Scanning and active sampling behaviours emerge from conserved insect neural circuits

Reviewer #1 (Public review):

Summary:

Freas and Wystrach present a computational model of steering in insects. In this model, the central complex provides an error signal indicating the animal should turn left or right; this error signal biases the function of an oscillator composed of two mutually inhibiting self-exciting units. The output of these units generates a "steering signal" that is used both to set the direction and speed of the ant. Additionally, a separate module induces pauses, and an inverse relation between forward speed and turning speed is externally imposed. Statistics of the trajectories generated by the model are compared to the measured behaviors of ants.

Strengths:

While the model is very simple compared to state-of-the-art models, that simplicity makes it a potentially useful guide to researchers studying insect navigation. Some predictions that emerge from the model appear to be experimentally testable, although a more complete description of the model and its parameters, as well as an analysis of how this model's predictions differ from previous models' predictions, would be required to design these experiments.

Weaknesses:

I found it difficult to identify evidence in the paper supporting central elements of the abstract. Hopefully, these difficulties can be resolved with a clearer presentation and the addition of supporting detail, especially in the methods.

(1) The model is not clearly described

In the Materials and Methods, there is no description of the model, just "The computational model is presented in Figure 1." (This is probably a typo and may refer to Figure 2A-C), and a link to Matlab source code. It is inappropriate to ask readers or reviewers to examine source code in lieu of providing a method, but I attempted to do so anyway. To my eye, the source code does not match the model presented in 2A-C. For instance, in 2C, "Steering signal" inhibits "Freeze", but I couldn't find this in the source. "Freeze" is shown to inhibit "steering signal," but as "steering signal" is a signed quantity, it's not clear what this means. Literally, since "ang_speed_raw = L-R," it would seem to indicate the "freeze" would bias towards right turns. In the code, "freeze" appears to be implemented through the boolean variable "speed_inhibition_time." The logic controlled by this variable doesn't appear to inhibit the "steering signal" but instead (depending on control parameters) either reduces the movement speed and amplifies the turning rate, or it turns the angular speed output into a temporal integral of the control signal.

There are a number of parameters in the source code that aren't described at all in the paper, including the internal oscillator parameters.

Together, these limitations make it difficult to understand what is being simulated, what parts of the model are tied to biology, and where the model improves on or departs from previous work.

It is absolutely essential that authors fully describe the computational model, that they explain the meaning of all parameters of the model, and that they explain how the particular values of these parameters were chosen.

(2) The biological inspiration is unclear

A central claim of the paper is that the model is "biologically grounded." But some elements, for instance, using a signed quantity to represent left-right steering drive, are not biologically possible; at best, these are shorthand for biologically possible implementations, e.g., opposing groups of left-right driving neurons.

The mechanism that produces fixations and saccades - the "freeze" module - is not tied to any particular anatomy of the insect brain. Initiation of a freeze occurs at a specific time coded into the model by the authors; it is not generated by an internal model signal. Release of a freeze is by drawing a random variable; there is no neural mechanism proposed to generate this signal.

In some versions of the model, instead of directly controlling the signal, during fixations, the angular drive signal is integrated into a variable "cumul_drive." No neural substrate is proposed for this integrator. In the code, if cumul_drive passes a threshold, the angular heading of the ant changes (saccades), but only if this threshold is passed before the Poisson process ends the fixation. No neural substrate is proposed for any of this logic.

The model steps forward in time by a fixed increment - the actual duration (in seconds) of this time step is not specified. From Figure 4F, G, it appears a simulation time step is meant to be about 10ms. This would imply an oscillator frequency of about 2 Hz (Fig 2B), that the heading oscillates at a similar frequency (2G), and that a forward crawling ant stops moving every 500 ms (2I). Are these plausible? Can they be compared to an experiment?

Model parameters, including the ones that control the frequency of the oscillator, are non-dimensionalized. It is not possible to evaluate whether these parameters are biologically plausible or match experimental results.

(3) Claims that behaviors emerge from the model may be overstated

The abstract claims that steering correction and fixations/saccades emerge naturally from the same model. But it appears to me that fixations/saccades are externally imposed by the specification of specific times for a "freeze." Faster angular rotation during saccades than during course correction is imposed and does not emerge naturally from neural simulations.

(4) Citations to previous literature are difficult to follow, and modeling results are presented as though they are experimental data

I would ask the authors to be much clearer in their description and citation of previous work. It should be clear whether the cited work was experimental or computational. To the extent possible, the actual measurement should be described succinctly. Instead of grouping references together to support a sentence with multiple claims, references should be cited for each claim. Studies of computational models should not be presented as proving a biological result.

For example:

a) Lines 141-146:
"Previous studies have established many key components of insect navigation, including .... the intrinsic oscillatory dynamics in the lateral accessory lobes (LALs) that support continuous zigzagging locomotion (Clément et al., 2023; Kanzaki, 2005; Namiki and Kanzaki, 2016; Steinbeck et al., 2020)."

The first reference is to one author's previous modeling work - it hypothesizes that oscillations in the LAL support zigzagging but includes no data that would "establish" the fact. Kanzaki et al. 2005 describes numerical modeling and simulation with a physical robot. Namiki and Kanzaki, 2016 is a review article that links the LAL to zigzagging behavior. It describes the LAL as a winner-take-all bistable network but does not describe or hypothesize that the LAL has intrinsic oscillatory dynamics. Steinbeck et al. 2020 is a more comprehensive review; it reinforces that the LAL is a winner-take-all bistable network that drives left-right steering, including during zig-zagging behavior. But in my reading, I could not find a statement that the LAL has intrinsic oscillatory dynamics (the closest is Steinbeck et al. saying the activity pattern switches regularly, as does the behavior; this doesn't imply that the LAL is intrinsically oscillatory.)

b) Lines 701-703:
"In plume-tracking moths, CX output has been shown to modulate LAL flip-flop neurons driving zigzagging (Adden et al., 2022)."

This reads as though an experimental measurement was made, but in fact, this is modeling work.

c) Lines 703-706:
"In ants, strong goal signals in the CX - whether elicited by the path integrator or visual familiarity (Wehner et al., 2016; Wystrach et al., 2020b, 2015) do not only sharpen directional accuracy but also increase oscillation frequency (Clément et al., 2023)."

Here again, modeling results are presented as though they were experimental data.

Reviewer #2 (Public review):

Summary:

The paper by Freas and Wystrach is an interesting computational study, exploring the detailed mechanisms of how simple neural circuits could explain complex behavioral patterns observed in navigating ants. The authors compare detailed, high-speed video recordings of Australian desert ants (Melophorus bagoti) with predictions made by their new computational model and find convincing similarities between the model and the behavioral data, at a level of detail not previously studied. Particularly interesting are emerging properties of the model, yielding behavioral motifs it was not designed to reproduce, but which occur in natural ant behavior.

Strengths:

A strength of the study is that the model is based on previous models, without making major novel explicit assumptions. It combines existing models of the insect central complex with a model of the lateral accessory lobe and adds a stochastic inhibition of forward velocity to the interaction of central complex and lateral accessory lobes. The central complex provides corrective steering signals when the goal direction and the current heading of an insect are not aligned, while the lateral accessory lobes provide an intrinsic oscillator underlying the behavioral oscillations shown by walking ants at all times. These background oscillations are modulated by the steering signals from the central complex. Depending on which phase of the intrinsic oscillations coincides with the corrective signals, and how fast the ant is moving forward during this time, a complex set of behaviors emerges. Most prominently, scanning behaviors, which are regularly carried out by the ants, are recapitulated in great detail by the model. Additionally, other behaviors, such as full loops, emerge naturally from the model. While computational models are not to be seen as definite evidence for any biological reality, they can provide strong support for particular neural implementations. The current study is an excellent example in that it provides evidence for a serial arrangement of central complex circuits upstream of the lateral accessory lobe circuits, modulated by speed-regulating input. While the latter is hypothetical, it yields a clear hypothesis that can be validated by connectomics studies and functional work in the future.

The study shows that even complex behavioral motifs do not require dedicated neural modules, but can rather emerge from the interplay of already known circuits - highlighting the efficiency of insect brains and possibly providing the path towards embodied hardware solutions of such circuits in autonomous agents.

Weaknesses:

There are several weaknesses in the paper as it is.

Firstly, the model is not described in the methods, but only found when following the link to the authors' GitHub repository. This is clearly not sufficient and prevents readers from evaluating the model's assumptions directly. Most importantly, how natural do the emerging properties indeed emerge from the model? What parameters need to be tuned to generate a match between data and model?

Second, it is often not entirely clear what is biological data and what is a computational model. This relates to figures, text, and references. As a reader, this makes it difficult to clearly judge what is new in the current paper, how it adds to previous models, and what the predictions and assumptions are for biology.

Third, while neural data from bees and flies are taken to motivate and design the computational model, the discussion and interpretation revolve almost exclusively around ants. For the most part, this is justified, as the behavioral data used to benchmark the model are taken from ants. Nevertheless, more broadly discussing the newly defined circuit in the context of flying insects would give a better idea of the broad relevance of the neural circuits predicted by the model.