Biology subjects

Ringach, D.

Publications and source records attributed to Ringach, D..

14 recordsLinked to original sources

bioRxiv2026

Tuning Diversity Improves Discrimination and Detection Performance under Metabolic Constraints

Cortical populations exhibit a wide range of tuning properties, raising the question of whether such variability is a feature or a bug of cortical function. Prior work has shown that tuning diversity can improve population codes by mitigating the effects of correlated noise and increasing the discrimination and identification capacity of geometric representations. Motivated by these findings, we study a model in which a heterogeneous family of tuning curves, coding for a circular variable, is replicated at equally spaced preferred angles. We show that this heterogeneous population achieves better discrimination and detection than an equally sized homogeneous population constructed from shifted copies of the familys mean tuning curve, while using the same spike budget. Thus, homogeneous tuning is unstable under perturbations that preserve the mean tuning curve, because such perturbations leave metabolic cost unchanged while improving coding performance. We propose that such instability creates evolutionary pressure toward heterogeneity of tuning, making its prevalence a consequence of a process that optimizes coding performance under metabolic constraints. Significance StatementThe tuning curves of cortical neurons vary substantially, raising the question of whether this diversity has functional significance or merely reflects biological noise. We show that perturbing an initially homogeneous population while preserving its mean tuning curve produces a heterogeneous population with better discrimination and detection performance at the same metabolic cost. Thus, tuning diversity may emerge as a natural consequence of selection for efficient coding under metabolic constraints.

neuroscience↗

bioRxiv2025

Contrast and pattern adaptation in visual cortex share a common gain control mechanism

Neuronal populations in primary visual cortex adjust their responses to the statistical structure of the environment, including both stimulus contrast and the probability of occurrence of visual patterns. Here we show that, across a wide range of adaptation states, the distribution of population responses is well described by a zero-inflated log-normal model with three parameters: the probability of "silence" P0, the log-response mean {micro}, and its variance{sigma} 2. Adaptation produces coordinated changes in P0 and {micro}, whereas{sigma} 2 remains approximately invariant. These coordinated shifts collapse the family of response distributions onto a one-dimensional manifold, consistent with the existence of a common gain mechanism underlying both contrast and pattern adaptation. We further demonstrate that {micro} obeys power-law relationships with stimulus contrast and with orientation probability, and that P0 varies linearly with {micro}. Finally, we show that these empirical relations arise naturally in a population of linear-nonlinear neurons driven by Gaussian inputs whose mean, but not variance, is modulated by the environment. Together, these results suggest that contrast and pattern adaptation rely on a shared mechanism that adjusts the mean input to cortical populations while preserving the overall structure of their response distribution. NEW & NOTEWORTHYThis study demonstrates that contrast and pattern adaptation in V1 reshape population activity through a common gain mechanism. Despite large changes in responsiveness, the variance of log responses remains invariant, and shifts in mean activity are captured by a simple change in mean input to a population of linear-nonlinear neurons. The proposed mechanism links classic intracellular findings with population-level response distributions.

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bioRxiv2025

Subspace reverse-correlation estimation of receptive fields during free viewing

Accurately mapping receptive fields under naturalistic viewing requires correcting for distortions introduced by eye movements. Current approaches often rely on parameterized "shifter networks" optimized with Poisson GLMs, but these methods are computationally intensive and limited to small regions of interest. We present a new framework that exploits the translation properties of Hartley basis functions to model eye position effects directly as phase shifts, eliminating the need for explicit retinal image reconstruction. This formulation admits closed-form gradients, enabling efficient parameter optimization and rapid convergence. Validation on simulated data shows that the method accurately recovers both ground-truth receptive fields and the underlying image transformation. Applied to high-density mouse V1 recordings, the approach improves receptive field sharpness by an average of 56% compared to naive estimates, with optimization completing in minutes on a standard desktop computer. While the method is specific to Hartley basis stimuli, once calibrated, it provides a reusable mapping between eye position and retinal translation. This efficiency and scalability make the technique a practical tool for receptive field mapping in free-viewing experiments and for integration with optical imaging and large-scale electrophysiology.

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bioRxiv2025

Establishing a continuum of cell types in the visual cortex

The mammalian cerebral cortex is composed of neurons whose properties vary in a continuous fashion rather than falling into discrete cell types. In the mouse visual cortex, excitatory neurons in layer 2 and 3 (L2/3) form such a continuum along cortical depth, patterned by the graded expression of hundreds of genes. Here we sought to understand how this continuum develops and contributes to cortical wiring. Using single-nucleus multiomics (RNA- and ATAC-Seq) and spatial transcriptomics, we show that the L2/3 continuum is established in two phases. During the first postnatal week, a genetically hardwired program establishes a primitive continuum of cell identities spanning the depth of L2/3. The second program, promoted by visual experience, is later superimposed upon the preexisting continuum. This second phase is driven by activity-regulated transcription factors that drive the L2/3 depth-dependent expression of genes linked to synaptic function and plasticity. We show that neurons at different positions along the L2/3 continuum project preferentially to distinct higher visual areas and that visual deprivation disrupts targeting to some higher visual areas while sparing others. Thus, cortical continua emerge through a stepwise process in which genetic programs and sensory experience specify neuronal identity and sculpt intracortical wiring specificity.

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bioRxiv2025

A universal power law optimizes energy and representation fidelity in visual adaptation

Sensory systems continuously adapt their responses based on the probability of encountering a given stimulus. In the mouse primary visual cortex (V1), the population response magnitude is a power law of the stimulus probability in the environment. For a given stimulus type (e.g., oriented gratings), the power law's exponent is invariant to changes in statistical environments, enabling predictions of population responses to new environments. Here, we aim to provide a normative explanation for the power law behavior. We develop an efficient coding model where neurons adjust their firing rates through optimization of a weighted objective, hypothesizing that the neural population adapts to enhance stimulus detection and discrimination while reducing overall neural activity. We show that a model balancing representational fidelity and energy efficiency matches the power law observed experimentally for a wide range of parameters, while models of adaptation with alternative coding objectives and resource constraints are unable to reproduce this empirical observation. Furthermore, we account for the invariance of the power law's exponent across environmental changes by linking it to the dependence of tuning curve modulation on stimulus probability. Finally, we explain how variations in the exponent with different stimulus types (e.g., natural stimuli) result from changes in the minimal distances between neural representations, in agreement with experimental findings. We conclude that a universal power law of adaptation can be explained as a trade-off between representation fidelity and energy cost.

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bioRxiv2024

Temporal dynamics of energy-efficient coding in mouse primary visual cortex

Sparse coding enables cortical populations to represent sensory inputs efficiently, yet its temporal dynamics remain poorly understood. Consistent with theoretical predictions, we show that stimulus onset triggers broad cortical activation, initially reducing sparseness and increasing mutual information. Subsequently, competitive interactions sustain mutual information as activity declines and sparseness increases. Notably, coding efficiency, defined as the ratio of mutual information to metabolic cost, progressively increases, demonstrating the dynamic optimization of sensory representations.

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bioRxiv2024

Contrast gain control is a reparameterization of a population response curve

Neurons in primary visual cortex (area V1) adapt in different degrees to the average contrast of the environment, suggesting that the representation of visual stimuli may interact with the state of cortical gain control in complex ways. To investigate this possibility, we measured and analyzed the responses of neural populations to visual stimuli as a function of contrast in different environments, each characterized by a unique distribution of contrast. Our findings reveal that, for a given stimulus, the population response can be described by a vector function r(gec), where the gain ge is a decreasing function of the mean contrast of the environment. Thus, gain control can be viewed as a reparameterization of a population response curve, which is invariant across environments. Different stimuli are mapped to distinct curves, all originating from a common origin, corresponding to a zero-contrast response. Altogether, our findings provide a straightforward, geometric interpretation of contrast gain control at the population level and show that changes in gain are well coordinated among members of a neural population.

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bioRxiv2023

On the contrast response function of adapted neural populations

The magnitude of neural responses in sensory cortex depends on the intensity of a stimulus and its probability of being observed within the environment. How these two variables combine to influence the overall response of cortical populations remains unknown. Here we show that, in primary visual cortex, the vector magnitude of the population response is described by a separable power-law that factors the intensity of a stimulus and its probability.

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bioRxiv2023

A power law of cortical adaptation

How do neural populations adapt to the time-varying statistics of sensory input? To investigate, we measured the activity of neurons in primary visual cortex adapted to different environments, each associated with a distinct probability distribution over a stimulus set. Within each environment, a stimulus sequence was generated by independently sampling form its distribution. We find that two properties of adaptation capture how the population responses to a given stimulus, viewed as vectors, are linked across environments. First, the ratio between the response magnitudes is a power law of the ratio between the stimulus probabilities. Second, the response directions are largely invariant. These rules can be used to predict how cortical populations adapt to novel, sensory environments. Finally, we show how the power law enables the cortex to preferentially signal unexpected stimuli and to adjust the metabolic cost of its sensory representation to the entropy of the environment.

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bioRxiv2022

Thalamocortical boutons cluster by ON/OFF responses in mouse primary visual cortex

In higher mammals, the thalamic afferents to primary visual cortex cluster according to their responses to increases (ON) or decreases (OFF) in luminance. This feature of thalamocortical wiring is thought to create columnar, ON/OFF domains in V1. We have recently shown that mice also have ON/OFF cortical domains, but the organization of their thalamic afferents remains unknown. Here we measured the visual responses of thalamocortical boutons using two-photon imaging and found they also cluster in space according to ON/OFF responses. Moreover, fluctuations in the relative density of ON/OFF boutons mirrors fluctuations in the relative density of ON/OFF receptive field positions on the visual field. These findings indicate that there is a segregation of ON/OFF signals already present in the thalamic input, a result that may reflect the spatial organization of retinal ganglion cell mosaics themselves. New and NoteworthyNeurons in primary visual cortex cluster into ON and OFF domains, which have shown to be linked to the organization of receptive fields and cortical maps. Here we show that in the mouse, such clustering is already present in the thalamic input, suggesting that the cortical architecture is shaped by the periphery.

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bioRxiv2021

ON/OFF domains shape receptive fields in mouse visual cortex

In higher mammals, thalamic afferents to primary visual cortex (area V1) segregate according to their responses to increases (ON) or decreases (OFF) in luminance1-4. This organization induces columnar, ON/OFF domains postulated to provide a scaffold for the emergence of orientation tuning2,5-15. To further test this idea, we asked whether ON/OFF domains exist in mouse V1 - a species containing orientation tuned, simple cells, like those found in other mammals16-19. Here we show that mouse V1 is indeed parceled into ON/OFF domains. Revealingly, fluctuations in the relative density ON/OFF neurons on the cortical surface mirror fluctuations in the relative density of ON/OFF receptive field centers on the visual field. In each cortical volume examined, the average of simple-cell receptive fields correlates with the difference between the average of ON and OFF receptive fields7. Moreover, the local diversity of simple-cell receptive fields is explained by a model in which neurons linearly combine a small number of ON and OFF signals available in their cortical neighborhoods15,20. Altogether, these findings indicate that ON/OFF domains originate in fluctuations of the spatial density of ON/OFF inputs on the visual field which, in turn, shapes the structure of receptive fields10-13,21-23.

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bioRxiv2018

On the Subspace Invariance of Population Responses

The response of a neural population in cat primary visual cortex to the linear combination of two sinusoidal gratings (a plaid) can be well approximated by a weighted sum of the population responses to the individual gratings - a property we refer to as subspace invariance. We tested subspace invariance in mouse primary visual cortex by measuring the angle between the population response to a plaid and the plane spanned by the population responses to its individual components. We found robust violations of subspace invariance, represented by a median angular deviation of ~55 deg. The cause of this departure is a strong, negative correlation between the mean responses a neuron to the individual gratings and its response to the plaid. We suggest that an early nonlinearity may distort the power distribution of grating and plaid stimuli such that plaids have a prominent power component at {+/-}45 deg off the fundamental orientations. We conclude that subspace invariance does not hold in mouse V1. This finding rules out a large class of possible models of population coding, including vector averaging and gain control.

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bioRxiv2018

Active Learning of Cortical Connectivity from Two-Photon Imaging Data

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network.. Unlike existing system identification techniques, this \"active learning\" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

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bioRxiv2017

Community-based benchmarking improves spike inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations1, 2. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators3. Different algorithms for estimating spike trains from noisy calcium measurements have been proposed in the past4-8, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

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