Biology subjects

Roudi, Y.

Publications and source records attributed to Roudi, Y..

8 recordsLinked to original sources

bioRxiv2022

Bayesian interpolation for power laws in neural data analysis

Power laws arise in a variety of phenomena ranging from matter undergoing phase transition to the distribution of word frequencies in the English language. Usually, their presence is only apparent when data is abundant, and accurately determining their exponents often requires even larger amounts of data. As the scale of recordings in neuroscience becomes larger, an increasing number of studies attempt to characterise potential power-law relationships in neural data. In this paper, we aim to discuss the potential pitfalls that one faces in such efforts and to promote a Bayesian interpolation framework for this purpose. We apply this framework to synthetic data and to data from a recent study of large-scale recordings in mouse primary visual cortex (V1), where the exponent of a powerlaw scaling in the data played an important role: its value was argued to determine whether the populations stimulus-response relationship is smooth, and experimental data was provided to confirm that this is indeed so. Our analysis shows that with such data types and sizes as we consider here, the best-fit values found for the parameters of the power law and the uncertainty for these estimates are heavily dependent on the noise model assumed for the estimation, the range of the data chosen, and (with all other things being equal) the particular recordings. It is thus challenging to offer a reliable statement about the exponents of the power law. Our analysis, however, shows that this does not affect the conclusions regarding the smoothness of the population response to low-dimensional stimuli but casts doubt on those to natural images. We discuss the implications of this result for the neural code in the V1 and offer the approach discussed here as a framework that future studies, perhaps exploring larger ranges of data, can employ as their starting point to examine power-law scalings in neural data.

neuroscience↗

bioRxiv2020

Efficiency of local learning rules in threshold-linear associative networks

We show that associative networks of threshold linear units endowed with Hebbian learning can operate closer to the Gardner optimal storage capacity than their binary counterparts and even surpass this bound. This is largely achieved through a sparsification of the retrieved patterns, which we analyze for theoretical and empirical distributions of activity. As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot self-organized Hebbian learning can be just as efficient.

neuroscience↗

bioRxiv2020

Task-dependent mixed selectivity in the subiculum

CA1 and subiculum (SUB) connect the hippocampus to numerous output regions. Cells in both areas have place-specific firing fields, although they are more dispersed in SUB. Weak responses to head direction and running speed have been reported in both regions. However, how such information is encoded in CA1 and SUB, and the resulting impact on downstream targets, is poorly understood. Here we estimate the tuning of simultaneously recorded CA1 and SUB cells to position, head direction, and speed. Individual neurons respond conjunctively to these covariates in both regions but the degree of mixed representation is stronger in SUB, and more so during goal-directed spatial navigation than free foraging. Each navigational variable could be decoded with higher precision, from a similar number of neurons, in SUB than CA1. The findings point to a possible contribution of mixed-selective coding in SUB to efficient transmission of hippocampal representations to widespread brain regions.

neuroscience↗

bioRxiv2019

Hippocampal spike-time correlations and place-field overlaps during open field foraging

Phase precessing place cells encode spatial information on fine timescales via the timing of their spikes. This phase code has been extensively studied on linear tracks and for short runs in the open field. However, less is known about the phase code on unconstrained trajectories lasting tens of minutes, typical of open field foraging. In previous work (Monsalve-Mercado and Leibold, 2017), an analytic expression was derived for the spike-time cross-correlation between phase precessing place cells during natural foraging in the open field. This expression makes two predictions on how this phase code differs from the linear track case: cross-correlations are symmetric with respect to time, and they represent the distance between pairs of place fields in that the theta-filtered cross-correlations around zero time-lag are positive for cells with nearby fields while they are negative for those with fields further apart. Here we analyze several available open field recordings and show that these predictions hold for pairs of CA1 place cells. We also show that the relationship remains during remapping in CA1, and it is also present in place cells in area CA3. For CA1 place cells of Fmr1-null mice, which exhibit normal place fields but somewhat weaker temporal coordination with respect to theta compared to wild type, the cross-correlations still remain symmetric but the relationship to place field overlap is largely lost. The relationship discussed here describes how spatial information is communicated by place cells to downstream areas in a finer theta-timescale, relevant for learning and memory formation in behavioural tasks lasting tens of minutes in the open field.

neuroscience↗

bioRxiv2019

Action representation in the mouse parieto-frontal network

The posterior parietal cortex (PPC), along with anatomically linked frontal areas, form a cortical network which mediates several functions that support goal-directed behavior, including sensorimotor transformations and decision making. In primates, this network also links performed and observed actions via mirror neurons, which fire both when an individual performs an action and when they observe the same action performed by a conspecific. Mirror neurons are thought to be important for social learning and imitation, but it is not known whether mirror-like neurons occur in similar networks in other species that can learn socially, such as rodents. We therefore imaged Ca2+ responses in large neural ensembles in PPC and secondary motor cortex (M2) while mice performed and observed several actions in pellet reaching and wheel running tasks. In all animals, we found spatially overlapping neural ensembles in PPC and M2 that robustly encoded a variety of naturalistic behaviors, and that subsets of cells could stably encode multiple actions. However, neural responses to the same set of observed actions were absent in both brain areas, and across animals. Statistical modeling analyses also showed that performed actions, especially those that were task-specific, outperformed observed actions in predicting neural responses. Overall, these findings show that performed and observed actions do not drive the same cells in the parieto-frontal network in mice, and suggest that sensorimotor mirroring in the mammalian cortex may have evolved more recently, and only in certain species.

neuroscience↗

bioRxiv2018

Maximum-entropy and representative samples of neuronal activity: a dilemma

The present work shows that the maximum-entropy method can be applied to a sample of neuronal recordings along two different routes: (1) apply to the sample; or (2) apply to a larger, unsampled neuronal population from which the sample is drawn, and then marginalize to the sample. These two routes give inequivalent results. The second route can be further generalized to the case where the size of the larger population is unknown. Which route should be chosen? Some arguments are presented in favour of the second. This work also presents and discusses probability formulae that relate states of knowledge about a population and its samples, and that may be useful for sampling problems in neuroscience.

neuroscience↗

bioRxiv2018

Finding informative neurons in the brain using Multi-Scale Relevance

We propose a metric - called Multi-Scale Relevance (MSR) - to score neurons for their prominence in encoding for the animals behaviour that is being observed in a multi-electrode array recording experiment. The MSR assumes that relevant neurons exhibit a wide variability in their dynamical state, in response to the external stimulus, across different time scales. It is a non-parametric, fully featureless indicator, in that it uses only the time stamps of the firing activity, without resorting to any a priori covariate or invoking any specific tuning curve for neural activity. We test the method on data from freely moving rodents, where we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded.

neuroscience↗

bioRxiv2017

Grid cells show field-to-field variability and this explains the aperiodic response of inhibitory interneurons

Research on network mechanisms and coding properties of grid cells assume that the firing rate of a grid cell in each of its fields is the same. Furthermore, proposed network models predict spatial regularities in the firing of inhibitory interneurons that are inconsistent with experimental data. In this paper, by analyzing the response of grid cells recorded from rats during free navigation, we first show that there are strong variations in the mean firing rate of the fields of individual grid cells and thus show that the data is inconsistent with the theoretical models that predict similar peak magnitudes. We then build a two population excitatory-inhibitory network model in which sparse spatially selective input to the excitatory cells, presumed to arise from e.g. salient external stimuli, hippocampus or a combination of both, leads to the variability in the firing field amplitudes of grid cells. We show that, when combined with appropriate connectivity between the excitatory and inhibitory neurons, the variability in the firing field amplitudes of grid cells results in inhibitory neurons that do not exhibit regular spatial firing, consistent with experimental data. Finally, we show that even if the spatial positions of the fields are maintained, variations in the firing rates of the fields of grid cells are enough to cause remapping of hippocampal cells.

neuroscience↗