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bioRxiv2025

Comparative Analysis of FOXP2 Expression in the Thalamus of Mice, Rats, and Macaques: Implications for the Evolution of Language Circuits.

FOXP2 is a transcription factor essential for the development and function of neural circuits involved in language. Although its expression has been extensively characterized in the cortex and basal ganglia, its organization within the adult thalamus remains poorly understood. In this study, we present a comparative analysis of FoxP2 protein expression across thalamic nuclei in mice, rats, and macaques, with a focus on nuclei associated with higher-order cognitive functions and language-related circuits in humans. We found that FoxP2 is expressed in most thalamic nuclei across species, with a consistent absence in the reticular nucleus and zona incerta. Expression was highest in midline and intralaminar nuclei, whereas the anterior group showed low and variable expression among species. Macaques exhibited broader and, in some nuclei, more intense FoxP2 expression, particularly in associative regions such as the pulvinar, lateral geniculate, and parts of the ventral group, indicating increased specialization of thalamocortical pathways. This distribution suggests a conserved role for FoxP2 in shaping thalamic circuits supporting sensorimotor integration, attention, memory, and linguistic processing. Phylogenetic comparisons further indicate that enhanced FoxP2 expression in associative thalamic territories in primates, likely intensified in humans, may have contributed to the evolution of neural circuits required for speech and language. These findings provide molecular and anatomical insights into how FoxP2 helps organize thalamocortical networks relevant both to language function and to neuropsychiatric disorders involving thalamocortical dysconnectivity.

neuroscience↗

bioRxiv2021

Does the Zebra Finch Mating Song Circuit Use Spike Times Efficiently?

Precise and reliable spike times are thought to subserve multiple possible functions, including improving the accuracy of encoding stimuli or behaviours relative to other coding schemes. Indeed, repeating sequences of spikes with sub-millisecond precision exist in nature, such as the synfire chain of spikes in area HVC of the zebra-finch mating-song circuit. Here, we analyzed what impact precise and reliable spikes have on the encoding accuracy for both the zebra-finch and more generic neural circuits using computational modelling. Our results show that neural circuits can use precisely timed spikes to encode signals with a higher-order accuracy than a conventional rate code. Circuits with precisely timed and reliably emitted spikes increase their encoding accuracy linearly with network size, which is the hallmark signature of an efficient code. This qualitatively differs from circuits that employ a rate code which increase their encoding accuracy with the square-root of network size. However, this improved scaling is dependent on the spikes becoming more accurate and more reliable with larger networks. Finally, we discuss how to test this scaling relationship in the zebra mating song circuit using both neural data and song-spectrogram-based recordings while taking advantage of the natural fluctuation in HVC network size due to neurogenesis. The zebra-finch mating-song circuit may represent the most likely candidate system for the use of spike-timing-based, efficient coding strategies in nature.

neuroscience↗

bioRxiv2022

Presynaptic contact and activity opposingly regulate postsynaptic dendrite outgrowth

The organization of neural circuits determines nervous system function. Variability can arise during neural circuit development (e.g. neurite morphology, axon/dendrite position). To ensure robust nervous system function, mechanisms must exist to accommodate variation in neurite positioning during circuit formation. Previously we developed a model system in the Drosophila ventral nerve cord to conditionally induce positional variability of a proprioceptive sensory axon terminal, and used this model to show that when we altered the presynaptic position of the sensory neuron, its major postsynaptic interneuron partner modified its dendritic arbor to match the presynaptic contact, resulting in functional synaptic input (Sales et al., 2019). Here we investigate the cellular mechanisms by which the interneuron dendrites detect and match variation in presynaptic partner location and input strength. We manipulate the presynaptic sensory neuron by (a) ablation; (b) silencing or activation; or (c) altering its location in the neuropil. From these experiments we conclude that there are two opposing mechanisms used to establish functional connectivity in the face of presynaptic variability: presynaptic contact stimulates dendrite outgrowth locally, whereas presynaptic activity inhibits postsynaptic dendrite outgrowth globally. These mechanisms are only active during an early larval critical period for structural plasticity. Collectively, our data provide new insights into dendrite development, identifying mechanisms that allow dendrites to flexibly and robustly respond to developmental variability in presynaptic location and input strength.

neuroscience↗

bioRxiv2024

AgRP neurons mediate activity-dependent development of oxytocin connectivity and autonomic regulation

During postnatal life, the adipocyte-derived hormone leptin is required for proper targeting of neural inputs to the paraventricular nucleus of the hypothalamus (PVH) and impacts the activity of neurons containing agouti-related peptide (AgRP) in the arcuate nucleus of the hypothalamus. Activity-dependent developmental mechanisms are known to play a defining role during postnatal organization of neural circuits, but whether leptin-mediated postnatal neuronal activity specifies neural projections to the PVH or impacts downstream connectivity is largely unexplored. Here, we blocked neuronal activity of AgRP neurons during a discrete postnatal period and evaluated development of AgRP inputs to defined regions in the PVH, as well as descending projections from PVH oxytocin neurons to the dorsal vagal complex (DVC) and assessed their dependence on leptin or postnatal AgRP neuronal activity. In leptin-deficient mice, AgRP inputs to PVH neurons were significantly reduced, as well as oxytocin-specific neuronal targeting by AgRP. Moreover, downstream oxytocin projections from the PVH to the DVC were also impaired, despite the lack of leptin receptors found on PVH oxytocin neurons. Blocking AgRP neuron activity specifically during early postnatal life reduced the density of AgRP inputs to the PVH, as well as the density of projections from PVH oxytocin neurons to the DVC, and these innervation deficits were associated with dysregulated autonomic function. These findings suggest that postnatal targeting of descending PVH oxytocin projections to the DVC requires leptin-mediated AgRP neuronal activity, and represents a novel activity-dependent mechanism for hypothalamic specification of metabolic circuitry, with consequences for autonomic regulation. Significance statementHypothalamic neural circuits maintain homeostasis by coordinating endocrine signals with autonomic responses and behavioral outputs to ensure that physiological responses remain in tune with environmental demands. The paraventricular nucleus of the hypothalamus (PVH) plays a central role in metabolic regulation, and the architecture of its neural inputs and axonal projections is a defining feature of how it receives and conveys neuroendocrine information. In adults, leptin regulates multiple aspects of metabolic physiology, but it also functions during development to direct formation of circuits controlling homeostatic functions. Here we demonstrate that leptin acts to specify the input-output architecture of PVH circuits through an activity-dependent, transsynaptic mechanism, which represents a novel means of sculpting neuroendocrine circuitry, with lasting effects on how the brain controls energy balance.

neuroscience↗

bioRxiv2015

Rapid three dimensional two photon neural population scanning

Recording the activity of neural populations at high sampling rates is a fundamental requirement for understanding computation in neural circuits. Two photon microscopy provides one promising approach towards this. However, neural circuits are three dimensional, and functional imaging in two dimensions fails to capture the 3D nature of neural dynamics. Electrically tunable lenses (ETLs) provide a simple and cheap method to extend laser scanning microscopy into the relatively unexploited third dimension. We have therefore incorporated them into our Adaptive Spiral Scanning (SSA) algorithm, which calculates kinematically efficient scanning strategies using radially modulated spiral paths. We characterised the response of the ETL, incorporated its dynamics using MATLAB models of the SSA algorithm and tested the models on populations of Izhikevich neurons of varying size and density. From this, we show that our algorithms can theoretically at least achieve sampling rates of 36.2Hz compared to 21.6Hz previously reported for 3D scanning techniques.

Neuroscience↗

bioRxiv2017

Theoretical principles of multiscale spatiotemporal control of neuronal networks: a complex systems perspective

Success in the fine control of the nervous system depends on a deeper understanding of how neural circuits control behavior. There is, however, a wide gap between the components of neural circuits and behavior. We advance the idea that a suitable approach for narrowing this gap has to be based on a multiscale information-theoretic description of the system. We evaluate the possibility that brain-wide complex neural computations can be dissected into a hierarchy of computational motifs that rely on smaller circuit modules interacting at multiple scales. In doing so, we draw attention to the importance of formalizing the goals of stimulation in terms of neural computations so that the possible implementations are matched in scale to the underlying circuit modules.

neuroscience↗

bioRxiv2020

A spiking neural program for sensory-motor control during foraging in flying insects.

Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning and motor control. We focus on cast & surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast & surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.

neuroscience↗

bioRxiv2022

Sensory input to cortex encoded on low-dimensional periphery-correlated subspaces

As information about the world is conveyed from the sensory periphery to central neural circuits, it mixes with complex ongoing cortical activity. How do neural populations keep track of sensory signals, separating them from noisy ongoing activity? Here we show that sensory signals are encoded more reliably in certain low-dimensional subspaces. These coding subspaces are defined by correlations between neural activity in primary sensory cortex and upstream sensory brain regions; the most correlated dimensions were best for decoding. We analytically show that these correlation-based coding subspaces improve, reaching optimal limits (without an ideal observer) as noise correlations between cortex and upstream regions are reduced. We show that this principle generalizes across diverse sensory stimuli in the olfactory system and the visual system of awake mice. Our results demonstrate an algorithm the cortex may use to multiplex different functions, processing sensory input in low dimensional subspaces separate from other ongoing functions. Significance statementTraditionally, primary sensory cortex was thought to have one job - processing sensory signals. As technical advances allow more holistic measurements of the brain and body in action, it has become clear that primary sensory cortex is involved with many other aspects of brain function, not just dealing with sensory input. How can a single neural circuit juggle multiple jobs simultaneously? Here we use numerical, analytical, and experimental methods to demonstrate an algorithm the brain may use to solve this problem by separating different jobs into different subspaces defined by correlations between primary sensory cortex and the brain regions that source the sensory input signals.

neuroscience↗

bioRxiv2023

Distance-dependent reconfiguration of hubs in Alzheimer's disease:a cross-tissue functional network study

The hubs of the intra-grey matter (GM) network were sensitive to anatomical distance and susceptible to neuropathological damage. However, few studies examined the hubs of cross-tissue distance-dependent networks and their changes in Alzheimers disease (AD). Using resting-state fMRI data of 30 AD patients and 37 normal older adults (NC), we constructed the cross-tissue networks based on functional connectivity (FC) between GM and white matter (WM) voxels. In the full-ranged and distance-dependent networks (characterized by gradually increased Euclidean distances between GM and WM voxels), their hubs were identified with weight degree metrics (frWD and ddWD). We compared these WD metrics between AD and NC; using the resultant abnormal WDs as the seeds, we performed seed-based FC analysis. With increasing distance, the GM hubs of distance-dependent networks moved from the medial to lateral cortices, and the WM hubs spread from the projection fibers to longitudinal fascicles. Abnormal ddWD metrics in AD were primarily located in the hubs of distance-dependent networks around 20-100mm. Decreased ddWDs were located in the left corona radiation (CR), which had decreased FCs with the executive networks GM regions in AD. Increased ddWDs were located in the posterior thalamic radiation (PTR) and the temporal-parietal-occipital junction (TPO), and their FCs were larger in AD. Increased ddWDs were shown in the sagittal striatum, which had larger FCs with the salience networks GM regions in AD. The reconfiguration of cross-tissue distance-dependent networks possibly reflected the disruption in the neural circuit of executive function and the compensatory changes in the neural circuits of visuospatial and social-emotional functions in AD.

neuroscience↗

bioRxiv2025

Beyond the straight path: high-density laminar recordings in the ventral hippocampus with curved microprobes

Neural circuits are organized within complex three-dimensional architectures. Most neural interfaces follow linear insertion trajectories, limiting their ability to achieve laminar recording in brain regions where neuronal layers lie approximately parallel to the insertion path. Here, we introduce a curved flexible neural interface that enables near-perpendicular alignment of the recording sites with the targeted neuronal layers. The device consists of a flexible Parylene-C neural probe, integrating 16 PEDOT:BF4-coated Au microelectrodes and a transient silk fibroin stiffener for controlled implantation. The electrodes exhibit low impedance at 1 kHz (29.2 {+/-} 2.5 k{Omega}) and were accurately positioned across the layers of the ventral hippocampus using a rotational implantation strategy. Chronic in vivo recordings demonstrate stable electrochemical performance and reliable acquisition of local field potentials over four weeks. This work establishes a strategy for anatomically matched neural interfacing, enabling high-resolution investigation of neural circuits that are challenging to study with conventional linear probes.

neuroscience↗

bioRxiv2017

A chemosensory switch couples genetic sex to behavioral valence

As a fundamental dimension of internal state, biological sex modulates neural circuits to generate naturally occurring behavioral variation. Understanding how and why circuits are tuned by sex can provide important insights into neural and behavioral plasticity. Here, we find that sexually dimorphic behavioral responses to C. elegans ascaroside sex pheromones are implemented by the functional modulation of shared chemosensory circuitry. In particular, the sexual state of a single sensory neuron pair, ADF, determines the nature of an animal's behavioral response regardless of the sex of the rest of the body. Genetic feminization of ADF causes males to be repelled by, rather than attracted to, ascarosides, while masculinization of ADF is sufficient to make ascarosides attractive to hermaphrodites. Genetic sex modulates ADF function by tuning chemosensation: ADF is able to detect the ascaroside ascr#3 only in males, a consequence of cell-autonomous action of the master sexual regulator tra-1. Genetic sex regulates behavior in part through the conserved DMRT gene mab-3, whose male-specific expression in ADF promotes ascaroside attraction. The sexual modulation of ADF has a key role in reproductive fitness, as feminization or ablation of ADF renders males unable to use ascarosides to locate mates. These results demonstrate that DMRT genes can functionally modulate shared neural circuits; moreover, they reveal an adaptive mechanism in which chromosomal sex controls a cell-autonomous switch that tunes sensory function, determines behavioral valence, and promotes reproductive fitness.

neuroscience↗

bioRxiv2021

Next-generation biocomputing: mimicking artificial neural network with genetic circuits

Artificial neural network (ANN) is nowadays one of the most used and researched computational methods. In the field of biocomputing, however, synthetic biologists are still using logic gate technologies to design genetic circuits. We here propose and computationally validate a novel method to mimic ANN with genetic circuits. We first describe the flow and the mathematical expression of this genetic circuit design and then we provide in silico proof to support the functionality of our method in regression and classification analyses. We believe that this de novo genetic circuit design method would have wide applications in biotechnology.

synthetic biology↗

bioRxiv2023

Degraded tactile coding in the Cntnap2 mouse model of autism

Atypical sensory processing in autism involves altered neural circuit function and neural coding in sensory cortex, but the nature of coding disruption is poorly understood. We characterized neural coding in L2/3 of whisker somatosensory cortex (S1) of Cntnap2-/- mice, an autism model with pronounced hypofunction of parvalbumin (PV) inhibitory circuits. We tested for both excess spiking, which is often hypothesized in autism models with reduced inhibition, and alterations in somatotopic coding, using c-fos immunostaining and 2-photon calcium imaging in awake mice. In Cntnap2-/- mice, c- fos-(+) neuron density was elevated in L2/3 of S1 under spontaneous activity conditions, but comparable to control mice after whisker stimulation, suggesting that sensory-evoked spiking was relatively normal. 2-photon GCaMP8m imaging in L2/3 pyramidal cells revealed no increase in whisker-evoked response magnitude, but instead showed multiple signs of degraded somatotopic coding. These included broadening of whisker tuning curves, blurring of the whisker map, and blunting of the point representation of each whisker. These altered properties were more pronounced in noisy than sparse sensory conditions. Tuning instability, assessed over 2-3 weeks of longitudinal imaging, was also significantly increased in Cntnap2-/- mice. Thus, Cntnap2-/- mice show no excess spiking, but a degraded and unstable tactile code in S1.

neuroscience↗

bioRxiv2018

Cell-type specific patterned stimulus-independent neuronal activity in the Drosophila visual system during synapse formation

Stereotyped synaptic connections define the neural circuits of the brain. In vertebrates, stimulus-independent activity contributes to neural circuit formation. It is unknown whether this type of activity is a general feature of nervous system development. Here, we report patterned, stimulus-independent neural activity in the Drosophila visual system during synaptogenesis. Using in vivo calcium, voltage, and glutamate imaging, we found that all neurons participate in this spontaneous activity, which is characterized by brain-wide periodic active and silent phases. Glia are active in a complementary pattern. Each of the 15 examined of the over 100 specific neuron types in the fly visual system exhibited a unique activity signature. The activity of neurons that are synaptic partners in the adult was highly correlated during development. We propose that this cell type-specific activity coordinates the development of the functional circuitry of the adult brain.

neuroscience↗

bioRxiv2026

A hypothalamic circuit links hunger to mesolimbic dopamine to drive feeding

Hunger enhances the motivational value of food, but the neural circuits linking homeostatic hunger neurons to dopamine systems remain poorly understood. Here, we identify a hypothalamic-midbrain neural circuit through which AgRP neurons engage the mesolimbic dopamine system. We demonstrate that AgRP neuron activity is both necessary and sufficient for the potentiated dopamine response to food by hunger, and that they achieve this by reducing inhibitory drive onto midbrain dopamine neurons. Importantly, this AgRP neuron-evoked dopamine signaling is required for subsequent food intake. Mechanistically, we find that AgRP neuron projections to NPY-sensitive paraventricular hypothalamic neurons selectively amplify food-evoked dopamine release to promote feeding behavior. These findings reveal a circuit mechanism through which hunger recruits dopamine signaling to transform physiological need into motivated feeding behavior.

neuroscience↗

bioRxiv2021

Towards a More General Understanding of the Algorithmic Utility of Recurrent Connections

Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would likely particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such "tag propagation" algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and demonstrate hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to multiple, interacting propagating tags, and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks. Author SummaryIn striking contrast to the majority of current-day artificial neural network research which primarily uses feedforward architectures, biological brains make extensive use of lateral and recurrent connections, raising the possibility that this difference makes a fundamental contribution to the gap in computational power between real neural circuits and artificial neural networks. Thus, despite the challenge of making effective comparisons between different network architectures, developing a more detailed understanding of the computational role played by such connections is a pressing need. Here, we leverage the computational capabilities of large-scale machine learning to robustly explore how differences in architectures affect a networks ability to learn tasks that require propagation of global information. We first focus on the task of determining whether two pixels are connected in an image which has an elegant and efficient recurrent solution: propagate a connected label or tag along paths. Inspired by this solution, we show that it can be generalized in many ways, including propagating multiple tags at once and changing the computation performed on the result of the propagation. Strikingly, this simple expansion of the tag propagation network is sufficient to solve a crucial abstraction to temporal connectedness at the core of many decision-making problems, which we illustrate for a an abstracted competitive foraging task Our results shed light on the set of computational tasks that can be solved efficiently by recurrent computation and how these solutions may relate to the structure of neural activity.

neuroscience↗

bioRxiv2020

Degenerate circuits use distinct mechanisms to respond similarly to the same perturbation

There is considerable flexibility embedded within neural circuits. For example, separate modulatory inputs can differently configure the same underlying circuit but these different configurations generate comparable, or degenerate, activity patterns. However, little is known about whether these mechanistically different circuits in turn exhibit degenerate responses to the same inputs. We examined this issue using the crab (Cancer borealis) stomatogastric nervous system, in which stimulating the modulatory projection neuron MCN1 and bath applying the neuropeptide CabPK II elicit similar gastric mill (chewing) rhythms in the stomatogastric ganglion, despite differentially configuring the same neural circuit. We showed previously that bath applying the peptide hormone CCAP or stimulating the muscle stretch-sensitive sensory neuron GPR during the MCN1-elicited gastric mill rhythm selectively prolongs the protraction or retraction phase, respectively. Here, we found that these two influences on the CabPK-rhythm elicited some unique and unexpected consequences compared to their actions on the MCN1-rhythm. For example, in contrast to its effect on the MCN1-rhythm, CCAP selectively decreased the CabPK-rhythm retraction phase and thus increased the rhythm speed, whereas the CabPK-rhythm response to stimulating GPR during the retraction phase was similar its effect on the MCN1-rhythm (i.e. prolonging retraction). Interestingly, despite the comparable GPR actions on these degenerate rhythms, the underlying synaptic mechanism was distinct. Thus, degenerate circuits do not necessarily exhibit degenerate responses to the same influence, but when they do, it can occur via different underlying mechanisms. Significance StatementCircuits generating seemingly identical behaviors are often thought to arise from identical circuit states, as that is the most parsimonious explanation. Here we take advantage of an alternate scenario wherein a well-defined circuit with known connectivity generates similar activity patterns using distinct circuit states, via known mechanisms. The same peptide hormone modulation of these distinct circuit states produced divergent activity patterns, whereas the same sensory feedback altered these circuit outputs similarly but via different synaptic pathways. The latter observation limits the insights available from comparable studies in systems lacking detailed access to the underlying circuit.

neuroscience↗

bioRxiv2019

Behavioural analysis of single-cell aneural ciliate, Stentor roeselii, using machine learning approaches

There is still a significant gap between our understanding of neural circuits and the behaviours they compute - i.e. the computations performed by these neural networks (Carandini 2012). Learning, behaviour, and memory formation, what used to only be associated with animals with neural systems, have been observed in many unicellular aneural species, namely Physarum, Paramecium, and Stentor (Tang & Marshall 2018). As these are fully functioning organisms, yet being unicellular, there is a much better chance to elucidate the detailed mechanisms underlying these learning processes in these organisms without the complications of highly interconnected neural circuits. An intriguing learning behaviour observed in Stentor roeselii (Jennings 1902) when stimulated with carmine has left scientists puzzled for more than a century. So far, none of the existing learning paradigm can fully encapsulate this particular series of five characteristic avoidant reactions. Although we were able to observe all responses described in literature and in a previous study (Dexter et al. 2019, manuscript in preparation), they do not conform to any particular learning model. We then investigated whether models based on machine learning approaches, including decision tree, random forest, and feed-forward neural networks could infer and predict the behavior of S. roeselii. Our results showed that an artificial neural network with multiple computational neurons is inefficient at modelling the single-celled ciliates avoidant reactions. This has highlighted the complexity of behaviours in aneural organisms. Additionally, this report will also discuss the significance of elucidating molecular details underlying learning and decision-making processes in these unicellular organisms, which could offer valuable insights that are applicable to higher animals.

animal behavior and cognition↗