Nickolaj Ajay Atchuthan

Neuroscience researcher and independent machine-learning developer.

My academic work is basic neuroscience research on cortical processing under general anaesthesia. Alongside it, I build practical machine-learning tools as personal side projects.

PhD Fellow at the Center for Neuroplasticity and Pain, Aalborg University

Nickolaj Ajay Atchuthan overlooking the sandstone landscape in Canyonlands National Park
Canyonlands, Utah

Selected work

Research programme
Oct 2023–Oct 2026

Understanding nociception across brain networks

A large-animal research programme combining graded peripheral stimulation with simultaneous recordings from sensory, prefrontal, and ACC-targeted regions. The programme covers multiregion physiology, an explainable CNN proof of concept, ongoing transformer benchmarking, and evidence-gated anatomical work.

The experiments are conducted under general anaesthesia. They address stimulation-related and nociception-related cortical processing—not conscious pain or a clinically validated biomarker.

Project in VBN 2025 preprint
  • Intracranial electrophysiology
  • Time–frequency analysis
  • Interpretable machine learning
  • Animal-level evaluation

Research infrastructure
Custom PhD tooling

TDT-to-BIDS electrophysiology pipeline

A custom pipeline for recordings acquired with a Tucker-Davis Technologies RZ2, RS4 storage, and Synapse. It converts raw TDT blocks into a structured BIDS-FIF dataset, separates simultaneous S1/PFC and ACC streams, reconstructs stimulation events, and creates analysis-ready epoch derivatives.

The workflow pairs acquisitions by animal, task, and run; fingerprints detector inputs and settings; and records explicit channel and epoch decisions before reviewed derivatives are used downstream.

  • TDT RZ2 / RS4 / Synapse
  • MNE-Python
  • MNE-BIDS
  • Quality-control review

Visiting research
Duke University, 2024

Spinal cord stimulation modelling

At the Grill Lab, I modelled neural-fibre responses to conventional, FAST, and burst spinal cord stimulation using PyFibers and a modified McIntyre-Richardson-Grill axon model. The work examined activation thresholds, firing fidelity, and temporal coding across waveform conditions.

IEEE paper
  • Biophysical modelling
  • PyFibers
  • Axon models
  • Parameter sweeps

Outside the lab

Outside research, I spend time climbing, hiking, and photographing the landscapes I move through.

A sandstone arch framing distant mesas beneath a clear sky
A lone hiker overlooking a canyon landscape at sunset
Sunlight filtering between tall trees onto a forest path

Experience

Apr 2024–present

PhD Fellow, Center for Neuroplasticity and Pain

Aalborg University

Developing methods to study nociception-related cortical processing in a large-animal model through intracranial recordings, physiological analysis, interpretable machine learning, and research software.

Oct 2023–Apr 2024

Research Assistant, Center for Neuroplasticity and Pain

Aalborg University

Worked on explainable convolutional modelling of intracranial neural recordings and on evaluating robust channel-quality methods for surface and depth recordings. This work continued into the PhD project.

Sep–Dec 2024

Visiting Scholar

Grill Lab, Duke University

Conducted computational modelling of neural-fibre responses to clinically relevant spinal cord stimulation waveforms, leading to an IEEE EMBC 2025 paper.

Jan–Jun 2023

Teacher in Business Informatics

TECHCOLLEGE

Designed and taught practical lessons in informatics, technology, and applied problem solving.

Education

2021–2023

MSc in Biomedical Engineering and Informatics

Aalborg University

The programme combined neural engineering, signal processing, and machine learning through semester-long research projects.

  • MSc thesis: CNN-LSTM analysis of S1 µECoG recordings from large-animal chronic-pain models, including high-frequency stimulation, spared nerve injury, and attribution analysis.
  • Resting-state EEG and functional-connectivity modelling of pain-sensitivity groups in collaboration with REDO Neuro Systems.
  • Convolutional modelling of noxious and non-noxious µECoG responses, presented and published at IEEE NER 2023.
  • Spatiotemporal analysis of intracortical microelectrode-array recordings, also published at IEEE NER 2023.

2018–2021

BSc in Biomedical Engineering

Aalborg University

The bachelor project developed modality-matched thermotactile feedback intended to restore temperature sensation for prosthetic users. The team was a finalist in the 2021 Health HUB AAU Innovation Award.

Publications

  1. 2025

    Exploring Tonic and Burst Stimulation in Neural Fibers: A Computational Modeling Approach

    Nickolaj Ajay Atchuthan, Warren M. Grill, and Suzan Meijs · IEEE EMBC 2025

  2. 2025

    Strengths & Weaknesses of RANSAC applied to Epidural & Intracortical Recordings

    Nickolaj Ajay Atchuthan, Felipe Rettore Andreis, Winnie Jensen, and Suzan Meijs · IEEE EMBC 2025

  3. 2025

    A convolutional neural network to distinguish between brain responses to non-noxious and noxious input of the same modality: what does the machine see that we do not see?

    Nickolaj Ajay Atchuthan, Mikkel Bjerre Danyar, Hjalte Færregård Clark, Felipe Rettore Andreis, Winnie Jensen, and Suzan Meijs · Research Square v1 preprint

  4. 2023

    Classification of noxious and non-noxious event-related potentials from S1 in pigs using a convolutional neural network

    Nickolaj Ajay Atchuthan, Hjalte Clark, Mikkel Bjerre Danyar, Amalie Koch Andersen, Felipe Rettore Andreis, and Suzan Meijs · IEEE NER 2023

  5. 2023

    Spatio-Temporal Analysis of LTP-like Neuroplasticity in Pigs

    Mikkel Bjerre Danyar, Hjalte Færregård Clark, Nickolaj Ajay Atchuthan, Louise K. Daugbjerg, Amalie Koch Andersen, Taseer A. M. Janjua, and Winnie Jensen · IEEE NER 2023

Profile

My primary work is basic neuroscience research on stimulation-related and nociception-related cortical responses using sensory, prefrontal, and ACC-targeted recordings in an anaesthetised large-animal model.

I work primarily in Python with MNE-Python, PyTorch, scientific data pipelines, and statistical analysis. Separately, I build implementation-focused machine-learning tools as personal projects, often to solve problems I encounter myself. Beyond the lab, I am interested in consciousness, memory, psychology, open science, climbing, and hiking.

LinkedIn GitHub AAU Research Profile