pinotsislab - News #new paperpinotsislab - News #new paperhttps://www.pinotsislab.com/blogs/tag/new-paperWed, 15 Apr 2026 05:41:16 -0700http://zoho.com/sites/<![CDATA[Potential explanation of representational drift]]>https://www.pinotsislab.com/blogs/post/explanation-of-representational-driftThe preprint can be found here . Abstract below. Beyond dimension reduction: Stable electric fields emerge from and allow representational drift It is k ]]>

 Cortical electric fields carry information and are more stable than neural activity. Same field multiple neuron combinations explain representational drift.

The preprint can be found here. Abstract below.


Beyond dimension reduction: Stable electric fields emerge from and allow representational drift


It is known that the exact neurons maintaining a given memory (the neural ensemble) change from trial to trial. This raises the question of how the brain achieves stability in the face of this representational drift. Here, we demonstrate that this stability emerges at the level of the electric fields that arise from neural activity. We show that electric fields carry information about working memory content. The electric fields, in turn, can act as “guard rails” that funnel higher dimensional variable neural activity along stable lower dimensional routes. We obtained the latent space associated with each memory. We then confirmed the stability of the electric field by mapping the latent space to different cortical patches (that comprise a neural ensemble) and reconstructing information flow between patches. Stable electric fields can allow latent states to be transferred between brain areas, in accord with modern engram theory.
]]>Wed, 19 Jan 2022 12:48:05 +0000<![CDATA[Biophysical models provide better biomarkers than EEG]]>https://www.pinotsislab.com/blogs/post/biophysical-models-provide-better-biomarkers-than-eegNew depression preprint from our lab. Biophysical models open the black box. They provide biomarkers that are more informative, interpretable and poin ]]>

New  preprint on depression

New depression preprint from our lab. Biophysical models open the black box. They provide biomarkers that are more informative, interpretable and point to pathologies; EEG biomarkers are less informative and non biophysical.Abstract below, paper here.

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. We constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.
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