🧵(1/n) I'm thrilled to announce that I published my very first! pre-print on BioRxiv. This work was co-supervised by Wilson Truccolo and Timothée Proix @ndlab.bsky.social. www.biorxiv.org/content/10.1... 🧠🧪 #compneuro - ThreadSky

nosrat.bsky.social • 66 days ago

🧵(1/n) I'm thrilled to announce that I published my very first! pre-print on BioRxiv. This work was co-supervised by Wilson Truccolo and Timothée Proix @ndlab.bsky.social.
https://www.biorxiv.org/content/10.1101/2024.12.17.628339v1
🧠🧪 #compneuro

Comments

neuroleyla.bsky.social•66 days ago

Excellent news, congratulations!!!

nosrat.bsky.social•66 days ago

Thank you Leyla 🙏

nosrat.bsky.social•65 days ago

#neuroscience #compneuro #neuroskyence

nosrat.bsky.social•66 days ago

(2/n) We introduced a framework to interpret complex spiking neuronal network dynamics using nonlinear Hawkes process models and diffusion approximations. This approach allows extracting tractable equations, offering dynamical insights into behavior and cognition.

nosrat.bsky.social•66 days ago

(3/n)Understanding neural spiking activity is 🗝️ to decoding how networks drive behavior and cognition. Existing models (like nonlinear Hawkes processes) are:
1-Powerful at capturing the precise spiking patterns.
2-Difficult to interpret due to their stochastic, non-Markovian and discrete nature.

nosrat.bsky.social•66 days ago

(4/n) We approached this challenge in two steps: (step one) Reformulating nonlinear Hawkes processes (NH) as piecewise deterministic Markov processes (NH-PDMPs) that evolves deterministically in-between each jump events (spikes). These models preserve the precise spike timing of neurons.

nosrat.bsky.social•66 days ago

(5/n) (step two) Applying a diffusion approximation, making the dynamics analyzable with tools from nonlinear dynamical systems.
This transform noisy spiking models into interpretable and geometric dynamical state space.

nosrat.bsky.social•66 days ago

(6/n) We tested this approximation extensively and showed that this approximation is reliable within the biological range of neuronal spike rate.
And we are able to generate full bifurcation maps and state spaces for both single neuron and network of neurons.

nosrat.bsky.social•66 days ago

(7/n) We applied this framework on canonical spiking patterns such as tonic/bursting spiking, tonic/bursting phasic spiking and class I and class II neurons, and showed that the dynamical features of the diffusion model fixed points can explain the underlying spiking patterns.

ruchiradatta.bsky.social•66 days ago

Congratulations!

nosrat.bsky.social•66 days ago

Thank you 🙏

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