New behavioral monitoring and neural-net modeling techniques are revolutionizing animal neuroscience. How can we use the new tools to understand how brains implement cognitive processes? Musall, Urai, Sussillo and Churchland (pp2019) argue that these tools enable a less reductive approach to experimentation, where the tasks are more complex and natural, and brain and behavior are more comprehensively measured and modeled. (The picture above is Figure 1 of the paper.)
There have recently been amazing advances in measurement, modeling, and manipulation of complex brain and behavioral dynamics in rodents and other animals. These advances point toward the ultimate goal of total experimental control, where the environment as well as the animal’s brain and behavior are comprehensively measured and where both environment and brain activity can be arbitrarily manipulated. The review paper by Musall et al. focuses on the role that monitoring and modeling complex behaviors can play in the context of modern neuroscientific animal experimentation. In particular, the authors consider the following elements:
- Rich task environments: Rodents and other animals can be placed in virtual-reality experiments where they experience complex visual and other sensory stimuli. Researchers can richly and flexibly control the virtual environment, combining naturalistic and unnaturalistic elements to optimize the experiment for the question of interest.
- Comprehensive measurement of behavior: The animal’s complex behavior can be captured in detail (e.g. running on a track ball and being videoed to measure running velocity and turns as well as subtle task-unrelated limb movements). The combination of video and novel neural-net-model-based computer vision, enables researchers to track the trajectories of multiple limbs simultaneously with great precision. Instead of focusing on binary choices and reaction times, some researchers now use comprehensive and detailed quantitative measurements of behavioral dynamics.
- Data-driven modeling of behavioral dynamics: The richer quantitative measurements of behavioral dynamics enable the data-driven discovery of the dynamical components of behavior. These components can be continuous or categorical. An example of categorical components are behavioral motifs (categories of similar behavioral patterns). Such motifs used to be inferred subjectively by researchers observing the animals. Today they can be inferred more objectively, using probabilistic models and machine learning. These methods can learn the repertoire of motifs, and, given new data, infer the motifs and the parameters of each instantiation of a motif.
- Cognitive models of task performance: Cognitive models of task performance provide the latent variables that the animal’s brain must represent to be able to perform the task. The latent variables connect stimuli to behavioral responses and enable us to take a normative, top-down perspective: What information processing should the animal perform to succeed at the task?
- Comprehensive measurement of neural activity: Techniques for measuring neural activity, including multi-electrode recording devices (e.g. Neuropixels) and optical imaging techniques (e.g. Calcium imaging) have advanced to enable the simultaneous measurement of many thousands of neurons with cellular precision.
- Modeling of neural dynamics: Neural-network models provide task-performing models of brain-information processing. These models abstract sufficiently from neurobiology to be efficiently simulated and trained, but are neurobiologically plausible in that they could be implemented with biological components. (One might say that these models leave out biological complexity at the cellular scale so as to be able to better capture the dynamic complexity at a larger scale, which might help us understand how the brain implements control of behavior.)
The paper provides a great concise introduction to these exciting developments and describes how the new techniques can be used in concert to help us understand how brains implement cognition. The authors focus on the role of monitoring and modeling behavior. They stress the need to capture uninstructed movements, i.e. movements that are not required for task performance, but nevertheless occur and often explain large amounts of variance in neural activity. They also emphasize the importance of behavioral variation across trials, brain states, and individuals. Detailed quantitative descriptions of behavioral dynamics enable researchers to model nuisance variation and also to understand the variation of performance across trials, which can reflect variation related to the brain state (e.g. arousal, fear), cognitive strategy (different algorithms for performing the task), and the individual studied (after all, every mouse is unique –– see figure above, which is Figure 1 in the paper).
Improvements to consider in case the paper is revised
The paper is well-written and useful already. In case the authors were to prepare a revision, they could consider improving it further by addressing some of the following points.
(1) Add a figure illustrating the envisaged style of experimentation and modeling.
It might be helpful for the reader to have another figure, illustrating how the different innovations fit together. Such a figure could be based on an existing study, or it could illustrate an ideal for future experimentation, amalgamating elements from different studies.
(2) Clarify what is meant by “understanding circuits” and the role of NNs as “tools” and “model organisms”.
The paper uses the term “circuit” in the title and throughout as the explanandum. The term “circuit” evokes a particular level of description: above the single neuron and below “systems”. The term is associated with small subsets of interacting neurons (sometimes identified neurons), whose dynamics can be understood in detail.
This is somewhat at a tension with the approach of neural-network modeling, where there isn’t necessarily a one-to-one mapping between units in the model and neurons in the brain. The neural-network modeling would appear to settle for a somewhat looser relationship between the model and the brain. There is a case to be made that this is necessary to enable us to engage higher-level cognitive processes.
The authors hint at their view of this issue by referring to the neural-network models as “artificial model organisms”. This suggests a feeling that these models are more like other biological species (e.g. the mouse “model”) than like data-analytical models. However, models are never identical to the phenomena they capture and the relationship between model and empirical phenomenon (i.e. what aspects of the data the model is supposed to predict) must be separately defined anyway. So why not consider the neural-network models more simply as models of brain information processing?
(3) Explain how the insights apply across animal species.
The basic argument of the paper in favor of comprehensive monitoring and modeling of behavior appears to hold equally for C. elegans, zebrafish, flies, rodents, tree shrews, marmosets, macaques, and humans. However, the paper appears to focus on rodents. Does the rationale change across species? If so how and why? Should human researchers not consider the same comprehensive measurement of behavior for the very same reasons?
(4) Clarify the relation to similar recent arguments.
Several authors have recently argued that behavioral modeling must play a key role if we are to understand how the brain implements cognitive processes (Krakauer et al. 2017, Neuron [cited already]; Yamins & DiCarlo 2016, Nature Neuroscience; Kriegeskorte & Douglas 2018, Nature Neuroscience 2018). It would be interesting to hear how the authors see the relationship between these arguments and the one they are making.