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Kristine Heiney, PhD

@krisheiney.bsky.social

Computational neuroscientist researching learning and memory

7 Posts104 Follower 171 Folgt

Olivia Guest · Ολίβια Γκεστ @olivia.science · 3mo ago

I wrote a very short piece on models & modelling in part because we need to protect our scientific praxis damage caused by AI in terms of addictive frictionless thinking, esp since it harms society broadly too. "Models" doi.org/10.5281/zeno... Thanks @carvalhais.org for inviting me to write this.

Models in cognitive and related sciences are part of an overarching methodology, computational
or broadly formal modelling, that aims to test, refine, and showcase our understandings of
brain, cognition, and behaviour.1 In their foundational book Models as Mediators, Mary S.
Morgan and Margaret Morrison (1999)2 make the case that models play a mediatory role in
science between what we observe and what we want to build to house our understandings,
theories. Under this conception of models, known as the pragmatic view,3 we can carve out
not only their unique role, but also why and how they must be protected from forces that
do not prize understanding. Taking a broad perspective that sees all human activity that
strives towards understanding as scientific or as under our purview as modellers, allows for
this characterisation of models as mediators to be more generally fruitful.4 In the context of
human-centred design, for example, our theories may be anything from our understanding of
users’ behaviours in a specific context to our understanding of what a user expects from a
product or system a priori.
Importantly, scientific modelling is apart from other scientific praxis, such as concocting
a verbal or formal theory or designing and running experiments.5 Models, as described by
Morrison and Morgan,6 have the following characteristics:
a. construction — models are neither directly derived from theory nor from data, but
through an effortful friction between the modeller and the medium (e.g. their favourite
programming language, mathematics, natural language) through which they are building
their model;7

In the contemporary entanglements with industry that scholarly fields find themselves —
such as with the imposition of gargantuan statistical models, which consume breathtaking
amounts of energy, and which require the building of evermore data-centres to house the glut of
data unavoidably required for their so-called human-like abilities9 — the idea of using models
in such slow10 ways is seemingly out the window. What is worthy of perseverating on in our
specific context is not so much that so-called artificial intelligence (AI) models may harm our
individual practice or displace our labour. They certainly may.11 However, the threat from AI
worthy of highlighting here is that a culture of frictionless practice, which such models (in
name only) promote, is not only harmful to people’s mental health and skils, but also directly
undermines scholarly praxis.12
Avoiding the effortful and difficult process of building, testing, and throwing away models
— be they written in well-crafted code, be they not-yet-fully-formal models, or be they mock-ups
of potential final products — with the goal of deeply understanding our theories about the world
is anti-scientific and anti-scholarly. This spills over into an anti-intellectual stance for a designer
or user of models. Such avoidance is normalised by the addictive convenience culture facilitated
and entrenched by contemporary AI. A culture in which so-called prompting a machine for a
seemingly polished output requires none of the above characteristics of modelling as a process.
Furthermore, presenting anti- or non-scientific modelling endeavours as something other than
what they are, may even veer into pseudo-science and pseudo-technology.13 To add insult
to injury, if junior modellers are not trained to perform this praxis, the end of skills being
inherited by future generations is nigh.
To subvert this, we must preserve the special status of crafting models. I propose this
can be done in a two-pronged manoeuvre: a) rejection …

quantitative predictions of the tides, which have been being refined over the millennia,24 to the
qualitative shift in explanation produced by carving out the concept of gravity. Quantitative
predictions did not pile up to produce our contemporary understanding of gravity.
Protecting modelling praxis may be difficult, but needs must when the devil drives. The
alternative is stifling our knowledge about (our understandings of) the world. And so to
avoid making theorising and modelling seem superfluous, we must remember that scientific
models do not operate like inferential statistical models wherein prediction is the goal. As
expounded on above, models are the instrument through which we perform fully-fledged
scientific reasoning on our theories, and which mediate between theory, formal or otherwise,
connecting it to observations, data, and phenomena, and back again. A model (in name only)
that is built through applying statistics over a dataset, which all contemporary artificial neural
network-based AI models are, will neither function as mediator between data and theory —
it has been contaminated — nor provide qualitative predictions.25 Nor will it serve any user
who seeks to gain insight and skills, nor any designer of systems who wishes for a society
comprising such fellow humans.
Ultimately, “all science would be superfluous if the outward appearance and the essence of
things directly coincided.”26 If we merge essences and appearances in our reasoning, we risk
our understandings of the world. All modelling, all metaphorical, theoretical, and mediatory
thinking, enrich us and our societies and make possible not only valuable understandings of
our own humanity, but constitute our humanity as such

Timothy O'Leary @timothyoleary.bsky.social · 3mo ago

Even if they don't dismiss the evidence of its existence, a big chunk of neuroscientists don't seem to get a key question that representational drift raises. The whole point is that responses reconfigure *without* loosing representational fidelity. This means *of necessity* that any geometric/

Timothy O'Leary @timothyoleary.bsky.social · 4mo ago

Some more Brain-Machine Interface shenanigans: this time we think we found evidence that an animal's agency in a task modulates hippocampal maps of that task. www.biorxiv.org/content/10.6...

biorxiv.org

Drew Schreiner @schreinerdrew.bsky.social · 4mo ago

Where, exactly, does learning happen in the brain? Out today in @nature.com, we identify a synaptic locus of birdsong learning and show that the circuit can be tuned to make birds learn faster - but at a cost. Read on👇 #neuroskyence 🧪 #prattle 💬 #bioacoustics Shareable link: rdcu.be/fiyrS

A synaptic locus of song learning - Nature

Combining a computational framework and optogenetic and chemogenetic manipulations within and downstream of the cortico-basal ganglia circuit identifies the specific cortico-basal ganglia synapse...

nature.com

Paul Graf @paulginva.bsky.social · 7mo ago

Writing forces your brain to coordinate memory, reasoning, and meaning-making simultaneously. Every time you write, you rewire toward clearer thinking. Every time you let an LLM do it, you rewire toward consumption.

This editorial discusses the critical value of human-generated scientific writing in the era of large language models (LLMs), arguing that writing is essential to structured thinking and research comprehension.
Writing as Thinking: The act of writing structure's thoughts, sorting research data, and identifying the main message, unlike LLMs which may lack true understanding or accountability.
LLM Hallucinations: LLM-generated text requires rigorous verification because these models can produce incorrect information or fake references.
Human vs. AI Roles: While LLMs are useful tools for brainstorming, improving grammar, or overcoming writer's block, human researchers must maintain control to engage in the creative task of shaping a compelling narrative.

Max Planck Institute for Biological Cybernetics @mpicybernetics.bsky.social · 5mo ago

🎉 @roxana-zeraati.bsky.social has been awarded the @klaus-tschira-stiftung.de Boost Fund! Moving beyond short-term trials to naturalistic scenarios: Roxana will explore how we decide in changing environments. 🔗 www.kyb.tuebingen.mpg.de/zeraati-tsch... #DecisionMaking #ComputationalNeuroscience

Portrait picture of Roxana Zeraati. Photo: Friedhelm Albrecht / University of Tübingen

Gaute Einevoll @gauteeinevoll.bsky.social · 5mo ago

Episode #39 in #TheoreticalNeurosciencePodcast: On modeling neural population activity with mean-field models – with Tilo Schwalger theoreticalneuroscience.no/thn39 How can mean‑field models be systematically derived from the underlying microscopic dynamics of individual neurons?

PessoaBrain @pessoabrain.bsky.social · 6mo ago

𝗕𝗿𝗮𝗶𝗻 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀 For those in favor or against, it seems like a good one to discuss in the Neuroscience & Philosophy Salon! #neuroskyence doi.org/10.1038/s415...

Alex Williams @itsneuronal.bsky.social · 6mo ago

Cosyne invited me to give a long tutorial (4 hours!) on methods to quantify differences high-d neural recordings across animals, brain regions, deep neural nets, etc. The recording is up on youtube. I hope it inspires more research on this fundamental topic! www.youtube.com/watch?v=n44x...

Cosyne 2026 - Cosyne Tutorial: Comparative Analysis of Neural Population Codes

YouTube video by Cosyne Talks

youtube.com

@in-code.bsky.social · 6mo ago

‪Ever wondered how GABAergic interneurons shape cognition? The IN-CODE consortium's latest NeuroView article introduces a "population approach", shifting the focus from individual interneurons to cooperative networks. Dive into the future of interneuron research here: doi.org/10.1016/j.ne...

Roselyne Chauvin @roselynechauvin.bsky.social · 6mo ago

The 6x US memory champion – Nelson Dellis – can memorize a deck of cards in 40 seconds and knows the first 10K digits of pi. To figure out how, he let us peak inside his brain. Here is what we learned in our precision brain mapping study www.biorxiv.org/content/10.6... youtube.com/shorts/MryMq...

how does his brain do it ? #neuroscience #memory #sport Nelson Dellis 6x US memory champion

YouTube video by Roselyne Chauvin

youtube.com

Kristine Heiney, PhD @krisheiney.bsky.social · 6mo ago

📍🇵🇹 Excited for #cosyne2026! Come see my poster on Saturday if you want to talk drift. My colleagues at @sprekeler.bsky.social's lab also have some great posters coming up: #compneurosky

Renato Duarte @rcfduarte.bsky.social · 6mo ago

I tracked every keyword in 22 years of Cosyne abstracts to map how computational neuroscience evolved — from Bayesian brains to neural manifolds to LLMs — and where it's heading next.

22 years of Brain Science: what CoSyNe tells us about the evolution of Neuroscience

Tracking the intellectual DNA of Computational and Systems Neuroscience through its flagship meeting

open.substack.com

Sam Gershman @gershbrain.bsky.social · 6mo ago

RIP redundancy reduction? Beautiful work by Liu & colleagues showing that neural redundancy increases with learning, as predicted by a Bayesian model: www.science.org/doi/10.1126/...

Task learning increases information redundancy of neural responses in macaque visual cortex

How does the brain optimize sensory information for decision-making in new tasks? One hypothesis suggests that learning reduces redundancy in neural representations to improve efficiency, whereas anot...

science.org

Kristine Heiney, PhD @krisheiney.bsky.social · 6mo ago

Out now in @plos.org CB: doi.org/10.1371/jour... Neural representations of tasks change over time, even in the absence of changes in task performance. But neurons change tuning at different rates. How does a neuron's stability relate to its interactions with the population? #compneuro #neuroskyence

Information theoretic measures of neural and behavioural coupling predict representational drift

Author summary Activity in the brain represents information about the outside world and how we interact with it. Recent evidence shows that these representations slowly change day to day, while memori...

doi.org

Stefano Nichele @stenichele.bsky.social · 6mo ago

Kristine's paper "Information theoretic measures of neural and behavioural coupling predict representational drift" is now available on PLOS Comp. Biology. Link: doi.org/10.1371/jour... This work was led by Kristine (former PhD student) while visiting University of Cambridge during her PhD.

Ann Kennedy @antihebbiann.bsky.social · last yr.

I wrote a Comment on neurotheory, and now you can read it! Some thoughts on where neurotheory has and has not taken root within the neuroscience community, how it has shaped those subfields, and where we theorists might look next for fresh adventures. www.nature.com/articles/s41...

Theoretical neuroscience has room to grow

Nature Reviews Neuroscience - The goal of theoretical neuroscience is to uncover principles of neural computation through careful design and interpretation of mathematical models. Here, I examine...

nature.com

Nicole Rust @nicolecrust.bsky.social · last yr.

Let’s talk @storiesofwin.bsky.social. I’m flattered to be among their profiles (coming soon) & I want to elevate the team behind this terrific effort. /1 www.storiesofwin.org

Stories of WiN

storiesofwin.org