Tag: serotonin

Essay 25: head direction gradients

On December 14, 2023

Essay 24, which investigated temporal gradient navigation, raised the question of head direction and navigation. The essay 24 model followed a zebrafish phototaxis experiment by [Chen and Engert 2014] which created a virtual light spot surrounded by darkness. The phototaxis behavior used Hb.m (medial habenula) and B.ip (interpeduncular nucleus) path using 5HT (serotonin) from V.mr (median raphe) as an average integrator [Cheng et al 2016] to generate the gradient without using head direction. Since B.ip receives head direction input [Petrucco et al 2023], essay 25 explores using head direction with the phototaxis gradient.

In the fruit fly drosophila, head direction and goal direction combine in the fan-shaped body to produce motor commands toward the goal [Matheson et al 2022]. Since the vertebrate B.ip connectivity with head direction resembles the fan-shaped body, this essay will use it as a model.

B.ip connectivity

Head direction from B.dtg (dorsal tegmental nucleus of Gudden) and the photo-gradient input from Hb.m would combine in tabular rows and columns in B.ip, if it resembles the fan-shaped body.

Head direction encoding

Head direction is necessarily encoded by neurons. Each neuron in the head direction population has a specific direction, and fires when the animal is heading toward the neuron’s preferred direction.

In general, the heading is encoded is an ensemble of neurons, where several neurons around the actual direction fire at different rates (or possibly delayed phases). In the diagram above, the central direction (blue) has a higher activity while neighboring neurons have smaller values [Petrucco et al 2023].

Drosophila uses a coding for its head direction, where the amplitude of the actual direction neuron is close to one and the neurons at orthogonal directions are zero [Westeinde et al 2022]. This sinusoidal encoding enables neuron-friendly transformations and combinations [Touretzky et al 1993] with advantages over neural rate-encoding or phase encoding, particularly in response speed.

Fan-shaped body: allocentric to egocentric

Fruit fly navigation uses its fly-shaped body to combine an allocentric goal direction with the head direction to create motor commands to turn left or right. Egocentric is self-focused and allocentric is other-focused. Allocentric coordinates are animal-independent like North or toward a distant landmark, which egocentric coordinates are relative to the animal, like forward, right or left.

The fan-shaped body has a tabular shape where each column is a head direction and each row is a goal input [Hulse et al 2021]. The fan-shaped body combines the goal vector and the head direction to create motor commands [Westeinde et al 2022].

*The fan-shaped body combines head direction with goal vectors to produce motor commands.*

By shifting the head direction and combining the sinusoidal encodings of the goal vector, the motor output is a turn toward left or right. In drosophila, there’s a third motor command for a U-turn when the goal is behind the fly. Each motor command is carried by a specific neuron: PFL2.L (left), PFL2.R (right), and PFL3 (U-turn).

In drosophila, there are 18 distinct head direction columns and up to 9 goal rows. The fan-shaped body is also used for motivation calculations like sleep, despite sleep not fitting into the strict tabular model shown above. To create the strict organization, the fan-shaped body has 400 distinct neuron types [Hulse et al 2021].

Constructing goal vectors

In the phototaxis situation as in essay 24 or [Chen and Engert 2014] the goal vector is constructed from the gradient as the animal enters darkness from light and the head direction at that moment.

*Captured goal vector (red) when the animal crosses into darkness.*

As the diagram above suggests, the stored vector isn’t the true direction from light to dark, but only the sample along the animal’s path. The gradient value is then stored in the goal direction cells.

Storing the goal vector requires gating based on head direction. In zebrafish, serotonin accumulators can be gated by actions and used as a short term memory (5s – 20s) [Kawashima et al 2016]. For the essay, head dir gates serotonin accumulation as a replacement for the action gating.

*Storing gradient into the goal vector based on the current goal. The red direction (south-east) gates its associated serotonin accumulator.*

Since V.mr (median raphe) neurons produce consistent tonic oscillations, they are ideal for reading the accumulated value. No additional circuitry for the read is necessary.

Essay simulation

Because the essay model is a functional level, not a circuit level, it can use a directional vector encoding: a pair of floating-point numbers for direction and gradient for strength.

The simulation also calculated two averages: a short-term average for the goal vector gradient and a long-term average for phototaxis gradient motivation. The goal vector average needs to be shorter to avoid bleed-over from a previous direction.

*Screenshot of animal crossing into darkness.*

The above screenshot shows the animal’s state when it crosses into darkness. The long-timescale motivational gradient (“gr/grad”) is negative, driving the animal to avoid darkness. The short directional gradient (“sa”) is near zero, avoiding update of the stored goal vector. (Note: gradients are 0.5-centered for graphing consistency.)

The homunculus diamond in the upper right shows the current head direction (black semicircle pointing north-east) and the avoidance goal vector (orange semi-circle pointing east). Since the animal is heading toward the avoidance direction, it has a U-turn motor command (orange triangle at top). In addition, since the goal vector and head direction are near a right angle, right turns are inhibited (red at lower right). Because locomotion remains exploratory and stochastic, inhibits reduce turn probability but don’t force turns.

Discussion

This essay’s model is more speculative even compared to other essays, because I haven’t found any papers reporting in B.ip head direction behavior other than the base existence of head direction afferents [Petrucco et al 2023]. In particular, the drosophila fan-shaped body is not homologous to B.ip because the pre-vertebrate animal amphioxus lacks either structure. Nevertheless, it’s interesting that a goal gradient vector circuit is at least possible and relatively simple.

Specifically, the goal vector provides an evolutionary step toward hippocampal (E.hc) object vector cells and grid cells, because those are relatively small enhancements over the goal vector. Without a Bi.ip goal vector system as an intermediary step, hippocampal navigation is too big of an evolutionary step with too many concurrent requirements to be likely.

Note that the hippocampal system is strongly connected with the Hb, B.ip, V.mr, B.dtg system from this essay. E.hc (hippocampus), P.ms (medial septum), Hb (habenula), B.ip (interpeuncular nucleus), V.mr (median raphe), B.dtg (head direction) form a strong connected system together with H.sum (supramammilary/ retromammilary nucleus).

References

Chen X, Engert F. Navigational strategies underlying phototaxis in larval zebrafish. Front Syst Neurosci. 2014 Mar 25;8:39.

Cheng RK, Krishnan S, Jesuthasan S. Activation and inhibition of tph2 serotonergic neurons operate in tandem to influence larval zebrafish preference for light over darkness. Sci Rep. 2016 Feb 12;6:20788.

Hulse, B. K., Haberkern, H., Franconville, R., Turner-Evans, D., Takemura, S. Y., Wolff, T., … & Jayaraman, V. (2021). A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection. Elife, 10.

Kawashima T, Zwart MF, Yang CT, Mensh BD, Ahrens MB. The Serotonergic System Tracks the Outcomes of Actions to Mediate Short-Term Motor Learning. Cell. 2016 Nov 3;167(4):933-946.e20.

Matheson, A. M., Lanz, A. J., Medina, A. M., Licata, A. M., Currier, T. A., Syed, M. H., & Nagel, K. I. (2022). A neural circuit for wind-guided olfactory navigation. Nature Communications, 13(1), 4613.

Petrucco L, Lavian H, Wu YK, Svara F, Štih V, Portugues R. Neural dynamics and architecture of the heading direction circuit in zebrafish. Nat Neurosci. 2023 May;26(5):765-773.

Touretzky, D. S., Redish, A. D., & Wan, H. S. (1993). Neural representation of space using sinusoidal arrays. Neural Computation, 5(6), 869-884.

Westeinde Elena A., Emily Kellogg, Paul M. Dawson, Jenny Lu, Lydia Hamburg, Benjamin Midler, Shaul Druckmann, Rachel I. Wilson (2022). Transforming a head direction signal into a goal-oriented steering command. bioRxiv 2022.11.10.516039;

17: Issues on vertebrate seek

By ferg

On September 25, 2023

In Uncategorized

While implementing the basic model, some issues came up, including issues already solved in earlier essays.

What controls “give-up”?

The foraging task needs to give-up on a non-promising odor, ignore it, leave from the current place, and explore for a new odor. In an earlier essay, odor habituation implemented give-up. If the seek didn’t find the food within the habituation time, the sense would disappear, disabling the seek action.

*Animal circling food with no ability to break free.*

The perseveration problem can be solved in many ways, including the goal give-up circuit in essay 17 and the odor habituation in an earlier essay. One approach cuts the sensor; the other disables the action. But two solutions raises the question of more possible solutions, any or all of which might affect the animal.

Sense habituation (cutting sensor)
Habenula give-up (inhibit action)
Motivational state – hypothalamus hunger/satiety
Circadian rhythm – foraging at twilight
Global periodic reset – rest / sleep

Give-up or leave?

The distinction between giving-up and leaving is between abandoning the current action and switching to a new, overriding action. Although the effect is similar, the implementing circuit differs. In a leave circuit, after the give-up time, the animal would actively leave the current area (place avoidance). Assuming the leave action has a higher priority than seeking, then lateral inhibition would disable the seek action. In foraging vocabulary, does failure inhibit exploitation or does it encourage exploration?

*Distinct circuits for give-up and leave to curtail a failed odor approach.*

As the diagram above shows, this distinction isn’t a semantic quibble, but represents different circuits. In the give-up circuit, the quit decision either inhibits the olfactory seek input and/or inhibits the seek action. With seek disable, the default action moves the animal away from the failed odor. In the leave circuit, the quit decision activates a leave action, which moves the animal away from the failed place, inhibiting the seek action laterally.

Leave or avoid?

Leaving an area is a primitive action and is a requirement for foraging. However, neuroscience papers don’t generally study foraging, they study place avoidance from aversive stimuli, which raises a question. Since the physical action of leaving and aversive place avoidance is identical, do the two actions share circuits or are they distinct?

*Distinct leave and avoid actions compared to shared locomotion.*

In the avoid circuit, danger avoidance is distinct from food-seeking, only sharing at the lowest motor layers. In the leave circuit, exploration leaving and place avoidance share the same mid-locomotor action.

Slow and fast twitch swimming

[Lacalli 2012] explores the evolution of chordate swimming, inspired by a discovery of mid-Cambrian fossils, which suggest that fast-twitch muscles are a later addition to a more basal chordate swimming, possibly to escape from new Cambrian predators. The paper explores the non-vertebrate Amphioxus motor circuitry in like of the fossil, suggesting two distinct motor circuits: normal swimming and escape.

*Slow and fast paths for normal swimming and fast predator escape.*

In this model, higher layers are independent paths that only resolve at the lowest motor command neuron level (such as B.rs). For the foraging tasks, this model that leaving an explored area would use a different system from leaving a noxious area (place aversion), despite being the same underlying motion.

Serotonin as muscle gain-control

In the zebrafish, [Wei et al. 2014] studied serotonin in V.dr (dorsal raphe) as gain-control for muscle output, amplifying the effect of glutamate signals. When they inhibited 5HT (serotonin), the muscle only produced 40% of its maximal strength. Serotonin acted as a gain-control, a multiplicative signal that amplified glutamate signals, allowing for a broader dynamic range.

[Kawashima et al. 2016] investigated 5HT in the context of task-learning for muscle effort, where 5HT caches the real-time adjustment by the cerebellum and pretectal areas. When 5HT is disabled, the real-time system still adjusts the muscle effort, but it doesn’t remember the adjustment for future bouts. That study considers the 5HT neurons as leaky integrators of motor-gated visual feedback, where zebrafish gauge the success of swimming effort by visual motion. Notably, the neurons only store visual information when the fish is actively swimming, as an action-outcome integrator.

The two studies focused on opposite muscle effects, both increasing effort and decreasing effort. 5HT can either inhibit or excite depending on the receptor type, suggesting that 5HT shouldn’t be interpreted as representing a specific value, either positive or negative, but instead possibly carrying either value.

Taking these studies as analogies, it seem reasonable to consider V.dr as an action-outcome accumulator for future effort in the 10-30 seconds range, not specific to either positive or negative amplification. Of course, because serotonin has diverse effects in multiple circuits, reality is likely more complicated.

Serotonin zooplankton dispersal and learning

Many aquatic animals have a larval zooplankton stage, where the larva disperses from its spawn point for several days or weeks, then descends to the sea floor for its adult life. A small number of serotonin neurons signal the switch to descend. Essentially, this is a single explore/exploit pair.

*Larva exploring in a dispersal stage, switching to descend to the sea floor for adult life.*

Habenula function circuit

Essay 17 is running with the model of the habenula as central to the give-up/move-on circuit. The following is a straw man model of the habenula based on the above discussion of quitting, leaving and avoiding circuits. Because essay 17 has no learning or higher areas like the striatum, the diagram ignores any learning functionality. This diagram is for a hypothetical pre-stratal habenular function.

*Odor-based locomotion using the habenula.*

Note, this locomotion only includes odor-based navigation. The audio-visual-touch locomotion uses a different system based on the optic tectum. This dual-locomotive system may be the result of a bilaterian chimaera brain [Tosches and Arendt 2013].

The habenula connectivity and avoidance path is loosely based on [Stephenson-Jones et al. 2012] on the lamprey habenula connectivity. The seek path is loosely based on [Derjean et al. 2010] for the zebrafish.

In this model, Hb.m (medial habenula) is primarily a danger-avoidance circuit, and M.ipn (interpeduncular nucleus) is a place avoidance locomotive region. Hb.l (lateral habenula) is a give-up circuit that both inhibits the seek function (giving up) and excites the shared leave locomotor region, implementing the foraging exploit to explore decision. Here, place avoidance and exploratory leaving are treated as equivalent. As mentioned above, this diagram is mean to be a straw man or a thought experiment, because it’s easier to work with a concrete model.

References

Derjean D, Moussaddy A, Atallah E, St-Pierre M, Auclair F, Chang S, Ren X, Zielinski B, Dubuc R. A novel neural substrate for the transformation of olfactory inputs into motor output. PLoS Biol. 2010 Dec 21

Kawashima T, Zwart MF, Yang CT, Mensh BD, Ahrens MB. The Serotonergic System Tracks the Outcomes of Actions to Mediate Short-Term Motor Learning. Cell. 2016 Nov 3

Lacalli, T. (2012). The Middle Cambrian fossil Pikaia and the evolution of chordate swimming. EvoDevo, 3(1), 1-6.

Stephenson-Jones M, Floros O, Robertson B, Grillner S. Evolutionary conservation of the habenular nuclei and their circuitry controlling the dopamine and 5-hydroxytryptophan (5-HT) systems. Proc Natl Acad Sci U S A. 2012 Jan 17

Tosches, Maria Antonietta, and Detlev Arendt. The bilaterian forebrain: an evolutionary chimaera. Current opinion in neurobiology 23.6 (2013): 1080-1089.

Wei, K., Glaser, J.I., Deng, L., Thompson, C.K., Stevenson, I.H., Wang, Q., Hornby, T.G., Heckman, C.J., and Kording, K.P. (2014). Serotonin affects movement gain control in the spinal cord. J. Neurosci. 34