Study reveals how brain develops reliable visual perception

Very excited to share our latest publication from the Fitzpatrick Lab at MPFI, in collaboration with our brilliant friends at FIAS! In this work, we combine novel experimental observations with computational modeling to investigate how feedforward and recurrent circuits contribute to the maturation of reliable visual representations in the mammalian cerebral cortex. https://lnkd.in/ehDaUWuM Development of coherent cortical responses reflects increased discriminability of feedforward inputs and their alignment with recurrent circuits Augusto Abel Lempel, Sigrid Trägenap, Clara T., Matthias Kaschube, David Fitzpatrick

A recent Nature study (published July 23, 2025) reveals that hippocampal spatial representations evolve—even in strictly controlled and unchanging sensory environments. Utilizing a highly reproducible multisensory virtual-reality setup, researchers observed that representational drift persists regardless of sensory or behavioral consistency, indicating drift is not merely a response to external variation . Most notably, the study identifies neuronal excitability as a key predictor: place cells with higher excitability exhibit greater long-term stability, while less excitable cells display more drift over days . This finding underscores the role of intrinsic neuronal properties—in particular cell excitability—in maintaining stable cognitive maps over time. These insights deepen our understanding of memory encoding and spatial navigation, suggesting that neural systems balance stability and flexibility through internal dynamics. Implications extend to domains such as learning, memory resilience, and computational modeling of neural circuits. This work opens promising avenues for exploring how to harness or support neuronal excitability to sustain cognitive mapping and memory precision. #health #balance #wellness #cognitive

Just published in #IEEE TNSRE A new study introduces a recurrent neural network–based method for blind source separation of event-related potentials (ERPs). This advancement provides researchers with more accurate tools for #ERP analysis, with potential applications in both neuroscience and rehabilitation engineering. If you’re considering applying this method to your ERP data, the authors welcome your questions and collaboration. Read the article here: https://bit.ly/41GukFE #NeuralEngineering #RehabilitationResearch #MachineLearning #BiomedicalEngineering

A new method has been developed to detect causality in neuronal spike train data, addressing limitations of traditional approaches that require regular sampling and linearity. By reconstructing state spaces from interspike intervals and establishing temporal correspondence between time series, this technique enables direct analysis of causal relationships in complex, nonlinear neural systems. Tested on mathematical neuron models, the method accurately identified various types of connectivity, even under noisy conditions. Beyond neuroscience, these findings may inform causality detection in other fields with similar point process data, such as finance and seismology.

Exciting findings from a recent study in PLoS Computational Biology shed light on the brain's remarkable adaptability. The research highlights how the interplay of inhibitory mechanisms, balancing slow (theta) and fast (gamma) rhythms, allows the brain to navigate various sources of information. This includes processing sensory inputs from the external environment and recalling stored experiences from memory. Explore more about the intricate dynamics of feedforward and feedback inhibition in shaping theta-gamma cross-frequency interactions within neural circuits in the full article: [The role of feedforward and feedback inhibition in modulating theta-gamma cross-frequency interactions in neural circuits | PLOS Computational Biology](https://lnkd.in/dE-pgSxb)

3. Neuroscience and Learning as Electrical Power • Case Study: Hebbian Learning (“neurons that fire together, wire together”) • Each synapse is like a “book” in the brain, storing knowledge. • The force: electrochemical interaction across synapses. • The power: rate of neural firing and cognitive effort applied during learning. • Infant learning shows how alphabet and numbers act as cosmic codes building the architecture of knowledge.

This study led by Oscar Herreras at the Cajal Institute demonstrated that irregular brain field potentials contain distinct, source-specific temporal signatures that can be identified using machine learning. To achieve this, the team used NeuroNexus linear silicon probes with 16 or 32 recording sites (50–100 µm spacing) to record high-resolution field potentials across multiple brain regions in anesthetized rats. These recordings enabled the separation of overlapping signals via independent component analysis, revealing unique dynamics for each neural generator. Read “Brain sources composing irregular field potentials have unique temporal signatures” here: https://hubs.li/Q03FtS9d0 #NeuroNexus #Neuralprobes #siliconprobes #brainresearch

Our new paper is now published in Science Advances! “Bioadaptive Liquid-Infused Multifunctional Fibers for Long-Term Neural Recording via BDNF Stabilization and Enhanced Neural Interaction” (DOI: 10.1126/sciadv.adz1228) introduces TAB coating – a dual-functional surface that blocks nonspecific adhesion while promoting neural interaction. This breakthrough enables stable, high-quality neural recordings for over 12 months, addressing a key challenge in long-term #BCI applications. Grateful to co–first authors Dr. Taeyoung Kim & Ms. Yeonzu Son, and to Prof. Tae Kyung Lee, Prof. Justin J. Chung, Prof. Seo Jung Kim for their contributions — and special thanks to Prof. Seongjun Park (SNU) for leading the neural electrode platform development. #BCI #Neurotechnology #BrainMachineInterface #ScienceAdvances #NeuralRecording