We are developing a theory of near electric fields in the brain—fields located close to the cortical surface and generated by Hebbian cell assemblies. Our goal is to enhance brain–computer interfaces (BCIs) by understanding how these near fields shift and how they interact with individual neurons [1]–[5].

Current neuromodulation approaches in BCI primarily rely on macroscale electric fields, often applied from outside the brain. These fields are spatially broad and lack the specificity needed to capture fine-grained neural dynamics. While some invasive BCI devices can interact with near fields from within the brain, they frequently cause inflammation and tissue damage.

In collaboration with MIT and Johns Hopkins labs, we are investigating a new class of invasive BCI devices that have been safely tested in animal models [6]. These devices do not damage brain tissue and are capable of recording microscopic neural activity, including signals from individual neurons. However, focusing solely on single-cell recordings is insufficient. It is analogous to trying to understand a symphony by listening to only one violin. The brain, like an orchestra, requires simultaneous observation of many interacting elements. Capturing neural activity effectively therefore demands recordings across populations of neurons.

 Our approach addresses this limitation by proposing a broader theoretical framework. It characterizes brain activity at the microscale while incorporating interactions across larger groups of cells, as well as the brain’s underlying biochemical processes. The BCI systems we aim to develop will combine wide spatial coverage with microscale resolution.

Specifically, we seek to establish a theory of near electric fields at the mesoscale. This includes explaining how such fields emerge not only from neuronal activity but also from cytoskeletal components such as filaments and proteins [6]. We will further investigate how these fields can be leveraged in invasive BCI applications, including their role in altered brain states such as depression, their relevance for deep brain stimulation (DBS), and their potential as biomarkers [7]. Additionally, this framework may  explain intertrial variability in neural activity [8].

Listen to our Clubhouse talk.

References

[1] Pinotsis, D.A., Brincat, S.L., and Miller, E.K. (2017). On memories, neural ensembles and mental flexibility. NeuroImage 157, 297–313.

[3] Pinotsis, D.A., and Miller, E.K. (2022). Beyond dimension reduction: Stable electric fields emerge from and allow representational drift. NeuroImage 253, 119058.
[4] Pinotsis, D., and Miller, E.K. (2023). In vivo ephaptic coupling allows memory network formation. Cerebral Cortex, 2023, 1–19 https://doi.org/10.1093/cercor/bhad251

[5] Pinotsis, D. A., Fridman, G., & Miller, E. K. (2023). Cytoelectric Coupling: Electric fields sculpt neural activity and “tune” the brain’s infrastructure.Progress in Neurobiology, 226,102465

[6] Aplin, F.P., and Fridman, G.Y. (2019). Implantable direct current neural modulation: theory,feasibility, and efficacy. Frontiers in neuroscience 13, 379.

[7] Pinotsis, D.A.,  Alagapan,S., Sarikhani,P.,  Nauvel,T.,   Rozell C.J.,  and  Mayberg, H.S. (2026) Ephaptic coupling and power fluctuations in depression, Cerebral Cortex,  36, 3.   

[8] Pinotsis, D., and Miller, E.K. (preprint). Ephaptic coupling   can explain variability in neural activity.