In oscillatory circuits, some actions of neuromodulators depend on the oscillation frequency.However, the mechanisms are poorly understood. We explored this problem by characterizing neuromodulation of the lateral pyloric (LP) neuron of the crab stomatogastric ganglion. Many peptide modulators, including proctolin, activate the same ionic current (I MI ) in stomatogastric neurons. Because I MI is fast and non-inactivating, its peak level does not depend on the temporal properties of neuronal activity. We found, however, that the amplitude and peak time of the proctolin-activated current in LP is frequency-dependent. Because frequency affects the rate of voltage change, we measured these currents with voltage ramps of different slopes and found that proctolin activated two kinetically distinct ionic currents: the known I MI , whose amplitude is independent of ramp slope or direction, and an inactivating current (I MI-T ), which was only activated by positive ramps and whose amplitude increased with increasing ramp slope. Using a conductance-based model we found that I MI and I MI-T make distinct contributions to the bursting activity, with I MI increasing the excitability, and I MI-T regulating the burst onset by modifying the post-inhibitory rebound in a frequency-dependent manner. The voltagedependence and partial calcium permeability of I MI-T is similar to other characterized neuromodulatoractivated currents in this system, suggesting that these are isoforms of the same channel. Our computational model suggests that calcium permeability may allow this current to also activate the large calcium-dependent potassium current in LP, providing an additional mechanism to regulate burst termination. These results demonstrate a mechanism for frequency-dependent actions of neuromodulators. Significance statementOscillatory neurons respond to synaptic input in complex ways that depend on the polarity, amplitude, and rate of the input, and intrinsic properties of the cell. As a result, neuromodulator inputs that activate voltage-gated ionic currents can have indirect and state-dependent effects. We show that when a target of neuromodulation is a transient ionic current, an additional layer of complexity of the response emerges in which the oscillation frequency and the indirect influence of other ionic currents shape the amplitude and temporal properties of the neuronal response to the modulator.

Neural circuits can generate many spike patterns, but only some are functional. The study of how circuits generate and maintain functional dynamics is hindered by a poverty of description of circuit dynamics across functional and dysfunctional states. For example, although the regular oscillation of a central pattern generator is well characterized by its frequency and the phase relationships between its neurons, these metrics are ineffective descriptors of the irregular and aperiodic dynamics that circuits can generate under perturbation or in disease states. By recording the circuit dynamics of the well-studied pyloric circuit in Cancer borealis, we used statistical features of spike times from neurons in the circuit to visualize the spike patterns generated by this circuit under a variety of conditions. This approach captures both the variability of functional rhythms and the diversity of atypical dynamics in a single map. Clusters in the map identify qualitatively different spike patterns hinting at different dynamical states in the circuit. State probability and the statistics of the transitions between states varied with environmental perturbations, removal of descending neuromodulatory inputs, and the addition of exogenous neuromodulators. This analysis reveals strong mechanistically interpretable links between complex changes in the collective behavior of a neural circuit and specific experimental manipulations, and can constrain hypotheses of how circuits generate functional dynamics despite variability in circuit architecture and environmental perturbations.

280 / 350 words) 11 Locomotion is essential for an animal's survival. This behavior can range from directional 12 changes to adapting the motor force to the conditions of its surroundings. Even if speed and 13 force of movement are changing, the relative coordination between the limbs or body 14 segments has to stay stable in order to provide the necessary thrust. The coordinating 15 information necessary for this task is not always conveyed by sensory pathways. Adaptation 16 is well studied in sensory neurons, but only few studies have addressed if and how 17 coordinating information changes in cases where a local circuit within the central nervous 18 system is responsible for the coordination between body segments at different locomotor 19 activity states. 20One system that does not depend on sensory information to coordinate a chain of coupled 21 oscillators is the swimmeret system of crayfish. Here, the coordination of four coupled CPGs 22 is controlled by central Coordinating Neurons. Cycle by cycle, the Coordinating Neurons 23 encode information about the activity state of their home ganglion as burst of spikes, and send 24 it as corollary discharge to the neighboring ganglia. Activity states, or excitation levels, are 25 variable in both the living animal and isolated nervous system; yet the amount of coordinating 26 spikes per burst is limited. 27Here, we demonstrate that the system's excitation level tunes the encoding properties of the 28 Coordinating Neurons. Their ability to adapt to excitation level, and thus encode relative 29 changes in their home ganglion's activity states, is mediated by a balancing mechanism. 30Manipulation of cholinergic pathways directly affected the coordinating neurons' 31 electrophysiological properties. Yet, these changes were counteracted by the network's 32 influence. This balancing may be one feature to adapt the limited spike range to the system's 33 current activity state. 34 35 37 One remarkable feature of animals is their ability to extract useful information from 38 ubiquitous noise to ensure the organism's functionality. For example, sensory neurons adapt 39 to occurring ranges of stimulus intensities to maximize information transfer via their electrical 40 activity (Dean et al., 2005; Laughlin, 1981; Maravall et al., 2007), a process in accordance 41 with Barlow's efficient coding hypothesis (Barlow, 1961). Most studies investigating neural 42 adaptation and gain control focus on perception of environmental stimuli and adaptation of 43 sensory neurons. Less is known about adaptive abilities of interneurons which provide 44 information about internal states. Such interneurons can play important roles in coupling of 45 neural networks that underlie behavior. Coupling networks through interneurons allows for 46 faster information exchange compared to coupling via sensory pathways (LeGal et al., 2017). 47 For example, lamprey increase their respiratory frequency in response to increased activity of 48 the mesencephalic locomotor region even before proprioceptiv...