Ann Kennedy

@antihebbiann.bsky.social

Theoretical neuroscientist interested in brain-body interactions and evolution of adaptive behavior. Associate Professor at Scripps Research Institute in San Diego.

Preprint time! This is a cool one. Complex systems, be they neural nets, interacting genes, or power grids, can produce diverse dynamics-- things like point attractors, limit cycles, and chaos. The dynamic landscape you get depends on how elements interact. But those interactions can also change!

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Preprint time! This is a cool one. Complex systems, be they neural nets, interacting genes, or power grids, can produce diverse dynamics-- things like point attractors, limit cycles, and chaos. The dynamic landscape you get depends on how elements interact. But those interactions can also change!

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First off, these cells are gigantic. This one, for example, has >72 cm of axon! To our knowledge, it’s the longest neuron ever fully reconstructed. I leave it to the reader as an exercise to estimate the length of a human LC neuron. 5

The complete morphology of a single LC-NE neuron.

Approximating the Lorenz attractor with a chaotic leaky water wheel. The orange dot is the centre of mass of the water. It traces (a projection of) the familiar attractor as the wheel spins seemingly randomly left and right.

New preprint by @shtakasu.bsky.social , Gast & @antihebbiann.bsky.social, showing that local exc/inh connectivity balance can strongly reshape #RecurrentNetwork dynamics, even when the connectivity spectrum remains unchanged. #CompNeuro #TheoreticalNeuroscience #RandomNetworks #PopulationDynamics

FIG. 1. Numerical simulations illustrating how local connectivity balance affects network dynamics.
Ann Kennedy@antihebbiann.bsky.social · 3w ago

New preprint on our favorite theme: same connectome + different neurons = different dynamics. @shtakasu.bsky.social looked at detail balance (E+I inputs sum to 0) in RNNs w/different activation functions, finding that it stabilized some networks and destabilized others. arxiv.org/html/2608.30...

Behold the future of research code from the GPT-6 press release: a notebook that writes an accessory script by string concatenation and inspecting code in a notebook, evals the script in place, and then ignores it entirely to write a hardcoded summary.

an ad-hoc "save data" script in a jupyter notebook that constructs a python module by constructing a string from a literal, catting the inspected source from live functions to it, calling eval, and then writing that out to a separate script? despite the fact that.... we are in a jupyter notebook??? and we can just.... write python??? and run it???

New preprint led by @rgast.bsky.social- part 1 of Richard's ongoing effort to make a better firing rate model: developing mean-field models that can be fit to biological data to capture both the dynamics of neural populations and the underlying physiological heterogeneity that sculpt those dynamics.

Richard Gast@rgast.bsky.social · 2mo ago

The Ott-Antonsen ansatz revolutionized our understanding of coupled oscillator systems with heterogeneous oscillators. We developed a multi-ensemble method that increases the applicability of the OA ansatz to empirical data substantially arxiv.org/abs/2607.09516, as we demonstrate on neural data.