Sean's website

Papers and whatnot

View My GitHub Profile

Escola Lab

Sean Escola

I am an assistant professor of psychiatry in the Center for Theoretical Neuroscience at Columbia University. I also am co-founder of Herophilus, a drug discovery company, and Neuromatch, an online neuroscience community.

Current lab members

Sean Escola (PI)
Andrew Chen (postdoc and resident in psychiatry)
Kaushik Lakshminarasimhan (postdoc)
Jack Lindsey (joint graduate student with Ashok Litwin-Kumar)
Salomon Muller (postdoc)
Yuriy Shymkiv (postdoc)
Thia Steinhardt (postdoc)

Former lab memebers

James Murray (former postdoc, now assistant professor at the University of Oregon)
Laureline Logiaco (former postdoc, now a postdoc at MIT in Ila Fiete’s lab)
Francisco Salema Oom de Sacadura (former rotation student, now a graduate student at Columbia in Mark Churchland’s lab)

Now hiring postdocs!

There are two new postdoc positions in the lab. Postdocs are funded for two years with an expectation of continued funding assuming research progress. In addition to working with me, postdocs will be part of the Center for Theoretical Neuroscience and free to work with other faculty in the Center. Email me for more information.


How do humans and other animals generate sequences of behaviors?

To investigate this, we have developed a model of motor cortex, the basal ganglia, and thalamus which has offered insights into the computational roles that each of these structures play in sequence generation. Specifically, we show that the activation and inactivation of different cortical-thalamic loops by the basal ganglia can control the dynamics of motor cortical activity in order to produce multiple behavioral outputs when the projection to the spinal cord is fixed. This is a novel hypothesis for the role of the neuroanatomy in motor computation.

  1. Laureline Logiaco, Larry Abbott, Sean Escola, “Thalamic control of cortical dynamics in a model of flexible motor sequencing”, Cell Reports, 2021
  2. Laureline Logiaco, Sean Escola, “Thalamocortical motor circuit insights for more robust hierarchical control of complex sequences”, arXiv, 2020

How does the motor system contend with the contraints place upon by biologically realistic learning?

We are applying biologically constrained learning rules to the thalamo-cortical weights while constraining intracortical, readout, and cortico-thalamic weights to be fixed. We show that learning is much more successful when cortico-thalamic weights match the readout, a testable hypothesis. Furthermore, when we restrict the readout as arising from a subpopulation of the cortical network (analogous to cortical layers 5 and 6 in vivo), we find that learning at synapses between thalamus and the non-readout projecting cortical units is no longer possible unless the intracortical connectivity obeys a specific structure. This work unifies neuroanatomy, biological learning, and computational goals to make specific predictions about motor system synaptic weight structures.

  1. Kaushik Lakshminarasimhan, Marjorie Xie, Jeremy Cohen, Britton Sauerbrei, Adam Hantman, Ashok Litwin-Kumar, Sean Escola, “Specific connectivity optimizes learning in thalamocortical loops”, bioRxiv, 2022

How is control of behavior transferred from motor cortex to subcortical circuits over the course of learning complex behaviors?

In collaboration with Dr. Bence Ölveczky’s lab at Harvard, we have been building models to understand the result that motor cortex is necessary for the learning but not for the execution of complex behavior, while thalamus and striatum are necessary for both learning and execution. We have shown that if the connections between cortex and striatum learn relatively quickly using a supervised or reinforcement learning rule, while those between thalamus and striatum learn relatively slowly using a simple Hebbian learning rule, then the thalamic inputs to striatum will learn to mimic the cortical inputs during repetition of the same behavior. This results in transfer of control from cortex to thalamus. Recently, Dr. Ölveczky and colleagues have shown that when animals learn multiple similar complex behaviors, transfer of control from motor cortex to thalamus is no longer successful. Our current modeling efforts show that there is an intrinsic tradeoff in motor learning between flexibility and robustness and that interference of control transfer occurs when, for a given task, flexibility is prioritied.

  1. Kevin Mizes, Jack Lindsey, Sean Escola, Bence Ölveczky, “Similar striatal activity exerts different control over automatic and flexible motor sequences”, bioRxiv, 2021
  2. James Murray, Sean Escola, “Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways”, Nature Communication, 2020
  3. James Murray, Sean Escola, “Learning multiple variable-speed sequences in striatum via cortical tutoring”, eLife, 2017

How can neuroscience and AI inform each other?

Historically, neuroscience has played a large influence on the development of AI, mostly famously by inspiring the core architectures of the neural networks that underpin the ongoing machine learning revolution. We believe that there is much more that biologically intelligent systems can teach AI and that a path towards elicidating this is through the development of virutal embodied agents that are trained to accurately recapitulate the detailed behaviors of in vivo animals. This line of thinking is well captured by a 1988 quote from AI pioneer Hans Moravec, who said that abstract thought “is a new trick, perhaps less than 100 thousand years old…effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge.” To this end, we are developing a platform for virtual rodent neuroscience that will allow us to carefully and exhaustively probe the mechanisms by which animal brains produce ethologically adaptive behaviors.

Moreover, this platform also serves to advance basic questions in neuroscience by allowing for virtual experiments to be performed that can compliment ones in the lab. A cycle of virtual neuroscience predictions, experimental testing, and updated models can ultimately result in a robust testbed in which high fidelity experiments can be conducted at scale to vastly accelerate the pace of neuroscience research.

  1. Anthony Zador, Sean Escola, Blake Richards, Bence Ölveczky, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick, Dmitri Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad Körding, Alexei Koulakov, Yann LeCun, Timothy Lillicrap, Adam Marblestone, Bruno Olshausen, Alexandre Pouget, Cristina Savin, Terrence Sejnowski, Eero Simoncelli, Sara Solla, David Sussillo, Andreas S. Tolias, Doris Tsao, “Catalyzing next-generation Artificial Intelligence through NeuroAI”, Nature Communications, 2023

Can switching between brain states with different state-specific computational modes be detected in large-scale neural recording data in the absence of experimentally observable external cues?

Historically, neural recordings are analyzed by aligning data to experimentally known times (e.g., stimulus onset, movement onset, etc.). However, given that motivational and attentional features are not clearly accessible behaviorally, and that computations may have variable durations from trial to trial, it is possible that there exist multiple internally relevant times that strongly influence neural responses. We seek to develop tools to infer these internally relevant times and thus potentially provide more complete characterizations of neural responses.

  1. Sean Escola, Alfredo Fontanini, Don Katz, Liam Paninski, “Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems”, Neural Computation, 2011
  2. Sean Escola, Michael Eisele, Kenneth Miller, Liam Paninski, “Maximally Reliable Markov Chains Under Energy Constraints”, Neural Computation, 2009

Can we reach outside of the doors of academia for a potentially broader societal benefit?

I have recently been involved in the founding and development of two external organizations with this as part of their missions. Herophilus, Inc., a for-profit drug development entity, applies systems neuroscience and machine learning to patient-derived human cerebral organoids for the discovery of novel disease phenotypes and drug treatments for complex neuropsychiatric disorders. This effort has led to multiple biological and technological advances thus far. Neuromatch, Inc., founded in response to the Covid pandemic, is a not-for-profit conference and summer school organization that bring high-quality zero-cost access to neuroscience education to students globally. In 2020, our summer school enrolled 2000 students; in 2021, 4000 students.

  1. Alex Rogozhnikov, Pavan Ramkumar, Rishi Bedi, Saul Kato, Sean Escola, “Hierarchical confounder discovery in the experiment–machine learning cycle”, Cell Patterns, 2022
  2. Alex Rogozhnikov, Pavan Ramkumar, Kevan Shah, Rishi Bedi, Saul Kato, Sean Escola, “Demuxalot: scaled up genetic demultiplexing for single-cell sequencing”, bioRxiv, 2021
  3. Tara van Viegen, Athena Akrami, Kate Bonnen, Eric DeWitt, Alexandre Hyafil, Helena Ledmyr, Grace W Lindsay, …, Sean Escola, Megan AK Peters, “Neuromatch Academy: Teaching Computational Neuroscience with Global Accessibility”, Trends in Cognitive Sciences, 2021
  4. Kevan Shah, Rishi Bedi, Alex Rogozhnikov, Pavan Ramkumar, Zhixiang Tong, Brian Rash, Morgan Stanton, Jordan Sorokin, …, Gaia Skibinski, Saul Kato, Sean Escola, “Optimization and scaling of patient-derived brain organoids uncovers deep phenotypes of disease”, bioRxiv, 2020

Other publications

For a complete list, visit my Google Scholar Profile