decentralized, and hybrid organizations can create their own feedback loops for recognizing and reinforcing successful patterns while identifying and dissolving unsuccessful, or unprofitable ones.
This can work in every permanent or ad hoc team within an organization. Teams that collaborate effectively end up developing stronger working relationships, shared mental models, and streamline communication. These strengthened connections make future collaboration more efficient and effective, creating a positive feedback loop. Organizations can accelerate this process by creating opportunities for repeated interaction, celebrating successful collaborations, and creating some design for effective practices.
Pruning, however, often requires more deliberate actions. Unlike neural networks, which automatically weaken unused synapses, organizations tend to accumulate structures, processes, and relationships that persist even after they cease to be useful. If deliberate actions aren’t taken then organizations hinder, or even stop learning, leading their organizations into decline.
Much like the process of synaptic pruning, effective organizational learning and development needs to include a regular review and elimination of outdated processes, roles, and products or services. This might involve dissolving unproductive teams, simplifying overly complex processes, or redirecting resources from declining to emerging initiatives.
Pattern Completion and Organizational Memory
Neural networks automatically associate complete patterns from partial cues. It is a phenomenon called pattern completion [8]. This capability arises from the dense interconnections within networks of engram, where activating a subset of neurons triggers the full pattern. Pattern completion enables recognition, inference, and generalization, allowing the brain to respond appropriately to novel situations based on past experience.
Decentralized and hybrid organizations show similar capabilities when they develop strong organizational memory. Experienced organizations can respond to new challenges by recognizing similarities to past situations and choosing to respond appropriately in the same pattern. Organizational pattern completion does not require explicit recall or central coordination; it emerges from the distributed knowledge and strengthened connections throughout the network. When individuals recognize familiar patterns, they activate established relationships and practices, triggering coordinated responses without top-down direction.
So there is a bit of contrast here. Sometimes organizations can get stuck in their own way. When they become efficient at recognizing and completion patterns, learning, growth, and resilience can stall. Without the flexibility to recognize when new situations require genuinely new responses organizations can fall into decline.
From Neurons to Networks: Rethinking How Organizations Learn
Neuroscience shows us that intelligence cannot be commanded—it emerges. Learning, growth, and resilience, whether neural or organizational, are not imposed from above but cultivated through countless local interactions that strengthen, fade, and reform over time.
The same holds true for organizations that thrive. They do not evolve by perfecting control but by refining connection—through feedback, adaptation, and collaboration. What Hebb observed in the nervous system is mirrored in every healthy organization: local relationships give rise to global intelligence.
Just as neurons strengthen their connections through repeated activity, organizations grow through repeated collaboration. Just as the brain prunes unused synapses to remain efficient, organizations must deliberately unlearn, simplify, and shed outdated structures. The goal is not constant expansion, but continuous adaptation.
To design for learning and resilience, organizations must behave more like living neural networks—strengthening what works, pruning what doesn’t, and creating the conditions for new patterns to form. Leadership becomes less about issuing commands and more about cultivating conditions where learning can emerge.
In the end, the decentralized intelligence of the human brain offers a model for the decentralized intelligence of human organizations. Both depend on relationships—networks that learn, remember, and adapt together.
The future of organizational learning and development lies not in hierarchy or control, but in patterned decentralization—a living system capable of remembering, adapting, and evolving through its own interconnected parts.
References
[1] Hebb, D. O. (1949). The Organization of Behavior: A Neuropsychological Theory. Wiley.
[2] Keysers, C., & Gazzola, V. (2014). Hebbian learning and predictive mirror neurons for actions, sensations and emotions. Philosophical Transactions of the Royal Society B, 369(1644), 20130175. https://pmc.ncbi.nlm.nih.gov/articles/PMC4006178/
[3] Ramirez, A., & Arbuckle, M. R. (2016). Synaptic Plasticity: The Role of Learning and Unlearning in Addiction and Beyond. Biological Psychiatry, 80(9), e73-e75. https://pmc.ncbi.nlm.nih.gov/articles/PMC5347979/
[4] Ori Brafman and Rod A. Beckstrom wrote the book, The Starfish and the Spider: The Unstoppable Power of Leaderless Organizations(2006).
[5] Citri, A., & Malenka, R. C. (2008). Synaptic plasticity: Multiple forms, functions, and mechanisms. Neuropsychopharmacology, 33(1), 18-41. https://www.nature.com/articles/1301559
[6] Barwich, A. S. (2019). The Value of Failure in Science: The Story of Grandmother Cells in Neuroscience. Frontiers in Neuroscience, 13, 1121. https://pmc.ncbi.nlm.nih.gov/articles/PMC6822296/
[7] Josselyn, S. A., & Tonegawa, S. (2020). Memory engrams: Recalling the past and imagining the future. Science, 367(6473), eaaw4325. https://pmc.ncbi.nlm.nih.gov/articles/PMC7577560/
[8] Allport, D. A. (1985). Distributed memory, modular subsystems and dysphasia. In S. K. Newman & R. Epstein (Eds.), Current perspectives in dysphasia (pp. 207-244). Churchill Livingstone.
[21] Hamel, G., & Zanini, M. (2020). Humanocracy: Creating organizations as amazing as the people inside them. Harvard Business Review Press.