🚨🚨🚨NEW PREPRINT🚨🚨🚨
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 and experimental framework, in collaboration with Vincent Tang, Mikio Aoi, Jonathan Pillow, Valerio Mante, @SussilloDavid and Carlos Brody.
https://www.biorxiv.org/content/10.1101/2022.11.28.518207v1
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First, we trained rats to perform a task requiring context-dependent evidence accumulation towards a decision (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).
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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 responses 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 Mante et al., 2013, we studied the properties of linearized system dynamics around fixed points.
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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.
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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!
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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.
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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!
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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.
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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!
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In conclusion, our results provide a new experimentally-supported theoretical framework to analyze biological and artificial systems performing flexible decision-making, opening the door to the study of individual variability in neural computations underlying higher cognition!
13/16
First of all, I can't thank enough my wonderful Mentor, Carlos Brody, for being an inexhaustible source of guidance, wisdom and support throughout this project! 🙏 🙏 🙏
Thanks also to Vincent Tang, Mikio Aoi, @SussilloDavid , Valerio Mante and Jonathan Pillow for a super-fun collaboration!
Finally, thanks to the Simons Collaboration on the Global Brain (SCGB) and the Simons Foundation Autism Research Initiative (SFARI) for supporting my research!
14/16
We are super excited about two future directions for this research.
First, we plan to use high-density probes to record large neural populations across brain areas as rats solve the task, and to directly study recurrent dynamics in simultaneously recorded neural populations using latent-based approaches (e.g. LFADS and PLNDE).
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Second, with the support of a SFARI 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!
https://sfari.org/2021/08/19/sfari-2021-bridge-to-independence-fellows-announced/
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Marino Pagan