Biology subjects

Moosavi, S. A.

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

4 recordsLinked to original sources

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2024

Population coding under the scale-invariance of high-dimensional noise

High-dimensional scale-invariant neural activity is ubiquitous across brain regions and species, but its implications for information coding remain unclear. Here, we ask how stimulus information in the high-dimensional activity of mouse V1 scales with neuron number: does it saturate due to noise correlations, or increase without bound as subpopulations grow? Contrary to previous reports, we find that leading noise components that scale linearly with population size, and thus can limit information, are not sufficiently aligned with the signal to impose a bound. This conclusion follows from two scale-invariant power-law properties of neuronal responses in mouse V1: the noise eigenspectrum and alignment of noise components with the signal. We show that population subsampling links the observed power-law exponents to information boundedness, and that information scaling depends on the full eigenspectrum rather than its leading modes. Finally, we prove that, under subsampling, information-limiting correlations, if present, are differential correlations. Our findings clarify how information scales in high-dimensional neuronal activity under scale-invariant noise. TeaserScale-invariant noise in mouse V1 does not limit stimulus information as population size increases.

neuroscience↗

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.

neuroscience↗