Biology subjects

Dougherty, M. E.

Publications and source records attributed to Dougherty, M. E..

3 recordsLinked to original sources

bioRxiv2022

Not optimal, just noisy: the geometry of correlated variability leads to highly suboptimal sensory coding

The brain represents the world through the activity of neural populations. Correlated variability across simultaneously recorded neurons (noise correlations) has been observed across cortical areas and experimental paradigms. Many studies have shown that correlated variability improves stimulus coding compared to a null model with no correlations. However, such results do not shed light on whether neural populations correlated variability achieves optimal coding. Here, we assess optimality of noise correlations in diverse datasets by developing two novel null models each with a unique biological interpretation: a uniform correlations null model and a factor analysis null model. We show that across datasets, the correlated variability in neural populations leads to highly suboptimal coding performance according to these null models. We demonstrate that biological constraints prevent many subsets of the neural populations from achieving optimality according to these null models, and that subselecting based on biological criteria leaves coding performance suboptimal. Finally, we show that the optimal subpopulation is exponentially small as a function of neural dimensionality. Together, these results show that the geometry of correlated variability leads to highly suboptimal sensory coding.

neuroscience↗

bioRxiv2021

Columnar localization and laminar origin of cortical surface electrical potentials

Electrocorticography (ECoG) methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. To address this gap, we recorded from rat auditory cortex using customized ECoG, and simulated cortical surface electrical potentials with a full-scale, biophysically detailed cortical column model. Experimentally, ECoG-derived auditory representations were tonotopically organized and signals were anisotropically localized to [≤]{+/-}200 m, i.e., a single cortical column. Biophysical simulations reproduce experimental findings, and indicate that neurons in cortical layers V and VI contribute [~]85% of evoked high-gamma signal recorded at the surface. Cell number and synchronicity were the primary biophysical properties determining laminar contributions to evoked ECoG signals, while distance was only a minimal factor. Thus, evoked ECoG signals primarily originate from neurons in the infragranular layers of a single cortical column. In BriefBaratham et al., investigated the localization and origins of sensory evoked ECoG responses. They experimentally found that ECoG responses were anisotropically localized [≤]{+/-}200 m, i.e., a single cortical column. Biophysically detailed simulations revealed that neurons in layers V &VI were the primary sources of evoked ECoG responses, in contrast to common thinking. HighlightsEvoked ECoG signals are localized on the surface to a cortical column. Neurons in cortical layers V and VI constitute the vast majority of the signal recorded at the surface. Different laminar contributions to ECoG signal are driven by cell density and synchronicity.

neuroscience↗

bioRxiv2020

Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery

A central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations and their modulation by external factors using high-dimensional and stochastic neural recordings. Statistical models, particularly parametric models, play an instrumental role in accomplishing this goal, because their fitted parameters can provide insight into the underlying biological processes that generated the data. However, extracting conclusions from a parametric model requires that it is fit using an inference procedure capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. Recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation. Here, we used the Union of Intersections, a statistical inference framework capable of state-of-the-art selection and estimation performance, to fit functional coupling, encoding, and decoding models across a battery of neural datasets. We found that, compared to baseline procedures, UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance across recording modality, brain region, and task. Specifically, we obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that rely on fewer single-units. Together, these results demonstrate that accurate parameter inference reshapes interpretation in diverse neuroscience contexts. The ubiquity of model-based data-driven discovery in biology suggests that analogous results would be seen in other fields.

neuroscience↗