Sunday, February 27, 2011

CCK11: Why Networks?

In my last two posts, I talked about Olaf Sporns' book Networks of the Brain, and I want to continue working with that book. I'm finding it very rich and resonant.

In his Introduction (2), Sporns asks, "Why should we take advantage of modern network approaches to study the brain?" I want to ask why should we take advantage of modern network approaches to study knowledge and learning?

First, because knowledge and learning are network phenomena, and network approaches tell us things about learning and knowledge that we just can't get any other way. Sporns' comments are salient here. "Virtually all complex systems form networks of interacting components" (2). Learning and knowledge are complex systems receptive to network approaches which, says Sporns "can provide fundamental insights into the means by which simple elements organize into dynamic patterns, thus greatly adding to the insights that can be gained by considering the individual elements in isolation" (2). If we consider just a single brain alone, knowledge is the selection and orchestration of many individual neurons into meaningful patterns of interactions. No single neuron alone constitutes a chunk of knowledge; rather, knowledge emerges only when all the relevant nurons fire coherently. As I write each word of this sentence, patterns of relevant neurons are firing in my brain to orchestrate thoughts, motor movements, and memories into (we hope) a coherent expression of knowledge.

We must, of course, keep in mind that it greatly helps to understand "the individual elements in isolation," and I appreciate those physical scientists who dedicate their careers to studying how neurons fire. Knowledge absolutely depends upon the proper firing of individual neurons within our brains, and if we understand this electro-chemical process, then we understand real and crucial facts about knowledge and knowledge creation. But understanding single neurons alone isn't enough. It cannot explain thought, memory, consciousness, and knowledge. Not even complete knowledge of the fundamental physical element of knowledge—the neuron—will explain knowledge. To understand knowledge—to know knowledge—we must understand how individual neurons orchestrate themselves into meaningful patterns. As Sporns says of the brain:
The collective actions of individual nerve cells linked by a dense web of intricate connectivity guide behavior, shape thoughts, form and retrieve memories, and create consciousness. No single nerve cell can carry out any of these functions, but when
large numbers are linked together in networks and organized into a nervous system, behavior, thought, memory, and consciousness become possible. Understanding these integrative functions of the brain requires an understanding of brain networks and the complex and irreducible dynamic patterns they create.
Studying the neural patterns within a single brain seems complex enough, but if we accept the network approach, then we must accept that, as Sporns says, "networks span multiple spatial scales, from the microscale of individual cells and synapses to the macroscale of cognitive systems and embodied organisms. This architecture is also found in other complex systems—for example, in the multiscale arrangement of social networks, ranging from interpersonal relations and cohesive social groups, to local communities and urban settlements, all the way to national economies and global political organizations" (2). Thus, just understanding the pattern created by a single mental concept is not sufficient; rather, we must move up the scale to clusters of concepts housed within other clusters of larger networks, like ecosystems housed within planets, within solar systems, within galaxies, within galaxy clusters, within multiple universes.

To remove us from the black hole that I just landed us in and to put some bounds on this train of thought, imagine the patterns of neurons that are firing in your brain as you read each word in this current sentence (actually, studies show that you are likely reading clusters of words, but more of that in another post). You have a unique pattern of neurons for each word or word cluster, but each unique pattern of neurons yields precious little knowledge—precious little because each identifiable pattern is precious, or absolutely necessary for your knowing the sentence, but also little, or absolutely insufficient for knowledge. Rather, to understand what I'm writing, you must scale up to a larger pattern of patterns, from individual words and word clusters to sentences. And if you follow this logic, you see that to understand the sentence, you must scale up again to the paragraph. Then you must scale up again from the paragraph to the post. Then again from the post to the entire blog. Then you must scale again to the conversations about Connectivism, CCK11, complexity, rhizomatics, and to awareness that I'm writing all of this on a beautiful Sunday morning in Macon, GA, USA after a wonderful night's sleep and  two cups of coffee while my wife across the room watches the television show CBS Sunday Morning about the upcoming Oscars.

Sorry, but I tend to expand outwards. To center my thinking again, consider that to understand any knowledge pattern, we must understand the context, or ecosystem, within which that knowledge pattern exists, and we must accept that no scale level makes complete sense without understanding the scales above it and below it, or to say it better, no scale can be understood without understanding the scales it contains and the scales within which it is contained. To understand any layer of an onion, you must understand both how it functions as a layer by itself AND how it functions with the layers it encloses and the layers that enclose it. In the words of Edgar Morin, your thought—your knowledge—must be complex. Olaf Sporns says it better and more succinctly than I, so I'll end with him:
In multiscale systems, levels do not operate in isolation—instead, patterns at each level critically depend on processes unfolding on both lower and higher levels. The brain is a case in point. We cannot fully understand brain function unless we approach the brain on multiple scales, by identifying the networks that bind cells into coherent populations, organize cell groups into functional brain regions, integrate regions into systems, and link brain and body in a complete organism. In this hierarchy, no single level is privileged over others. The notion that brain function can be fully reduced to the operation of cells or molecules is as ill conceived as the complementary view that cognition can be understood without making reference to its biological substrates. Only through multiscale network interactions can molecules and cells give rise to behavior and cognition. Knowledge about network interactions on and across multiple levels of organization is crucial for a more complete understanding of the brain as an integrated system.
Apply this, then, to students and learning: Only through multiscale network interactions can learning give rise to behavior and cognition. Knowledge about network interactions on and across multiple levels of organization is crucial for a more complete understanding of students and learning as integrated systems. Ultimately for me then, we must apply network approaches to learning and knowledge for political and moral reasons: to stop the dreadful reduction of students to test scores. Test scores simply do not capture the complexity of learning and knowledge.


  1. Hi Keith

    I've read your last 3 posts re. Olaf Sporns / Complex networks and I can totally identify with your articulation of these ideas. I got excited when I was reading these posts as your perspective highlights a number of issues very well:
    1. "Complexity" is really a new science, a new way of looking at the world or "mapping reality" - (Stephen Hawking said that he thought the 21st Century was going to be the century of complexity...)We are starting to see the application of complexity theory in many different disciplines from neuroscience to heathcare, from economics to education.

    2. Quantitative analysis is really important and although I have only seen Olaf Sporns via Youtube ( have not read his book it does seem that the work the he is doing in the neuroscience context is being backed up with qualitative data. There are many studies and papers from the Santa Fe Institute ( have also have the benefit of quantitative analysis. However this type of analysis is not easy - I think we are probably looking at some serious non-linear computer modelling which is not particularly accessible to those of us involved in education. I could be wrong - in my own recent MSc dissertation I tried to analyse online discussion groups from the context of complex adaptive systems but I found for the quantitative side that I really lacked the necessary tools to do the job. In fact I kept coming round to the same problem - how to analyse a non-linear system using linear tools? Finding the right tools is an important study in itself.

    3.Connectivism is exciting. I too see the potential of the ideas to help people see education from a different perspective - people have been arguing whether Connectivism is a "theory" or not and I don't think the semantics of the argument really matter - what I like about it is that is helping open up new models based on interaction and emergence. My only issue - and this is something that I think you say very clearly - is that networks are complex and it is the interactions and emergence at each level which are important. In my reading so far it appears that Connectivism is too much based on the artificial neural network model of networks - a model that I don't think stresses the importance of how interaction can affect the network itself. The wikipedia article ( from this week's #CCK11 readings states: "A CAS behaves/evolves according to three key principles: order is emergent as opposed to predetermined (c.f. Neural Networks), ..." However, I think that Connectivism itself is an emergent set of ideas / theory and I hope the more recent theories regarding complexity in a network model will become integrated in time.

    Anyway thanks again for your posts!

    best wishes

  2. Graeme, thanks for the comments. I think you are right that complexity will dominate the conversations of this century, and Connectivism may contribute to that conversation.

    Sporns' book has also awakened in me an interest in the kinds of quantitative analyses that seem more common in the physical sciences than in the humanities, and I, too, lack access to the tools and technics that would facilitate that kind of research. Still, I think I'd like to give it a try, perhaps with a few choice collaborators from other departments. Any interest?

    As for Connectivism's treatment of complexity, I agree with you that the conversation appears to still be emerging, but this is an aspect that attracts me. I much prefer the emergent stage to the stable stage—more action, more movement. All in all, Connectivism is good space to talk in. I like the acoustics and the people.