Crowdsourcing your ideas for the #BrainIdeasCountdown:
Before we all turn into Winter Holiday pumpkins: What are some most interesting ideas in brain research that I haven't highlighted yet? I've sketched out my own ideas for these last 2/10 days (promise!). But brain research is working on so much & I'm curious to hear your thoughts about what exactly that is. Here's my (random) list:
Idea10: Our moods depend on what's happening in our gut.
Idea 9: Across individuals, the same brain functions are implemented by biological details that vary a lot.
Idea 8: Consciousness level can be measured by brain complexity.
Idea 7: Stimulation of the brain at multiple nodes may dance it from dysfunction back to normal function.
Idea 6: Gene therapy may circumvent the need to understand how mutated proteins lead to brain dysfunction.
Idea 5: Neurons in the brain influence one another through the electric fields that they generate, ephaptic coupling.
Idea 4: Our health and well-being is determined not just by our genes, but also the genes of those around us, "social genetic effects."
Idea 3: We rely on our memories of the past to predict the future.
Ideas 1&2: Coming soon!
For details, click here: #BrainIdeasCountdown
So: What haven't I highlighted yet?
Thinking about brain research this way is a bit of a twist on how we normally think about things. I would say that we tend to think more in terms of findings, eg "That paper found ..." whereas this is something more like, "That stack of papers is working on the idea that ..."
It's interesting to think about one's own work in that light: What ideas am I working on and who else is working on the same idea (perhaps with a different approach)? Similarly, what sorts of ideas is the field working on? And are these ideas new or old?
Here's a slightly more provocative way to pose the question: In The Idea of the Brain, Matthew Cobb argues, "In reality, no major conceptual innovation has been made in our overall understanding of how the brain works for over half a century ... we still think about brains in the way our scientific grandparents did."
Setting aside semantic debates about what constitutes a "major conceptual innovation", brain researchers are clearly working on a large number of ideas that their grandparents had not thought of. But what are those, exactly?
@NicoleCRust Matthew Cobb is here too @matthewcobb – has there been any recent idea on what the brain is or how it operates that wasn't a rehash of an idea from before 1970?
#neuroscience #brain
@albertcardona @matthewcobb
Thanks. How about:
The brain is a complex recurrent dynamical system, Hopfield 1982.
https://www.pnas.org/doi/10.1073/pnas.79.8
Or this one? Across different individuals, the same brain functions are implemented by biological details that vary a lot. This is true even for simple circuits like the ones that control the stomach of a crab, where the numbers of ion channels can vary 2-6x across different crabs but the circuit always does the same thing.
@albertcardona @matthewcobb
Not sure if this one is actually true, but it seems like a new idea: Ephaptic coupling.
https://www.researchgate.net/profile/Costas-Anastassiou/publication/266264218_Ephaptic_coupling_to_endogenous_electric_field_activity_Why_bother/links/5a0351260f7e9beb176f2238/Ephaptic-coupling-to-endogenous-electric-field-activity-Why-bother.pdf
@albertcardona @matthewcobb
I'd love to see anyone add to this list! But the main point is also really important for everyone to grasp, I think: there are many fewer things in this list than you might imagine.
@albertcardona @matthewcobb
A shocking correlate of this is that the vast majority of brain researchers never come up with a new idea about how the brain works. Which I don't throw out there not to belittle (I'm one too) but to inspire the next generation: WE.NEED.NEW.IDEAS.ABOUT.HOW.THE.BRAIN.WORKS!
@NicoleCRust @albertcardona @matthewcobb
Do we *really*, though!
I'm very partial to Sydney Brenner's quote about progress in science: "Progress in science is made through new technologies, new discoveries, and new ideas, probably in that order."
To me, the biggest deficit--not that theory is unimportant--is in being able to experimentally address the problem at the scale that we know is important. It's kind of like we're trying to understand weather dynamics by giving everyone in London a thermometer (and not bothering to tell them to keep it outside), putting a wind-speed meter in Glasgow and in Miami, and observing the clouds from a lunar observatory.
We know for certain that there is lots of feedback, that connectivity is elaborate and important, and that gain control can be large--in short, we have every reason to believe that details ought to matter, and mostly we can't see them, which makes it very hard to discern how well we actually understand "how the brain works". If current (detailed) theories were fully adequate, I'm not sure we'd know!
So to me, we need ideas not about how the brain works but how to couple what we can actually observe with somewhat reducing the space of possible models. (Plus I second Albert's recommendation to invest effort in simpler systems.)
@NicoleCRust @albertcardona @matthewcobb
For example, if we're interested in cortex, we have to grapple with the fact that cortical connections are broadly specified during development, extensively modulated by activity, and may contain some intermediate representation and state of some computation that isn't easily connected either to outside stimuli or to goal-directed behavior. So we have a mixed problem of development, synaptic plasticity over multiple timescales, highly parallel input and output...and have a hard enough time even measuring the broad outlines of the basic rules like synaptic pruning, let alone how the system was built.
So, what is the operation of cortex, or a cortical column? Well, maybe the cortical architecture supports, as is, with known mechanisms of synaptic plasticity etc., a variety of general-purpose computations and the only thing we're really missing is the correct parameter ranges and initial network connectivity to support this function (but we'll never know without better data than we can get right now).
Alternatively, maybe the largely reductionistic approach taken by neuroscience has led us to missing systemic effects (LFPs, neuromodulators, etc.) that play a critical role in how the circuit functions, and if we had better measurements of such things (both what they are and the magnitude of their effects), we'd figure out the computation.
Or maybe we're not even conceptualizing the "computation" the right way at all. Maybe the analogy we draw to input-output devices like transistors or functions are poor ways to model how the brain works, and there's an emergent property of the system that will give us better explanatory power. But if we came up with a much better model, unless we magically manage to nail all the free parameters perfectly, would we actually agree with the experimental data we can collect any better than in the first case where we guess that each neuron is integrating dendritic inputs and producing spikes and that's basically all we need to know?
