Single-neuron projectome-guided analysis reveals the neural circuit mechanism underlying endogenous opioid antinociception.

Endogenous opioid antinociception is a self-regulatory mechanism that reduces chronic pain, but its underlying circuit mechanism remains largely unknown. Here, we showed that endogenous opioid antinociception required the activation of mu-opioid receptors (MORs) in GABAergic neurons of the central amygdala nucleus (CEA) in a persistent-hyperalgesia mouse model. Pharmacogenetic suppression of these CEAMOR neurons, which mimics the effect of MOR activation, alleviated the persistent hyperalgesia. Furthermore, single-neuron projection analysis revealed multiple projectome-based subtypes of CEAMOR neurons, each innervating distinct target brain regions. We found that the suppression of axon branches projecting to the parabrachial nucleus (PB) of one subtype of CEAMOR neurons alleviated persistent hyperalgesia, indicating a subtype- and axonal-branch-specific mechanism of action. Further electrophysiological analysis revealed that suppression of a distinct CEA-PB disinhibitory circuit controlled endogenous opioid antinociception. Thus, this study identified the central neural circuit that underlies endogenous opioid antinociception, providing new insight into the endogenous pain modulatory mechanisms.

Contributions of repeated learning to memory in humans: insights from single-neuron recordings in the hippocampus and amygdala.

Despite the well-established phenomenon of improved memory performance through repeated learning, studies investigating the associated neural mechanisms have yielded complex and sometimes contradictory findings, and direct evidence from human neuronal recordings has been lacking. This study employs single-neuron recordings with exceptional spatial-temporal resolution, combined with representational similarity analysis, to explore the neural dynamics within the hippocampus and amygdala during repeated learning. Our results demonstrate that in the hippocampus, repetition enhances both representational specificity and fidelity, with these features predicting learning times. Conversely, the amygdala exhibits heightened representational specificity and fidelity during initial learning but does not show improvement with repetition, suggesting functional specialization of the hippocampus and amygdala during different stages of the learning repetition. Specifically, the hippocampus appears to contribute to sustained engagement necessary for benefiting from repeated learning, while the amygdala may play a role in the representation of novel items. These findings contribute to a comprehensive understanding of the intricate interplay between these brain regions in memory processes. Significance statement  For over a century, understanding how repetition contributes to memory enhancement has captivated researchers, yet direct neuronal evidence has been lacking, with a primary focus on the hippocampus and a neglect of the neighboring amygdala. Employing advanced single-neuron recordings and analytical techniques, this study unveils a nuanced functional specialization within the amygdala-hippocampal circuit during various learning repetition. The results highlight the hippocampus's role in sustaining engagement for improved memory with repetition, contrasting with the amygdala's superior ability in representing novel items. This exploration not only deepens our comprehension of memory enhancement intricacies but also sheds light on potential interventions to optimize learning and memory processes.

The medial prefrontal cortex leaves the hippocampus when it prepares for the future.

Our memories help us plan for the future. In some cases, we use memories to repeat the choices that led to preferable outcomes in the past. The success of these memory-guided decisions depends on close interactions between the hippocampus and medial prefrontal cortex. In other cases, we need to use our memories to deduce hidden connections between the present and past situations to decide the best choice of action based on the expected outcome. Our recent study investigated neural underpinnings of such inferential decisions by monitoring neural activity in the medial prefrontal cortex and hippocampus in rats. We identified several neural activity patterns indicating awake memory trace reactivation and restructuring of functional connectivity among multiple neurons. We also found that these patterns occurred concurrently with the ongoing hippocampal activity when rats recalled past events but not when they planned new adaptive actions. Here, we discussed how these computational properties might contribute to success in inferential decision-making and propose a working model on how the medial prefrontal cortex changes its interaction with the hippocampus depending on whether it reflects on the past or looks into the future.

Multimodal convergence in the pedunculopontine tegmental nucleus: Motor, sensory and theta-frequency inputs influence activity of single neurons.

The pedunculopontine tegmental nucleus of the brainstem (PPTg) has extensive interconnections and neuronal-behavioural correlates. It is implicated in movement control and sensorimotor integration. We investigated whether single neuron activity in freely moving rats is correlated with components of skilled forelimb movement, and whether individual neurons respond to both motor and sensory events. We found that individual PPTg neurons showed changes in firing rate at different times during the reach. This type of temporally specific modulation is like activity seen elsewhere in voluntary movement control circuits, such as the motor cortex, and suggests that PPTg neural activity is related to different specific events occurring during the reach. In particular, many neuronal modulations were time-locked to the end of the extension phase of the reach, when fine distal movements related to food grasping occur, indicating strong engagement of PPTg in this phase of skilled individual forelimb movements. In addition, some neurons showed brief periods of apparent oscillatory firing in the theta range at specific phases of the reach-to-grasp movement. When movement-related neurons were tested with tone stimuli, many also responded to this auditory input, allowing for sensorimotor integration at the cellular level. Together, these data extend the concept of the PPTg as an integrative structure in generation of complex movements, by showing that this function extends to the highly coordinated control of the forelimb during skilled reach to grasp movement, and that sensory and motor-related information converges on single neurons, allowing for direct integration at the cellular level.

Location analysis of presynaptically active and silent synapses in single-cultured hippocampal neurons.

A morphologically present but non-functioning synapse is termed a silent synapse. Silent synapses are categorized into "postsynaptically silent synapses," where AMPA receptors are either absent or non-functional, and "presynaptically silent synapses," where neurotransmitters cannot be released from nerve terminals. The presence of presynaptically silent synapses remains enigmatic, and their physiological significance is highly intriguing. In this study, we examined the distribution and developmental changes of presynaptically active and silent synapses in individual neurons. Our findings show a gradual increase in the number of excitatory synapses, along with a corresponding decrease in the percentage of presynaptically silent synapses during neuronal development. To pinpoint the distribution of presynaptically active and silent synapses, i.e., their positional information, we employed Sholl analysis. Our results indicate that the distribution of presynaptically silent synapses within a single neuron does not exhibit a distinct pattern during synapse development in different distance from the cell body. However, irrespective of neuronal development, the proportion of presynaptically silent synapses tends to rise as the projection site moves farther from the cell body, suggesting that synapses near the cell body may exhibit higher synaptic transmission efficiency. This study represents the first observation of changes in the distribution of presynaptically active and silent synapses within a single neuron.

Convolutional neural networks reveal properties of reach-to-grasp encoding in posterior parietal cortex.

Deep neural networks (DNNs) are widely adopted to decode motor states from both non-invasively and invasively recorded neural signals, e.g., for realizing brain-computer interfaces. However, the neurophysiological interpretation of how DNNs make the decision based on the input neural activity is limitedly addressed, especially when applied to invasively recorded data. This reduces decoder reliability and transparency, and prevents the exploitation of decoders to better comprehend motor neural encoding. Here, we adopted an explainable artificial intelligence approach - based on a convolutional neural network and an explanation technique - to reveal spatial and temporal neural properties of reach-to-grasping from single-neuron recordings of the posterior parietal area V6A. The network was able to accurately decode 5 different grip types, and the explanation technique automatically identified the cells and temporal samples that most influenced the network prediction. Grip encoding in V6A neurons already started at movement preparation, peaking during movement execution. A difference was found within V6A: dorsal V6A neurons progressively encoded more for increasingly advanced grips, while ventral V6A neurons for increasingly rudimentary grips, with both subareas following a linear trend between the amount of grip encoding and the level of grip skills. By revealing the elements of the neural activity most relevant for each grip with no a priori assumptions, our approach supports and advances current knowledge about reach-to-grasp encoding in V6A, and it may represent a general tool able to investigate neural correlates of motor or cognitive tasks (e.g., attention and memory tasks) from single-neuron recordings.

Single-neuron projectomes of mouse paraventricular hypothalamic nucleus oxytocin neurons reveal mutually exclusive projection patterns.

Oxytocin (OXT) plays important roles in autonomic control and behavioral modulation. However, it is unknown how the projection patterns of OXT neurons align with underlying physiological functions. Here, we present the reconstructed single-neuron, whole-brain projectomes of 264 OXT neurons of the mouse paraventricular hypothalamic nucleus (PVH) at submicron resolution. These neurons hierarchically clustered into two groups, with distinct morphological and transcriptional characteristics and mutually exclusive projection patterns. Cluster 1 (177 neurons) axons terminated exclusively in the median eminence (ME) and have few collaterals terminating within hypothalamic regions. By contrast, cluster 2 (87 neurons) sent wide-spread axons to multiple brain regions, but excluding ME. Dendritic arbors of OXT neurons also extended outside of the PVH, suggesting capability to sense signals and modulate target regions. These single-neuron resolution observations reveal distinct OXT subpopulations, provide comprehensive analysis of their morphology, and lay the structural foundation for better understanding the functional heterogeneity of OXT neurons.

Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus.

Recognizing familiar faces and learning new faces play an important role in social cognition. However, the underlying neural computational mechanisms remain unclear. Here, we record from single neurons in the human amygdala and hippocampus and find a greater neuronal representational distance between pairs of familiar faces than unfamiliar faces, suggesting that neural representations for familiar faces are more distinct. Representational distance increases with exposures to the same identity, suggesting that neural face representations are sharpened with learning and familiarization. Furthermore, representational distance is positively correlated with visual dissimilarity between faces, and exposure to visually similar faces increases representational distance, thus sharpening neural representations. Finally, we construct a computational model that demonstrates an increase in the representational distance of artificial units with training. Together, our results suggest that the neuronal population geometry, quantified by the representational distance, encodes face familiarity, similarity, and learning, forming the basis of face recognition and memory.

Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans.

How the human cortex integrates ("binds") information encoded by spatially distributed neurons remains largely unknown. One hypothesis suggests that synchronous bursts of high-frequency oscillations ("ripples") contribute to binding by facilitating integration of neuronal firing across different cortical locations. While studies have demonstrated that ripples modulate local activity in the cortex, it is not known whether their co-occurrence coordinates neural firing across larger distances. We tested this hypothesis using local field-potentials and single-unit firing from four 96-channel microelectrode arrays in the supragranular cortex of 3 patients. Neurons in co-rippling locations showed increased short-latency co-firing, prediction of each other's firing, and co-participation in neural assemblies. Effects were similar for putative pyramidal and interneurons, during non-rapid eye movement sleep and waking, in temporal and Rolandic cortices, and at distances up to 16 mm (the longest tested). Increased co-prediction during co-ripples was maintained when firing-rate changes were equated, indicating that it was not secondary to non-oscillatory activation. Co-rippling enhanced prediction was strongly modulated by ripple phase, supporting the most common posited mechanism for binding-by-synchrony. Co-ripple enhanced prediction is reciprocal, synergistic with local upstates, and further enhanced when multiple sites co-ripple, supporting re-entrant facilitation. Together, these results support the hypothesis that trans-cortical co-occurring ripples increase the integration of neuronal firing of neurons in different cortical locations and do so in part through phase-modulation rather than unstructured activation.

Consciousness: a neurosurgical perspective.

Neurosurgeons are in a unique position to shed light on the neural basis for consciousness, not only by their clinical care of patients with compromised states of consciousness, but also by employing neurostimulation and neuronal recordings through intracranial electrodes in awake surgical patients, as well as during stages of sleep and anethesia. In this review, we discuss several aspects of consciousness, i.e., perception, memory, and willed actions, studied by electrical stimulation and single neuron recordings in the human brain. We demonstrate how specific neuronal activity underlie the emergence of concepts, memories, and intentions in human consciousness. We discuss the representation of specific conscious content by temporal lobe neurons and present the discovery of "concept cells" and the encoding and retrieval of memories by neurons in the medial temporal lobe. We review prefrontal and parietal neuronal activation that precedes conscious intentions to act. Taken together with other studies in the field, these findings suggest that specific conscious experience may arise from stochastic fluctuations of neuronal activity, reaching a dynamic threshold. Advances in brain recording and stimulation technology coupled with the rapid rise in artificial intelligence are likely to increase the amount and analysis capabilities of data obtained from the human brain, thereby improving the decoding of conscious and preconscious states and open new horizons for modulation of human cognitive functions such as memory and volition.

Representation of Motion Direction in Visual Area MT Accounts for High Sensitivity to Centripetal Motion, Aligning with Efficient Coding of Retinal Motion Statistics.

The overrepresentation of centrifugal motion in the middle temporal visual area (area MT) has long been thought to provide an efficient coding strategy for optic flow processing. However, this overrepresentation compromises the detection of approaching objects, which is essential for survival. In the present study, we revisited this long-held notion by reanalyzing motion selectivity in area MT of three macaque monkeys (two males, one female) using random-dot stimuli instead of spot stimuli. We found no differences in the number of neurons tuned to centrifugal versus centripetal motion; however, centrifugally tuned neurons showed stronger tuning than centripetally tuned neurons. This was attributed to the heightened suppression of responses in centrifugal neurons to centripetal motion compared with that of centripetal neurons to centrifugal motion. Our modeling implies that this intensified suppression accounts for superior detection performance for weak centripetal motion stimuli. Moreover, through Fisher information analysis, we establish that the population sensitivity to motion direction in peripheral vision corresponds well with retinal motion statistics during forward locomotion. While these results challenge established concepts, considering the interplay of logarithmic Gaussian receptive fields and spot stimuli can shed light on the previously documented overrepresentation of centrifugal motion. Significantly, our findings reconcile a previously found discrepancy between MT activity and human behavior, highlighting the proficiency of peripheral MT neurons in encoding motion direction efficiently.SIGNIFICANCE STATEMENT The efficient coding hypothesis states that sensory neurons are tuned to specific, frequently experienced stimuli. Whereas previous work has found that neurons in the middle temporal (MT) area favor centrifugal motion, which results from forward locomotion, we show here that there is no such bias. Moreover, we found that the response of centrifugal neurons for centripetal motion was more suppressed than that of centripetal neurons for centrifugal motion. Combined with modeling, this provides a solution to a previously known discrepancy between reported centrifugal bias in MT and better detection of centripetal motion by human observers. Additionally, we show that population sensitivity in peripheral MT neurons conforms to an efficient code of retinal motion statistics during forward locomotion.

Spike-frequency adaptation inhibits the pairwise spike correlation.

Introduction: The spike train output correlation with pairwise neurons determines the neural population coding, which depends on the average firing rate of individual neurons. Spike frequency adaptation (SFA), which serves as an essential cellular encoding strategy, modulates the firing rates of individual neurons. However, the mechanism by which the SFA modulates the output correlation of the spike trains remains unclear. Methods: We introduce a pairwise neuron model that receives correlated inputs to generate spike trains, and the output correlation is qualified using Pearson correlation coefficient. The SFA is modeled using adaptation currents to examine its effect on the output correlation. Moreover, we use dynamic thresholds to explore the effect of SFA on output correlation. Furthermore, a simple phenomenological neuron model with a threshold-linear transfer function is utilized to confirm the effect of SFA on decreasing the output correlation. Results: The results show that the adaptation currents decreased the output correlation by reducing the firing rate of a single neuron. At the onset of a correlated input, a transient process shows a decrease in interspike intervals (ISIs), resulting in a temporary increase in the correlation. When the adaptation current is sufficiently activated, the correlation reached a steady state, and the ISIs are maintained at higher values. The enhanced adaptation current achieved by increasing the adaptation conductance further reduces the pairwise correlation. While the time and slide windows influence the correlation, they make no difference in the effect of SFA on decreasing the output correlation. Moreover, SFA simulated by dynamic thresholds also decreases the output correlation. Furthermore, the simple phenomenological neuron model with a threshold-linear transfer function confirms the effect of SFA on decreasing the output correlation. The strength of the signal input and the slope of the linear component of the transfer function, the latter of which can be decreased by SFA, could together modulate the strength of the output correlation. Stronger SFA will decrease the slope and hence decrease the output correlation. Conclusions: The results reveal that the SFA reduces the output correlation with pairwise neurons in the network by reducing the firing rate of individual neurons. This study provides a link between cellular non-linear mechanisms and network coding strategies.

A biophysical and statistical modeling paradigm for connecting neural physiology and function.

To understand single neuron computation, it is necessary to know how specific physiological parameters affect neural spiking patterns that emerge in response to specific stimuli. Here we present a computational pipeline combining biophysical and statistical models that provides a link between variation in functional ion channel expression and changes in single neuron stimulus encoding. More specifically, we create a mapping from biophysical model parameters to stimulus encoding statistical model parameters. Biophysical models provide mechanistic insight, whereas statistical models can identify associations between spiking patterns and the stimuli they encode. We used public biophysical models of two morphologically and functionally distinct projection neuron cell types: mitral cells (MCs) of the main olfactory bulb, and layer V cortical pyramidal cells (PCs). We first simulated sequences of action potentials according to certain stimuli while scaling individual ion channel conductances. We then fitted point process generalized linear models (PP-GLMs), and we constructed a mapping between the parameters in the two types of models. This framework lets us detect effects on stimulus encoding of changing an ion channel conductance. The computational pipeline combines models across scales and can be applied as a screen of channels, in any cell type of interest, to identify ways that channel properties influence single neuron computation.

Neuronal Spiking Responses to Direct Electrical Microstimulation in the Human Cortex.

Microstimulation can modulate the activity of individual neurons to affect behavior, but the effects of stimulation on neuronal spiking are complex and remain poorly understood. This is especially challenging in the human brain where the response properties of individual neurons are sparse and heterogeneous. Here we use microelectrode arrays in the human anterior temporal lobe in 6 participants (3 female) to examine the spiking responses of individual neurons to microstimulation delivered through multiple distinct stimulation sites. We demonstrate that individual neurons can be driven with excitation or inhibition using different stimulation sites, which suggests an approach for providing direct control of spiking activity at the single-neuron level. Spiking responses are inhibitory in neurons that are close to the site of stimulation, while excitatory responses are more spatially distributed. Together, our data demonstrate that spiking responses of individual neurons can be reliably identified and manipulated in the human cortex.SIGNIFICANCE STATEMENT One of the major limitations in our ability to interface directly with the human brain is that the effects of stimulation on the activity of individual neurons remain poorly understood. This study examines the spiking responses of neurons in the human temporal cortex in response to pulses of microstimulation. This study finds that individual neurons can either be excited or inhibited depending on the site of stimulation. These data suggest an approach for modulating the spiking activity of individual neurons in the human brain.

Exploring the brain with sleep-related injuries, and fixing it.

The focus of my research efforts rests with determining dysfunctional neural systems underlying disorders of sleep, and identifying interventions to overcome those disorders. Aberrant central and physiological control during sleep exerts serious consequences, including disruptions in breathing, motor control, blood pressure, mood, and cognition, and plays a major role in sudden infant death syndrome, congenital central hypoventilation, and sudden unexpected death in epilepsy, among other concerns. The disruptions can be traced to brain structural injury, leading to inappropriate outcomes. Identification of failing systems arose from the assessment of single neuron discharge in intact, freely moving and state-changing human and animal preparations within multiple systems, including serotonergic action and motor control sites. Optical imaging of chemosensitive, blood pressure and other breathing regulatory areas, especially during development, were useful to show integration of regional cellular action in modifying neural output. Identification of damaged neural sites in control and afflicted humans through structural and functional magnetic resonance imaging procedures helped to identify the sources of injury, and the nature of interactions between brain sites that compromise physiological systems and lead to failure. Interventions to overcome flawed regulatory processes were developed, and incorporate noninvasive neuromodulatory means to recruit ancient reflexes or provide peripheral sensory stimulation to assist breathing drive to overcome apnea, reduce the frequency of seizures, and support blood pressure in conditions where a failure to perfuse can lead to death.

Motor decoding from the posterior parietal cortex using deep neural networks.

Objective.Motor decoding is crucial to translate the neural activity for brain-computer interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless, it is still unclear how different DNNs perform in different motor decoding problems and scenarios, and which network could be a good candidate for invasive BCIs.Approach.Fully-connected, convolutional, and recurrent neural networks (FCNNs, CNNs, RNNs) were designed and applied to decode motor states from neurons recorded from V6A area in the posterior parietal cortex (PPC) of macaques. Three motor tasks were considered, involving reaching and reach-to-grasping (the latter under two illumination conditions). DNNs decoded nine reaching endpoints in 3D space or five grip types using a sliding window approach within the trial course. To evaluate decoders simulating a broad variety of scenarios, the performance was also analyzed while artificially reducing the number of recorded neurons and trials, and while performing transfer learning from one task to another. Finally, the accuracy time course was used to analyze V6A motor encoding.Main results.DNNs outperformed a classic Naïve Bayes classifier, and CNNs additionally outperformed XGBoost and Support Vector Machine classifiers across the motor decoding problems. CNNs resulted the top-performing DNNs when using less neurons and trials, and task-to-task transfer learning improved performance especially in the low data regime. Lastly, V6A neurons encoded reaching and reach-to-grasping properties even from action planning, with the encoding of grip properties occurring later, closer to movement execution, and appearing weaker in darkness.Significance.Results suggest that CNNs are effective candidates to realize neural decoders for invasive BCIs in humans from PPC recordings also reducing BCI calibration times (transfer learning), and that a CNN-based data-driven analysis may provide insights about the encoding properties and the functional roles of brain regions.

Comparing representations and computations in single neurons versus neural networks.

Single-neuron-level explanations have been the gold standard in neuroscience for decades. Recently, however, neural-network-level explanations have become increasingly popular. This increase in popularity is driven by the fact that the analysis of neural networks can solve problems that cannot be addressed by analyzing neurons independently. In this opinion article, I argue that while both frameworks employ the same general logic to link physical and mental phenomena, in many cases the neural network framework provides better explanatory objects to understand representations and computations related to mental phenomena. I discuss what constitutes a mechanistic explanation in neural systems, provide examples, and conclude by highlighting a number of the challenges and considerations associated with the use of analyses of neural networks to study brain function.

A revisit of the amygdala theory of autism: Twenty years after.

The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.

Regional and cell-type-specific afferent and efferent projections of the mouse claustrum.

The claustrum (CLA) is a conspicuous subcortical structure interconnected with cortical and subcortical regions. Its regional anatomy and cell-type-specific connections in the mouse remain not fully determined. Using multimodal reference datasets, we confirmed the delineation of the mouse CLA as a single group of neurons embedded in the agranular insular cortex. We quantitatively investigated brain-wide inputs and outputs of CLA using bulk anterograde and retrograde viral tracing data and single neuron tracing data. We found that the prefrontal module has more cell types projecting to the CLA than other cortical modules, with layer 5 IT neurons predominating. We found nine morphological types of CLA principal neurons that topographically innervate functionally linked cortical targets, preferentially the midline cortical areas, secondary motor area, and entorhinal area. Together, this study provides a detailed wiring diagram of the cell-type-specific connections of the mouse CLA, laying a foundation for studying its functions at the cellular level.

Temporal derivative computation in the dorsal raphe network revealed by an experimentally driven augmented integrate-and-fire modeling framework.

By means of an expansive innervation, the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior.