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Marcus Ghosh @marcusghosh.bsky.social · 9d

For physical systems yes (from hydraulics to computers)!

But, I don't remember seeing an algorithm-centric version?

Marcus Ghosh @marcusghosh.bsky.social · 13d

Are #NeuroAI and #AINeuro equivalent?

@rdgao.bsky.social draws a nice distinction between the two.

And introduces Gao's second law:
“Any state-of-the-art algorithm for analyzing brain signals is, for some time, how the brain works.”

Part 1: www.rdgao.com/blog/2024/01...

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Marcus Ghosh @marcusghosh.bsky.social · 14d

Congratulations!

Exciting to see an inversion of NeuroAI too

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Marcus Ghosh @marcusghosh.bsky.social · 19d

Really neat!

It could be interesting to explore heterogenous neuromodulation?

In this case, having multiple modulator networks which act at different timescales and exert different effects on the downstream SNN?

This would be a bit closer to neuromodulators in vivo.

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Reposted by Marcus Ghosh

Dan Goodman @neural-reckoning.org · 20d

New preprint! What happens if you add neuromodulation to spiking neural networks and let them go wild with it? TLDR: it can improve performance especially in challenging sensory processing tasks. Explainer thread below. 🤖🧠🧪 www.biorxiv.org/content/10.1...

Neuromodulation enhances dynamic sensory processing in spiking neural network models

Neuromodulators allow circuits to dynamically change their biophysical properties in a context-sensitive way. In addition to their role in learning, neuromodulators have been suggested to play a role ...

www.biorxiv.org

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Marcus Ghosh @marcusghosh.bsky.social · 21d

Congratulations!

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Reposted by Marcus Ghosh

Friedemann Zenke @fzenke.bsky.social · 21d

Truly honored (and a little overwhelmed) to see our work featured in The Transmitter's "This Paper Changed My Life." Huge thanks to @neural-reckoning.org for the kind words - and to our amazing community that keeps pushing spiking neural network research forward 🙏

The Transmitter @thetransmitter.bsky.social · 21d

In our latest “This paper changed my life,” @neural-reckoning.org explains how @fzenke.bsky.social's 2019 paper, and its related coding tutorial SpyTorch, made it possible to apply modern machine learning to spiking neural networks.

#neuroskyence

www.thetransmitter.org/this-paper-c...

This paper changed my life: Dan Goodman on a paper that reignited the field of spiking neural networks

Friedemann Zenke’s 2019 paper, and its related coding tutorial SpyTorch, made it possible to apply modern machine learning to spiking neural networks. The innovation reinvigorated the field.

www.thetransmitter.org

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Marcus Ghosh @marcusghosh.bsky.social · 27d

What I was trying to highlight is that it is possible to observe low dimensional structure in population activity which is unrelated to the computation / task.

And it's not obvious that this problem vanishes in more complex circuits?

Marcus Ghosh @marcusghosh.bsky.social · 28d

I think by adding a mixture of noise, delays etc, we may not end up at d=1.

But even if we did, many studies would describe these "population dynamics" as a line attractor.

Rather than just a single neuron acting on it's own?

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Marcus Ghosh @marcusghosh.bsky.social · 28d

Its good to see that there are causal studies.

But, these seem to be the exception?

And perhaps the language used in the non-causal studies can be a bit misleading?

For instance, the post that launched this thread claimed that "the brain uses distributed coding" (based on solely on recordings).

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Marcus Ghosh @marcusghosh.bsky.social · 28d

What I was trying to highlight is that it is possible to observe low dimensional structure in population activity which is unrelated to the computation / task.

And it's not obvious that this problem vanishes in more complex circuits?

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Marcus Ghosh @marcusghosh.bsky.social · 28d

The toy circuit is very simple. But I'd be happy to say it computes?

For example, given LIF dynamics it could perform temporal coincidence detection (only outputting spikes in response to inputs close together in time).

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Marcus Ghosh @marcusghosh.bsky.social · Sep 8

I think by adding a mixture of noise, delays etc, we may not end up at d=1.

But even if we did, many studies would describe these "population dynamics" as a line attractor.

Rather than just a single neuron acting on it's own.

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Marcus Ghosh @marcusghosh.bsky.social · Sep 8

Causal experiments could help to untangle this.

But, it seems like this remains underexplored?

Keen to hear your thoughts @mattperich.bsky.social, @juangallego.bsky.social and others!

www.nature.com/articles/s41...

🧵5/5

A neural manifold view of the brain - Nature Neuroscience

Recent advances in neuroscience have revealed how neural population activity underlying behavior can be well described by topological objects called neural manifolds. Understanding how nature, nurture...

www.nature.com

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Marcus Ghosh @marcusghosh.bsky.social · Sep 8

However,

In this case, from the anatomy (circuit diagram above), we know that only neuron_1 is involved in the computation (transforming the input to the output).

And the manifold we observe is misleading.

🧵4/5

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Marcus Ghosh @marcusghosh.bsky.social · Sep 8

A common approach to analysing this data would be to apply PCA (or another technique).

Yielding a matrix of population activity (d x time). Where d < the number of neurons.

A common interpretation of this would be that "the brain uses a low dimensional manifold to link this input-output".

🧵3/5

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Marcus Ghosh @marcusghosh.bsky.social · Sep 8

If we start from this circuit:

Input -> neuron_1 -> output
↓
⋮
↓
neuron_n

And record neurons 1 to n simultaneously (where n could be very large).

We can obtain a matrix of neural activity (neurons x time).

🧵2/5

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Marcus Ghosh @marcusghosh.bsky.social · Sep 8

The misleading manifold?

The current debate (decoding vs causal relevance)

and a toy example I gave in the thread below

got me thinking about a related issue: how decoding may reflect structure more than function.

🧵 1/5

Marcus Ghosh @marcusghosh.bsky.social · Sep 4

These, and other, studies show that you can decode task-related signals from many brain areas.

But wouldn't we need causal manipulations to conclude that the brain "uses" them?

For example, maybe we can decode equally well from two areas. But, only one impacts behaviour when inactivated.

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Marcus Ghosh @marcusghosh.bsky.social · Sep 8

Okay, let's say we make the circuit above more realistic: add neurons, separate them into areas, consider multiple tasks etc.

The problem outlined above still persists?

If a method doesn't work in a simple system (e.g. the one above), there is no reason to think it will in a more complex one?

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Marcus Ghosh @marcusghosh.bsky.social · Sep 4

But what does the lag tell you about the system?

Taking the circuit above as an example.

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Marcus Ghosh @marcusghosh.bsky.social · Sep 4

There may be a difference in the lag between the input signal and neurons 1 and 2.

But this difference could be too small to be detectable?

And without causal experiments the conclusion could be something like "different neurons represent the stimulus at different timescales"?

🧵 (3/3)

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Marcus Ghosh @marcusghosh.bsky.social · Sep 4

Input -> neuron_1 -> output
↓
neuron_2

In this "circuit":
* We could decode the input from either neuron
* But the circuit is not "using" neuron_2 in the computation (transforming the input to output).
* And ablations would make this clear?

🧵 (2/3)

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Marcus Ghosh @marcusghosh.bsky.social · Sep 4

I agree that perturbations have their challenges (see doi.org/10.1371/jour... from @kayson.bsky.social for a better approach).

But without them you just have correlations?

Just to give a toy example:

🧵 (1/3)

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Reposted by Marcus Ghosh

Rory Byrne @rory.bio · Sep 4

Proud to have been a part of this, a great example of distributed async science!

Huge thanks to @marcusghosh.bsky.social, @neuralreckoning.bsky.social, @tfiers.bsky.social, @krhab.bsky.social and others for putting in the bulk effort 🙌

Dan Goodman @neural-reckoning.org · Sep 4

Is anarchist science possible? As an experiment, we got together a large group of computational neuroscientists from around the world to work on a single project without top down direction. Read on to find out what happened. 🤖🧠🧪

Diagram of how the "collaborative modelling of the brain" (COMOB) project started. Starting material lead to group research or solo research, coming together in online workshops (monthly) in an iterative cycle, finishing with writing up together. The diagram is illustrated with colourful cartoon blob characters.

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Marcus Ghosh @marcusghosh.bsky.social · Sep 4

Being part of this grassroots 🌱 neuroscience collaboration was a great experience!

Keep an eye out for our next collaborative effort

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