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Paper Overview

Tech advances → detailed study of neurons.

Promises to yield new information about brain structure and behavior.
Ex: Optogenetics promising causal relationships between brain and behavior.

Argument: Detailed examination of brain parts or their selective perturbation is not sufficient to understand how the brain generates behavior.
Computer science analogy: software and hardware; “distinction between processors and the processes that they implement”

Software = “what” the brain is doing
Hardware = “how” it is doing it
Q: Whether the processes governing behavior are best inferred from examination of the processors?

C. elegans - we know the genome, cell types, and connectome. But how all this structure maps onto the worm’s behavior remains incomplete.

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Putting things into perspective for us

Technique driven neuroscience can be considered an example of “substitution bias”; Kahneman 2011 p.12: “ … when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution”

Our Focus	Sidelined Questions
Massive and intricate datasets + means to analyze them	What counts as an explanation for this context
Development of technology	Organismal-level thinking for functional analysis of behavior
Models	What is a mechanism for the behavior we are trying to understand
Analysis approaches for datasets	What does it mean to understand the brain

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“

We fully acknowledge the crucial role that technology plays in advancing biological knowledge and the value of interventionist approaches, but this tool-driven trend is not sufficient for understanding the brain-behavior relationship.

”

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Why we need behavior - David Marr’s argument

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Understanding something is not the same as just describing it or knowing how to intervene to change it..

”

To most, it is not news that description is not understanding, but…
Too often in neuroscience causal efficacy is taken as equal to understanding

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‘‘.trying to understand perception by understanding neurons is like trying to understand a bird’s flight by studying only feathers. It just cannot be done’’

Marr’s main intuition was that it is much more difficult to infer from the neu- ral hardware (or implementation; level 3) what algorithm (level 2) the nervous system is employing as compared to getting to it via an analysis of the computational problem (level 1) it is trying to solve.

Marr’s main objection to trying to understand the brain by recording from neurons was that this only leads to descriptions rather than explanations. A description of neu- ral activity and connections is not synonymous with knowing what they are doing to cause behavior. Even strong believers in the work done at the level of neurons and molecules (the im- plementation level) concede Marr’s point (Bickle, 2015). An analogy that helps get this point across is understanding of the game of chess. Understanding the game does not depend on knowing anything about the material out of which the board or chess pieces are made. Indeed, Marr suggested that the details of the nervous system may not even matter

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Why we need behavior - mirror neurons

Mirror neurons: discovered in premotor cortex of monkeys - fire whether the monkey itself performs a motor goal or observes another individual doing so.

Huge number of variants of these experiments in both humans and primates
General approach: show a common neuronal firing (OR fMRI/EEG/MEG pattern) when a goal is achieved either in the first person or observed in the third person
Interpretation: as neurons can be decoded for intention in the first person, and these same neurons decoded for the same intention in the third person, then activation of the mirror neurons can be interpreted as meaning that the primate has understood the intention of the primate it is watching.
Problem: no independent behavioral experiment is done to show evidence that any kind of understanding is actually occuring, understand that could then be correlated with mirror neurons.

What is needed: a better a priori testable framework for behavioral-level understanding that can lead to more thoughtfully designed experiments.

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Why we need behavior - what about circuits?

Unclear: How different is it to move from “neuron” to “neurons”.

Going back to Fig 1: properties of neural tissues may be more diverse than the subset actually exploited for natural behaviors.

Emergent behavior: neurons in their aggregate organizations cause effects that are not apparent in any single neuron.

Then …. Behavior itself is emergent from aggregated neural circuits and therefore should also be studied in its own right.

Example: flocking in birds (steer to average heading of neighbors)

One has to observe the behavior and then begin to test rules that will lead to reproduction of the behavior.

Jonas & Kording 2017 - Could a neuroscientist understand a microprocessor?

is causal-mechanistic explanation sufficient to explain a behavioral phenomenon? Clearly knowing the sufficient and necessary conditions to evoke a behavior as found through an optogenetic or similar manipulation can fall far short of knowing the rules needed to instruct a robot or computer to perform the activity in question. Thus, we would argue that if the question is ‘‘how does the brain lead to behavior?’’ we are first asking why is the brain performing this behavior and then asking how is it doing it. So using the flying analogy (Figure 2), once we agree that bird flight is an adaptive behavior, we then determine that it flies by flapping it wings and not by wiggling its feet. Once we have worked this out, we can start studying the feathers that make up the wing. Seen this way, understanding that the flapping of wings is critical to flight aids the subsequent study of feathers. It is unlikely that, from the outset, studying an ostrich feather in isolation would lead to the conclusion that there is such a phe- nomenon as flight or even that feather-like structures would be useful for flight.

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There is no ‘‘interesting’’ variable inherent to the data that can be objectively plotted on a graph—abstract reasoning and normative claims cannot be substituted by, or obtained from, data. Tacit awareness that causal manipulative work, which tests necessity and sufficiency claims, is not the same as understanding is apparent with common sentences like, ‘‘The circuit X is involved in behavior Y.’’ This is, however, just a restatement of the correlation or causal relation and does no extra explanatory work. The italicized word is known as a filler term, which indicates the lack of an explicit conceptual framework for the mapping between circuit and behavior (Craver, 2008) and so just fills in for it (Figure 3). Importantly, however, the use of filler terms signals a tacit awareness of the lack of, and a desire for, a different kind of understanding, which we argue can be obtained by doing empirical and theo- retical behavioral work. This work would complement causal explanations at the neural and circuit level.

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Behaviorally driven neuroscience → Complete insights

Take-aways:

Experiments at the level of neural substrate are best designed with hypotheses based on pre-existing behavioral work that has discovered or proposed candidate algorithmic or computational processes.
The explanations of results at the neural level are entirely dependent on the higher-level vocabulary & concepts derived from behavioral work. Lower levels of explanation do not “explain away” higher levels.

Examples given:

Bradykinesia
Sound localization
Eletrolocation
Motor learning

Bradykinesia - lack of movement vigor, related to dopamine depletion in the substantia nigra, and partially and transiently reversed by increasing dopamine with medication. one of the main symptoms of parkinson’s disease.

Do the facts help us understannd why dopamine depletion leads to bradykinesia? This paper argues No.

Loss of a neurotransmitter may explain the necessary and sufficient starting conditions for bradykinesia, which in this case is dopaminergic cell death, is a major area of ongoing work at the cellular level.

Nevertheless, work restricted to this level cannot explain why dopamine depletion or data claiming neural correlates causes a loss of vigor. IN CONTRAST - psychophysical studies w/ PD patients suggest that low vigor is caused by a loss of motivation for fast movements because of a skewed perception of motivation and cost. Then these experiments led to mice experiments that found cells in dorsal striatum whose activity correlates with movement vigor, and that suppression of these cells optogenetically reduces vigor.

The complete interpretation of these experiments, requires the explanatory framework provided by the initial human behavioral work, not the other way around.

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Pluralistic Explanation of Neuroscience

Neuroscience has been focused of late on neural circuits. This is largely due to the recent development and incorporation of techniques that allow both causal manipulation and the rapid acquisition of large amounts of data.

There seems to be an implicit assumption that implementation-level description will not only allow causal claims but also somehow lead to algorithmic and computational understanding. This causual-manipulation view by itself will not lead to understanding. We need a more pluralistic conception.

Here we have argued that when scientists ask ‘‘how does the brain generate behavior,’’ they are in fact asking a question best approached through behavioral work, specifically task analysis, aided by theory, that allows behavior to be decomposed into separable modules and processing operations

The neural basis of behavior cannot be properly characterized without first allowing for independent detailed study of the behavior itself.