Merav's Pic

Research Interests
Neural Networks
Modeling & Analysis
Neural Encoding
In the Olfactory System
& Cortical Areas
Machine Learning
Algorithm Developing & Data Analysis

Ph.D. with Honor
In Neural Computation,
The Hebrew University of Jerusalem.
In collaboration with
The Center for Theoretical Neuroscience,
Columbia University in High Energy Physics
The Hebrew University of Jerusalem.
Visiting student at
University of Amsterdam. in Mathematics & Physics,
Einstein Institute of Mathematics &
Racah Institute of Physics
The Hebrew University of Jerusalem.

Website design: Merav Stern and Elran Bor

Merav Stern

Theoretical Neuroscience Research


Happy 2021!
Filled with hope to meet in person.

Image By Marylka Yoe Uusisaari

Numerical Cognition Based on Precise Counting with a Single Spiking Neuron - Read the paper

Rapp, H., Nawrot, M. P., Stern, M.

We show that estimation of numerosity can be realized and learned by a single spiking neuron with an appropriate synaptic plasticity rule. We propose that using action potentials to represent basic numerical concepts with a single spiking neuron is beneficial for organisms with small brains and limited neuronal resources.

A Transformation from Latency to Ensemble Coding in a Model of Piriform Cortex - Read the paper

Stern, M., Abbott, L.F., Franks K.,

Our model demonstrates how piriform cortex circuitry can implement multiplexed ensemble-identity/temporal-concentration odor coding.

Numerical Functional Plasticity of Odor Representations During Motherhood - Read the paper

Vinograd A.*,Fuchs-Shlomai Y.*,Stern M.,Mukherjee D.,Gao Y., Citri A.,Davison I.,Mizrahi A.

Motherhood is associated with changes in neural circuits that affect how the mother senses her surroundings. We show that durimg motherhood neurons of the bulb have elevated inhibition, and odor coding of natural odors is improved.

Transition to Chaos in Random Networks with Cell-Type-Specific Connectivity - Read the paper

Aljadeff, J.*, Stern, M.*, Sharpee, T.

We study dynamics of neural networks with cell-type specific connectivity by extending the dynamic mean field method.

Dynamics of Random Neural Networks with Bistable Units - Read the paper

Stern, M., Sompolinsky H., Abbott L.F.

We construct and analyze a rate-based neural network model in which self-interacting units represent clusters of neurons with strong local connectivity. Simulation results, mean-field calculations, and stability analysis reveal an interesting dynamical regime exhibiting transient but long-lived chaotic activity that combines features of chaotic and multiple fixed-point attractors.

Inferring Neural Population Spiking Rate from Wide-Field Calcium Imaging - Read the paper
Stern, M., Shea-Brown E. T., Witten D.

We develop and test novel methods to deconvolve the calcium traces and reveal the underlying neural spiking rate.

Plume Dynamics Structure the Spatiotemporal Activity of Glomerular Networks in the Mouse Olfactory Bulb - Read the paper
Lewis S.M., Xu L., Rigolli N. , Tariq M., Stern, M., Seminara A., Gire D.H.

We precisely track plume dynamics and image glomerular responses to this fluctuating input. We find that a consistent portion of Mitral cell activity in glomeruli follows odor concentration dynamics,and the strongest responding glomeruli are the best at following fluctuations within odor plumes.

Network Dynamics Governed by Lyapunov Functions: From Memory to Classification - Read the paper
Stern, M., Shea-Brown, E.

We review the Hopfield model (1982) in light of Krotov & Hopfield (2019). The Hopfield model (1982) has proved to be one of the most influential theoretical models in neuroscience. At its core lies the idea that a pattern of sustained neural activity can represent a memory.

Eigenvalues of Block Structured Asymmetric Random Matrices - Read the paper
Aljadeff J.*, Renfrew D., Stern M.*

We study the spectrum of an asymmetric random matrix with block structured variances.

Fundamental Strings and Higher Derivative Corrections to d-Dimensional Black Holes. - Read the paper
Giveon A., Gorbonos D., (Leading Author) Stern M.

Code & Notes

My code for inferring neural population spiking rate from wide-field calcium imaging

The code can be found on github. It includes all the methods that appear in my paper as well as additional detailed explanations about the implementation, running times and more.

Notes on Chaos in Random Neural Networks, Including their Lyaponuv Exponents

The notes are the details behind the paper "Chaos in Random Neural Networks" by Sompolinsky et al. 88'
They are based class notes by Larry Abbott (equations 1-36)
I extended them to include the second half of the paper (equations 37-end)the notes

  • Mentor at Alpha project HU, for excellent high school students in their research program towards final exam equivalent eligibility.
  • Co-mentor of various Ph.D. students from Oregon University (Physics) Columbia University (Brain Science) and University of Washington (Psychology).
  • Mentor at OIST computational neuroscience course, Okinawa, and at IBRO-Simons Computational Neuroscience Imbizo, Cape Town, a few weeks summer schools for graduate students.
  • Teacher assistant, Computational Neuroscience class, Columbia University.
  • Lecturer, Jerusalem College of Engineering.
  • Teacher assistant, Mechanics and Electricity for MD students, HU.
  • Teacher at Yoel Program. The program supports high school drops-out towards GED.
  • Teacher at The New York Academy of Sciences, Education and Public Programs
  • Supervisor and program content developer, the Youth Science Center, HU.
  • Program director, New Jersey "Y" camps
  • Coacher, international figure skating