In a paper published earlier this year in Scientific Reports and further discussed in a later ACS Chemical Neuroscience article, a group of researchers argues that learning might not function like we previously thought. The researchers (Sardi et al. 2018a, 2018b) explain that the dominant conceptualization in cognitive neuroscience of how learning works—synaptic learning, or “Hebbian learning” (Hebb 1949)—is wrong. Instead, using a series of computational models and experiments with synaptic blockers and neuronal cultures (see Sardi et al. 2018a:4-7), the authors find evidence for a different type of learning—what they refer to as “dendritic learning.” Just as “Copernicus was the first to articulate loudly that the earth revolves around the sun and not vice versa, even though all the accumulated astronomical evidence at that time fit the old postulation,” the researchers proclaim, as are they the first to “[swim] against conventional wisdom” of Hebbian learning theory (2018b:1231).
Of what consequence is this newfound process of dendritic learning for cultural sociology? Should we care at all? I’ll try to briefly describe some of the potential consequences of dendritic learning for cultural sociology; but, spoiler alert, I am not sure one way or the other if these consequences amount to being consequential for how we do sociology. But perhaps taking a peek at what dendritic learning is and how it is different from conventional understandings of how learning works is a nice place to start.
Figure 1. Are We Witnessing a “Revolution of the Cognitive Spheres”?
Note: Image from Copernicus’ On the Revolutions of the Heavenly Spheres (Palca 2011).
LINKS VS. NODES
Going on 70 years, the prevailing explanation for how learning works has been synaptic learning. Building from Hebb’s (1949) The Organization of Behavior, the idea behind synaptic learning is that if there is an activity that stimulates a neuron which in turn stimulates another neuron, and if that activity is repeated over time, then the first neuron becomes a more efficient stimulator of the second neuron and the two become more strongly connected in the brain.
Neuron-neuron stimulation occurs through synapses, the chemical (usually) or electrical (less frequently) structural gaps between neurons transmitting information across them. Synaptic learning, then, is a type of “activity-dependent synaptic plasticity” (Choe 2015:1305). Repeated practices or exposures to a certain stimulus modifies the synaptic strength between the two neurons: when the practice/exposure is repeated, the two neurons become more tightly associated in the brain, and when the practice/exposure is not repeated, the association weakens. This process occurs relatively slowly.
Synaptic learning is the inspiration behind the old adage that “neurons that fire together wire together.” Until very recently, this was the way we assumed new neural coalitions formed in biological neural networks. Consider an example from Luke Muehlhauser over on the Less Wrong blog (Muehlhauser 2011). Think back to Pavlov’s experiment on classical conditioning (Pavlov 1910): a dog is given food when the researcher rings a bell, and the timing between the bell ringing and the presentation of food is manipulated. At first, there is no association between the neurons stimulated by bell ringing and the neurons that trigger salivation; they are, ostensibly, mutually exclusive actions. However, if the researcher rings the bell and the food is presented to the dog at the same time (or in close enough time intervals), the neurons that fire when food is present and the neurons that fire with bell ringing are activated together. Over repeated trials, the synapses between “bell ringing” and “salivation” neurons become stronger and, eventually, simply ringing the bell induces salivation without the presentation of food (see Figure 2).
Figure 2. Synaptic Learning with Pavlov’s Experiment
Note: Reprinted from Less Wrong blog (Muehlhauser 2011).
Sardi and colleagues refer to synaptic learning as “learning by links” (Sardi et al. 2018a:1), since learning occurs through the synapses that link the neurons together. Their research, however, suggests a different type of learning—dendritic learning, also known as “learning by nodes” (Sardi et al. 2018a:2). In short, with this mode of learning, the workhorse of the neuron for learning purposes is not the synapses, but instead the dendrites. In a neuron cell, dendrites are the long, treelike extensions that connect the cell body (the soma, which contains the cell nucleus) to the synapses that themselves “connect” the neuron to other neurons.
Take a look at Figure 3, a neuron cell’s anatomy. The dendrites are responsible for taking in information from other neurons and passing it along into the soma, while the axon is responsible for passing the information on to other neurons via the axon terminals—which are themselves connected to the next neuron’s dendrites through synapses, thus propagating information transmission across the neural network. Without dendrites, information cannot be transmitted into the body of the neuron: e.g., damaged or abnormal dendrites are linked to brain under-connectivity issues associated with autism (Martínez-Cerdeño, Maezawa, and Jin 2016). Trying to construct new neural networks without dendrites is like trying to have group deliberation with all talk and no listening.
So, how does dendritic learning differ functionally from synaptic learning? While synaptic learning is based on the idea of synaptic plasticity, dendritic learning revolves around the notion of (you guessed it) a sort of dendritic plasticity: given increasing or decreasing levels of exposure to a neuron-activating stimulus, the extent of the neuron’s “dendritic excitability” can grow or diminish while the strength of the synapses remain relatively constant (Neuroskeptic 2018).
Consider Figure 4. Across both panels, the teardrop object at the bottom represents the neuron cell body, which is where the firing happens if the input signals from the dendrites are strong enough for an outgoing signal to be pushed from the cell body down through the axon and into the dendrites of the next neuron. The long treelike branches are the dendrites, and the tips are the synapses that connect the neuron’s dendrites to the axon terminals of other (not shown) neurons. The left panel illustrates conventional synaptic learning, where the synapses themselves are weighted (indicated by the red valves at the tips of the branches) upward or downward depending on the extent of stimulus exposure. The right panel shows dendritic learning: it is the extent to which a neuron’s dendrites are in a high state of stimulation, and not the strength of the synapses linking the neuron to other neurons, that determines the strength of the input signal and therefore whether or not the neuron fires. In dendritic learning, then, there are far fewer “learning parameters,” since the dendrites are responsible for the learning and not the synapses (see the right panel of Figure 4) (ScienceDaily 2018).
Figure 4. Synaptic Learning (left) vs. Dendritic Learning (right)
Note: Reprinted from ScienceDaily (2018).
IMPLICATIONS (?) FOR CULTURAL SOCIOLOGY
The “Neuroskeptic” over at Discover Magazine reviewed the evidence from the Sardi et al. papers and suggests that “[a]t best they have shown that dendritic learning also happens [in addition to synaptic learning],” and that “[they] don’t think Copernicus has returned to earth just yet” (Neuroskeptic 2018). I agree with Neuroskeptic in terms of what this means for neuroscience, largely because they are the neuroscientist and I am not. That said, there does seem to be the potential for some implications for how we do cultural sociology. But the potential may be greater for some subfields than for others.
I’m Not Sure What this Adds for How Sociologists Study Learning
The existence of dendritic learning has at least two major implications for cognitive neuroscience. First, learning may happen at much faster timescales than previously thought. Second, weak synapses matter a lot. In terms of timescale, it seems that the brain isn’t that bad at quick adaptation—at least relative to traditional Hebbian learning. As Sardi and colleagues note, “[t]his dynamic brain activity leads to the capability that when we think about an issue several times we may find different solutions” (Shrourou 2018). For the importance of weak synapses, the researchers point out that dendritic strengths are “self-oscillating” (2018b:1231), where weak synapses effectively “temper” the dendritic weights and prevent them from taking on extreme values. In other words, “dendritic learning enables stabilization around intermediate [dendritic strength] values” (Sardi et al. 2018a:4). These implications are pretty important for neuroscientists and medical researchers studying various diseases of the brain (Sardi et al. 2018b:1231-32).
What does all this mean for cultural sociologists? It might be too early to tell. Dendritic learning might be faster than synaptic learning, but the time scales in the experiments are in much smaller intervals (minutes) than the learning processes of interest to sociologists. The researchers note that future studies should “investigate . . . [dendritic learning] efficiency and available learning time scales in more realistic scenarios” (2018b:1231), so it’s an empirical question whether or not the learning speed differentials between synaptic and dendritic learning are a wash with longer timescales. So, in terms of theoretical leverage, dendritic learning may or may not offer much over and above how we already talk about learning in culture and cognition studies (see Lizardo et al. 2016:293-95). At the end of the day, for cultural sociologists it may all look like GOFILT—Good Old Fashioned Implicit Learning Theory—in which case the difference between synaptic and dendritic learning can be taken as ontologically true but analytically inconsequential. Only time (pun) will tell.
The Payoff May Come Sooner for Computational Social Science
In addition to understanding the learning processes behind biological neural networks and brain disorders, Sardi and colleagues also note that this “paradigm shift” matters for developing machine learning algorithms built to mimic human learning (2018b:1231). In natural language processing, for instance, if synaptic learning isn’t the baseline model of human learning (itself an empirical question), then perhaps analytical strategies that build associations between terms or documents based on term frequencies and co-occurrences aren’t based on the best cognitive model for machine learning.
But at face value I’m skeptical of this last proposition—I like word count methods for analyzing meaning, others do too (Nelson 2014; Underwood 2013), and I’ve read enough papers that make defensible claims using them to sell me on their continued use. That said, we have not seen dendritic learning rules implemented into machine learning algorithms yet (but see Sardi et al. 2018a:2-3 for an example of dendritic learning rules in a series of perceptron models), and it might prove particularly consequential in deep learning tasks and artificial neural network models. These sort of machine learning algorithms have not gained much traction in sociology, though, so, for now, it seems that the utility of distinguishing between synaptic and dendritic learning for culture and cognition studies is truly a waiting game.
I can continue all of my work without making these distinctions, and I suspect that most of the people reading this post are in the same position.
Choe, Yoonsuck. 2015. “Hebbian Learning.” Pp. 1305-09 in Encyclopedia of Computational Neuroscience, edited by D. Jaeger and R. Jung. New York: Springer.
Hebb, Donald O. 1949. The Organization of Behavior: An Neuropsychological Theory. New York: Wiley.
Khan Academy. 2018. “Overview of Neuron Structure and Function.” Khan Academy. Retrieved October 16, 2018 (https://www.khanacademy.org/science/biology/human-biology/neuron-nervous-system/a/overview-of-neuron-structure-and-function).
Lizardo, Omar, Robert Mowry, Brandon Sepulvado, Dustin S. Stoltz, Marshall A. Taylor, Justin Van Ness, and Michael Wood. 2016. “What Are Dual Process Models? Implications for Cultural Analysis in Sociology.” Sociological Theory 34(4):287-310.
Martínez-Cerdeño, Verónica, Izumi Maezawa, and Lee-Way Jin. 2016. “Dendrites in Autism Spectrum Disorders.” Pp. 525-43 in Dendrites: Development and Disease, edited by K. Emoto, R. Wong, E. Huang, and C. Hoogenraad. Tokyo: Springer.
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Sardi, Shira, Roni Vardi, Amir Goldental, Anton Sheinin, Herut Uzan, and Ido Kanter. 2018a. “Adaptive Nodes Enrich Nonlinear Cooperative Learning Beyond Traditional Adaptation By Links.” Scientific Reports 8(1):5100.
Sardi, Shira, Roni Vardi, Amir Goldental, Yael Tugendhaft, Herut Uzan, and Ido Kanter. 2018b. “Dendritic Learning as a Paradigm Shift in Brain Learning.” ACS Chemical Neuroscience 9:1230-32.
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