Biology subjects

Baccus, S. A.

Publications and source records attributed to Baccus, S. A..

18 recordsLinked to original sources

bioRxiv2026

Parallel construction of object motion from retina to cortex

Detecting a moving object against a moving background is fundamental to survival. The retina is known to perform this computation, extracting object motion from global motion before signals reach the brain. Under the prevailing hierarchical view of vision, downstream areas should inherit and elaborate this retinal computation rather than rebuild it. Using multielectrode recordings of matched stimuli in mouse retina and cortex along with computational modeling, we show instead that post-retinal processing recomputes rather than inherits object motion sensitivity. Retinal and cortical object-motion sensitivities have distinct tuning: the retina prefers fine jitter, whereas cortex prefers coarse drift, revealing a division of labor in which the retina detects moving objects, whereas the cortex conveys information about object pattern. Vision synthesizes object motion through parallel specialization, not hierarchical refinement.

neuroscience↗

bioRxiv2026

Single-camera, calibration-free gaze estimation using corneal reflections

BackgroundMice make substantial eye movements during head-fixed visual stimulation, and uncorrected gaze shifts corrupt receptive field measurements and confound stimulus-response relationships. Corneal-reflection video oculography in rodents has provided the methodological foundation for calibrated angular gaze tracking since Stahl (2004) but the calibration procedures used by existing methods -- physical camera rotation, motorized stages, behavioral tasks, or precisely co-aligned dual cameras -- have limited their adoption in many mouse neuroscience laboratories. Most studies instead use uncalibrated pupil tracking, deep learning pose estimation that returns pixel coordinates without angular calibration, or learned shifter networks that lack independent validation. New methodWe present an open-source corneal-reflection eye tracking system for head-fixed mice with two methodological contributions. First, a geometric model recovers gaze in calibrated angular units from the pixel displacements of the pupil and corneal reflections, using the known 3D positions of multiple fiducial LEDs as the source of angular scale. The model requires no estimate of Rp, the per-animal eye-geometry parameter that earlier corneal-reflection methods determine through physical calibration. Second, a self-calibration procedure exploits the redundancy of multiple stationary fiducial LEDs: each LED produces an independent gaze estimate from the same geometric model, and a single residual calibration parameter is determined by minimizing the disagreement between per-LED estimates. This replaces the physical camera-rotation calibrations of earlier video oculography (Sakatani and Isa, 2004, 2007; van Alphen et al., 2013; Kretschmer et al., 2017), the motorized stages of Zoccolan et al. (2010), and the precision dual-camera alignment of Payne and Raymond (2017) with a software operation that requires no moving parts, no behavioral task, and no per-animal procedure. The system provides three interactive GUI stages: (1) pupil and LED detection via Difference-of-Gaussians filtering, (2) 3D geometry definition, and (3) gaze angle computation with blink detection, fiducial correction, and manual curation. ResultsValidation against a rotary-encoder-controlled artificial eye demonstrated mean absolute errors below 1{degrees} across all four fiducial LEDs over the {+/-}20{degrees} working range of mouse eye movements, with Pearson correlations exceeding 0.998 between our methods estimation and encoder ground truth. The self-calibration reduced inter-LED disagreement by a factor of 4-6 in mouse recordings. Gaze-corrected stimulus reconstruction applied to Neuropixels recordings from mouse V1 produced qualitatively sharper receptive field estimates with improved signal-to-noise ratios. Comparison with existing methodsOur method is the first multi-LED, single-camera, fully software-calibrated corneal-reflection eye tracker for mice and includes an integrated open-source pipeline for detection, calibration, blink handling, and artifact correction. The multi-LED redundancy doubles as an internal consistency check -- if two LEDs disagree on gaze direction, the calibration is wrong -- providing a guarantee that learned approaches relying on neural-data-derived correction cannot offer. ConclusionsOur method makes calibrated corneal-reflection eye tracking accessible to non-specialist mouse laboratories using consumer-grade hardware ([~] $2,000-2,700 USD), eliminates the per-animal calibration procedures of earlier methods, and is validated by two independent ground truths at both the absolute angular (artificial eye) and functional (V1 receptive fields) levels. HighlightsO_LIOpen-source corneal-reflection eye tracking for head-fixed mice using a single camera and multiple stationary fiducial LEDs. C_LIO_LIGeometric gaze model derives angular scale from LED positions, eliminating per-animal eye-geometry calibration. C_LIO_LISelf-calibration via multi-LED redundancy replaces physical camera rotation, motorized stages, and dual-camera precision alignment. C_LIO_LIValidated to sub-degree accuracy against a rotary-encoder ground truth across the {+/-} 20{degrees} range of mouse eye movements. C_LIO_LIGaze correction produces sharper V1 receptive field estimates in Neuropixels recordings. C_LI

neuroscience↗

bioRxiv2025

Genome modeling and design across all domains of life with Evo 2

All of life encodes information with DNA. While tools for sequencing, synthesis, and editing of genomic code have transformed biological research, intelligently composing new biological systems would also require a deep understanding of the immense complexity encoded by genomes. We introduce Evo 2, a biological foundation model trained on 9.3 trillion DNA base pairs from a highly curated genomic atlas spanning all domains of life. We train Evo 2 with 7B and 40B parameters to have an unprecedented 1 million token context window with single-nucleotide resolution. Evo 2 learns from DNA sequence alone to accurately predict the functional impacts of genetic variation--from noncoding pathogenic mutations to clinically significant BRCA1 variants--without task-specific finetuning. Applying mechanistic interpretability analyses, we reveal that Evo 2 autonomously learns a breadth of biological features, including exon-intron boundaries, transcription factor binding sites, protein structural elements, and prophage genomic regions. Beyond its predictive capabilities, Evo 2 generates mitochondrial, prokaryotic, and eukaryotic sequences at genome scale with greater naturalness and coherence than previous methods. Guiding Evo 2 via inference-time search enables controllable generation of epigenomic structure, for which we demonstrate the first inference-time scaling results in biology. We make Evo 2 fully open, including model parameters, training code, inference code, and the OpenGenome2 dataset, to accelerate the exploration and design of biological complexity.

genomics↗

bioRxiv2024

Adaptation of retinal discriminability to natural scenes

Sensory systems discriminate between stimuli to direct behavioral choices, a process governed by two distinct properties - neural sensitivity to specific stimuli, and neural variability that importantly includes correlations between neurons. Two questions that have received extensive investigation and debate are whether visual systems are optimized for natural scenes, and whether correlated neural variability contributes to this optimization. However, the lack of sufficient computational models has made these questions inaccessible in the context of the normal function of the visual system, which is to discriminate between natural stimuli. Here we take a direct approach to analyze discriminability under natural scenes for a population of salamander retinal ganglion cells using a model of the retinal neural code that captures both sensitivity and variability. Using methods of information geometry and generative machine learning, we analyzed the manifolds of natural stimuli and neural responses, finding that discriminability in the ganglion cell population adapts to enhance information transmission about natural scenes, in particular about localized motion. Contrary to previous proposals, correlated noise reduces information transmission and arises simply as a natural consequence of the shared circuitry that generates changing spatiotemporal visual sensitivity. These results address a long-standing debate as to the role of retinal correlations in the encoding of natural stimuli and reveal how the highly nonlinear receptive fields of the retina adapt dynamically to increase information transmission under natural scenes by performing the important ethological function of local motion discrimination.

neuroscience↗

bioRxiv2024

Classification and analysis of retinal interneurons by computational structure under natural scenes

Inhibitory neurons are diverse across the brain, but for the visual system we lack the ability to functionally classify these neurons under complex natural stimuli. Here we take the approach of classifying retinal amacrine cell responses to natural scenes using optical recording and an interpretable neural network model. We fit mouse amacrine cell responses to a two-layer convolutional neural network model of a class shown previously to accurately capture salamander ganglion cell responses to natural scenes. Using an approach from interpretable machine learning, we determined for each stimulus the model interneurons that generated each amacrine response, analogous to the set of bipolar cells that target the amacrine population. From this analysis we clustered amacrine cells not by their natural scene responses, but by the model presynaptic neurons that constructed those responses, conservatively finding approximately seven groups by this approach. By analyzing the set of model presynaptic input neurons for each amacrine cluster, we find that distributed rather than dedicated inputs generate natural scene responses for different amacrine cell types. Additional analyses revealed distinct transient and sustained modes exhibited by the network during the response to simple flashes. These results give insight into the computational structure of how the diverse amacrine cell population responds to natural scenes, and generate multiple quantitative hypotheses for how synaptic inputs generate those responses.

neuroscience↗

bioRxiv2024

Sequence modeling and design from molecular to genome scale with Evo

The genome is a sequence that completely encodes the DNA, RNA, and proteins that orchestrate the function of a whole organism. Advances in machine learning combined with massive datasets of whole genomes could enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on 2.7M prokaryotic and phage genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multiscale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.

synthetic biology↗

bioRxiv2023

Information Geometry of the Retinal Representation Manifold

The ability for the brain to discriminate among visual stimuli is constrained by their retinal representations. Previous studies of visual discriminability have been limited to either low-dimensional artificial stimuli or pure theoretical considerations without a realistic encoding model. Here we propose a novel framework for understanding stimulus discriminability achieved by retinal representations of naturalistic stimuli with the method of information geometry. To model the joint probability distribution of neural responses conditioned on the stimulus, we created a stochastic encoding model of a population of salamander retinal ganglion cells based on a three-layer convolutional neural network model. This model not only accurately captured the mean response to natural scenes but also a variety of second-order statistics. With the model and the proposed theory, we computed the Fisher information metric over stimuli to study the most discriminable stimulus directions. We found that the most discriminable stimulus varied substantially across stimuli, allowing an examination of the relationship between the most discriminable stimulus and the current stimulus. By examining responses generated by the most discriminable stimuli we further found that the most discriminative response mode is often aligned with the most stochastic mode. This finding carries the important implication that under natural scenes, retinal noise correlations are information-limiting rather than increasing information transmission as has been previously speculated. We additionally observed that sensitivity saturates less in the population than for single cells and that as a function of firing rate, Fisher information varies less than sensitivity. We conclude that under natural scenes, population coding benefits from complementary coding and helps to equalize the information carried by different firing rates, which may facilitate decoding of the stimulus under principles of information maximization.

neuroscience↗

bioRxiv2021

A mechanistically interpretable model of the retinal neural code for natural scenes with multiscale adaptive dynamics

The visual system processes stimuli over a wide range of spatiotemporal scales, with individual neurons receiving input from tens of thousands of neurons whose dynamics range from milliseconds to tens of seconds. This poses a challenge to create models that both accurately capture visual computations and are mechanistically interpretable. Here we present a model of salamander retinal ganglion cell spiking responses recorded with a multielectrode array that captures natural scene responses and slow adaptive dynamics. The model consists of a three-layer convolutional neural network (CNN) modified to include local recurrent synaptic dynamics taken from a linear-nonlinear-kinetic (LNK) model [1]. We presented alternating natural scenes and uniform field white noise stimuli designed to engage slow contrast adaptation. To overcome difficulties fitting slow and fast dynamics together, we first optimized all fast spatiotemporal parameters, then separately optimized recurrent slow synaptic parameters. The resulting full model reproduces a wide range of retinal computations and is mechanistically interpretable, having internal units that correspond to retinal interneurons with biophysically modeled synapses. This model allows us to study the contribution of model units to any retinal computation, and examine how long-term adaptation changes the retinal neural code for natural scenes through selective adaptation of retinal pathways.

neuroscience↗

bioRxiv2020

How inhibitory neurons increase information transmission under threshold modulation

Modulation of neuronal thresholds is ubiquitous in the brain. Phenomena such as figure-ground segmentation, motion detection, stimulus anticipation and shifts in attention all involve changes in a neurons threshold based on signals from larger scales than its primary inputs. However, this modulation reduces the accuracy with which neurons can represent their primary inputs, creating a mystery as to why threshold modulation is so widespread in the brain. We find that modulation is less detrimental than other forms of neuronal variability and that its negative effects can be nearly completely eliminated if modulation is applied selectively to sparsely responding neurons in a circuit by inhibitory neurons. We verify these predictions in the retina where we find that inhibitory amacrine cells selectively deliver modulation signals to sparsely responding ganglion cell types. Our findings elucidate the central role that inhibitory neurons play in maximizing information transmission under modulation.

neuroscience↗

bioRxiv2019

Representations of the amacrine cell population underlying retinal motion anticipation

Retinal amacrine cells are a diverse population of inhibitory interneurons, posing a challenge to understand the specific roles of those interneurons in computations of the similarly diverse ganglion cell population. Here we study the predictive computation of motion anticipation, which is thought to compensate for processing delays when encoding moving objects. We recorded the membrane potential of the salamander amacrine cell population optically while recording electrically from ganglion cells with a multielectrode array. We find unexpectedly that ganglion cells with the greatest anticipation for moving stimuli exhibit a new type of predictive motion anticipation that is inconsistent with prior models of delayed inhibition. Based on the spatiotemporal correlations between thousands of amacrine and ganglion cell pairs, we modeled the contribution of the traveling wave of activity for different amacrine cell populations to the encoding of a moving bar. These models indicate that the population responses of slow biphasic amacrine cells create the greatest contribution to both types of ganglion cell motion anticipation, supporting a role for this specific amacrine cell class in the predictive encoding of moving stimuli. Significance StatementThe prediction of moving stimuli is a widespread function occurring in both visual and auditory systems. The diversity of interneuron populations makes it a challenge to understand the mechanisms of these computations. To analyze how the retina anticipates motion, we optically measured inhibitory amacrine cell population activity simultaneously with electrical recording from ganglion cell populations. We then used computational modelling to assess which amacrine cell types have the right spatiotemporal responses to generate motion anticipation. In contrast to previous suggestions of a general role for inhibition, we find that slow biphasic amacrine cells specifically have the greatest contribution to motion anticipation, thus highlighting the need to directly measure and model the effects of interneuron populations in complex sensory computations.

neuroscience↗

bioRxiv2018

Long-range sensitization in the vertebrate retina and human perception

Sensory stimuli have extensive correlations, which should be exploited by an efficient detector of specific sensory features according to theoretical principles. Here we show that visual stimulation far outside the classical receptive field center causes ganglion cells to subsequently increase their sensitivity to local stimuli in a process we term long-range sensitization. This increased sensitivity improves detection and discrimination of weak stimuli and exhibits pattern specificity, increasing sensitivity more when peripheral and central stimuli share spatial frequency statistics. This process requires input from wide-field, nonlinear inhibitory amacrine cells, supporting a simple circuit model that reproduces sensitization. We further show that long-range sensitization parallels a novel perceptual effect in humans, in which surround stimulation subsequently improves discrimination of a small central stimulus. We conclude that the retina uses long-range statistics of the visual world to better encode local visual features, and that such improved encoding may play a role in perception.

neuroscience↗

bioRxiv2018

Deep learning models reveal internal structure and diverse computations in the retina under natural scenes

Understanding how the visual system encodes natural scenes is a fundamental goal of sensory neuroscience. We show here that a three-layer network model predicts the retinal response to natural scenes with an accuracy nearing the fundamental limits of predictability. The models internal structure is interpretable, in that model units are highly correlated with interneurons recorded separately and not used to fit the model. We further show the ethological relevance to natural visual processing of a diverse set of phenomena of complex motion encoding, adaptation and predictive coding. Our analysis uncovers a fast timescale of visual processing that is inaccessible directly from experimental data, showing unexpectedly that ganglion cells signal in distinct modes by rapidly (< 0.1 s) switching their selectivity for direction of motion, orientation, location and the sign of intensity. A new approach that decomposes ganglion cell responses into the contribution of interneurons reveals how the latent effects of parallel retinal circuits generate the response to any possible stimulus. These results reveal extremely flexible and rapid dynamics of the retinal code for natural visual stimuli, explaining the need for a large set of interneuron pathways to generate the dynamic neural code for natural scenes.

neuroscience↗

bioRxiv2018

Diverse nonlinear modulation of visual features by retinal amacrine cells

The prevailing hierarchical view of the visual system consists of parallel circuits that begin in the retina, which then sum effects across sequential levels, increasing in complexity. Yet a separate type of interaction, whereby one visual pattern changes the influence of another, known as modulation, has received much less attention in terms of its circuit mechanisms. Retinal amacrine cells are a diverse class of inhibitory interneurons that are thought to have modulatory effects, but we lack a general understanding of their functional types. Using dynamic causal experiments in the salamander retina perturbing amacrine cells along with an unsupervised computational framework, we find that amacrine cell modulatory effects cluster into two distinct types. One type controls ganglion cell sensitivity to individual visual features, and a second type controls the ganglion cells output gain, acting to gate all features. These results establish three separate general roles of amacrine cells - to generate primary visual features, to use context to select specific visual features and to gate retinal output.

neuroscience↗

bioRxiv2017

Physical mechanisms of ultrasonic neurostimulation of the retina

Focused ultrasound has been shown to be effective at stimulating neurons in vivo, ex vivo and in vitro preparations. Ultrasonic neuromodulation is the only non-invasive method of stimulation that could reach deep in the brain with high spatial-temporal resolution, and thus has potential for use in clinical applications and basic studies of the nervous system. Understanding the physical mechanism by which energy in a high acoustic frequency wave is delivered to stimulate neurons will be important to optimize this technology. Two primary candidates for a physical mechanism are radiation force, the delivery of momentum by the acoustic wave, and cavitation, oscillating gas bubbles. We imaged the isolated salamander retina during ultrasonic stimuli that drive ganglion cell activity and observed micron scale displacements consistent with radiation force. We recorded ganglion cell spiking activity with a planar multielectrode array and changed the acoustic carrier frequency across a broad range (0.5 - 43 MHz), finding that increased stimulation occurs at higher acoustic frequencies, a result that is consistent with radiation force but not cavitation. A quantitative radiation force model can explain retinal responses, and could potentially explain previous in vivo results in the mouse, suggesting a new hypothesis to be tested in vivo. Finally, we found that neural activity was strongly modulated by the distance between the transducer and the electrode array showing the influence of standing waves on the response. We conclude that radiation force is the physical mechanism underlying ultrasonic neurostimulation in the ex vivo retina, and that the control of standing waves is a new potential method to modulate these effects.

neuroscience↗

bioRxiv2017

Synchronous inhibitory pathways create both efficiency and diversity in the retina

Visual information is conveyed from the retina to the brain by a diverse set of retinal ganglion cells. Although they have differing nonlinear properties, nearly all ganglion cell receptive fields on average compute a difference in intensity across space and time using a region known as the classical or linear surround1,2, a property that improves information transmission about natural visual scenes3,4. The spatiotemporal visual features that create this fundamental property have not been quantitatively assigned to specific interneurons. Here we describe a generalizable causal approach using simultaneous intracellular and multielectrode recording to directly measure and manipulate the sensory feature conveyed by a neural pathway to a downstream neuron. Analyzing two inhibitory cell classes, horizontal cells and linear amacrine cells, we find that rather than transmitting different temporal features, the two inhibitory pathways act synchronously to create the salamander ganglion cell surround at different spatial scales. Using these measured visual features and theories of efficient coding, we computed a fitness landscape representing the information transmitted using different weightings of the two inhibitory pathways. This theoretical landscape revealed a ridge that maintains near-optimal information transmission while allowing for receptive field diversity. The ganglion cell population showed a striking match to this prediction, concentrating along this ridge across a wide range of positions using different weightings of amacrine or horizontal cell visual features. These results show how parallel neural pathways synthesize a sensory computation, and why this architecture achieves the potentially competing objectives of high information transmission of individual ganglion cells, and diversity among receptive fields.

neuroscience↗

bioRxiv2017

Two-stage adaptation of inhibition mediates predictive sensitization in the retina

A critical function of the nervous system is the prediction of future sensory input. One such predictive computation is retinal sensitization, a form of short-term plasticity seen in multiple species that elevates local sensitivity following strong local stimulation. Here we perform a causal circuit analysis of retinal sensitization using simultaneous intracellular and multielectrode recording in the salamander. We show, using direct current injection into inhibitory sustained amacrine cells that a decrease in amacrine transmission is necessary, sufficient and occurs at the right time and manner to cause sensitization in ganglion cells. Because of neural dynamics and nonlinear pathways, a computational model is essential to explain how a change in steady inhibitory transmission causes sensitization. Whereas adaptation of excitation removes an expected result in order to transmit novelty, adaptation of inhibition provides a general mechanism to enhance the sensitivity to the sensory feature conveyed by an inhibitory pathway, creating a prediction of future input.

neuroscience↗

bioRxiv2017

Adaptive feature detection from differential processing in parallel retinal pathways

To transmit information efficiently in a changing environment, the retina adapts to visual contrast by adjusting its gain, latency and mean response. Additionally, the temporal frequency selectivity, or bandwidth changes to encode the absolute intensity when the stimulus environment is noisy, and intensity differences when noise is low. We show that the On pathway of On-Off retinal amacrine and ganglion cells is required to change temporal bandwidth but not other adaptive properties. This remarkably specific adaptive mechanism arises from differential effects of contrast on the On and Off pathways. We analyzed a biophysical model fit only to a cell's membrane potential, and verified pharmacologically that it accurately revealed the two pathways. We conclude that changes in bandwidth arise mostly from differences in synaptic threshold in the two pathways, rather than differences in synaptic release dynamics. Different efficient codes are selected by different thresholds in two independently adapting neural pathways.

neuroscience↗

bioRxiv2017

Inferring hidden structure in multilayered neural circuits

A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we develop a computational framework to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. For example, with our regularization framework, we can learn the STA and STC using 5 and 10 minutes of data, respectively, at a level of accuracy that otherwise requires 40 minutes of data without regularization. We apply our framework to retinal ganglion cell processing, learning cascaded linear-nonlinear (LN-LN) models of retinal circuitry, consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits had consistently high thresholds, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model's parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.\n\nAuthor SummaryComputation in neural circuits arises from the cascaded processing of inputs through multiple cell layers. Each of these cell layers performs operations such as filtering and thresholding in order to shape a circuits output. It remains a challenge to describe both the computations and the mechanisms that mediate them given limited data recorded from a neural circuit. A standard approach to describing circuit computation involves building quantitative encoding models that predict the circuit response given its input, but these often fail to map in an interpretable way onto mechanisms within the circuit. In this work, we build two layer linear-nonlinear cascade models (LN-LN) in order to describe how the retinal output is shaped by nonlinear mechanisms in the inner retina. We find that these LN-LN models, fit to ganglion cell recordings alone, identify filters and nonlinearities that are readily mapped onto individual circuit components inside the retina, namely bipolar cells and the bipolar-to-ganglion cell synaptic threshold. This work demonstrates how combining simple prior knowledge of circuit properties with partial experimental recordings of a neural circuits output can yield interpretable models of the entire circuit computation, including parts of the circuit that are hidden or not directly observed in neural recordings.

neuroscience↗