Thread by @marinopagan

🚨🚨🚨#TWEEPRINT TIME🚨🚨🚨
A big mystery in brain research is what are the neural mechanisms that drive individual differences in higher order cognitive processes. Here we present a new theoretical framework w/ @brody_lab @mikio_aoi @SussilloDavid @ValerioMante @jpillowtime
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https://twitter.com/biorxiv_neursci/status/1597444451449856001

First, we trained rats to perform flexible evidence accumulation (like the monkeys in Mante et al 2013). Rats were presented with a train of auditory pulses, and were cued to selectively accumulate location (ignoring frequency) or to accumulate frequency (ignoring location).
2/16

Using an automated, high-throughput training procedure we trained 20 rats to solve the task with high performance, collecting more than 120,000 trials for each rat! While rats performed the task, we recorded neural activity in frontal cortex to study population activity.
3/16

First, was neural activity in rats similar to monkeys, where irrelevant evidence was not gated before reaching the Frontal Eye Field? The neural trajectories we observed in rat Frontal Orienting Fields were strikingly similar 🤯, leading us to the same conclusion!
4/16

Then, we wanted to generalize this result, and study the entire space of possible network solutions, so we turned to math! 🤓 Following the footsteps of @SussilloDavid and @ValerioMante, we studied the properties of linearized system dynamics around fixed points.
5/16

Rearranging the formula for differential evidence integration revealed that any network solving the task uses a combination of three distinct components, i.e. under assumptions supported by both monkey and rat data, a triangle constitutes the space of all possible solutions.
6/16

Can we tell apart different mechanisms using neural data collected during the task? 🤔Yes!!! We developed a new analysis that leverages the high statistical power provided by our pulsatile stimulus to retrieve population trajectories evoked by single pulses of evidence!
7/16

We then developed a new method to engineer recurrent neural networks (RNNs) to implement different mechanisms, and we applied the analysis to their dynamics as they solved the task. The pulse-evoked trajectories clearly distinguish RNNs using different mechanisms.
8/16

Why did we develop a new method to build RNNs, you might ask, instead of simply training RNNs using backprop and studying those solutions? It turns out that RNNs trained using backprop only find a small subset of the larger space of solutions!
9/16

Ok, now that we have validated our analysis using RNNs, let’s apply it to data from rat brains! 🧠The result? Different individual rats display different pulse-evoked neural trajectories, very similar to those produced by RNNs implementing different mechanisms!🤯
10/16

Our theory also predicts a specific behavioral “fingerprint”. The context-dependent behavioral kernels should be a reflection on the time axis of the pulse responses. When measuring these behavioral kernels using logistic regression, that’s exactly what we observe in RNNs.
11/16

Remarkably, it works out also in rats! The variability of neural pulse-evoked trajectories is highly correlated with the variability of behavioral kernels, strongly suggesting that both measurements reflect the individual variability of the underlying mechanisms!
12/16

In conclusion, our results provide a new experimentally-supported theoretical framework to analyze biological & artificial systems performing flexible decision-making, opening the door to the study of individual variability in neural computations underlying higher cognition
13/16

Many thanks to @brody_lab for providing a wonderful environment to pursue this research. And thanks to Vincent Tang, @mikio_aoi @SussilloDavid @ValerioMante @jpillowtime for a super-fun collaboration! Thanks also to @SCglobalbrain and @SFARIorg for supporting my research!
14/16

I am super excited about two future directions for this research. First, I will use high-density probes to record large neural populations across brain areas as rats solve the task, and to directly study recurrent dynamics using latent-based approaches (e.g. LFADS + PLNDE).
15/16

Second, with the support of a @SFARIorg Bridge to Independence Award, I am extremely excited to use this task, and these systems and computational approaches to study the role of genetic mutations in cognitive flexibility in rat models of autism! sfari.org/2021/08/19/sfa…
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Oh no, I can't believe I only thanked @brody_lab for the environment! I also want to thank my wonderful Mentor, Carlos Brody himself!!! We worked on a lot of this together, and it was super-fun to do so! Carlos was an inexhaustible source of guidance, wisdom and support! 🙏 🙏 🙏