Alexei Koulakov

Koulakov earned an Engineer-physicist degree (equivalent to an MS) in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology in 1990. From 1988 to 1993, he worked as an Engineer in the Theoretical Department at the Institute of Nuclear Energy in Troitsk, Moscow region. He then pursued a PhD in Theoretical Physics at the University of Minnesota, completing it in 1998. Following his doctoral studies, he engaged in a postdoctoral fellowship in Neuroscience in the Sloan Center for Theoretical Neurobiology at the Salk Institute for Biological Studies in La Jolla, California, where he conducted research from 1998 to 2001.{{cite web|url=https://commonfund.nih.gov/TRA/recipients18|title=NIH Director's Transformative Research Award - 2018 Awardees}}{{cite web|url=https://physics.mst.edu/media/academic/physics/documents/newsletter/feb02_page11.pdf|title= Frontiers in Physics Colloquium Series - February 2002}}

Koulakov joined the Department of Physics at the University of Utah as an assistant professor in 2001. In 2003, he moved to Cold Spring Harbor Laboratory as an assistant professor, became an associate professor in 2008, and then was promoted to professor in 2012. He held that role until 2021, when he became the Charles Robertson Professor.

Koulakov has served as the director of the Swartz Center for Computational Neuroscience at Cold Spring Harbor Laboratory.{{cite web|url=https://www.cshl.edu/research/neuroscience/swartz-foundation/#faculty|title=Swartz Foundation}} He has organized workshops, including the Cosyne workshop in March 2018, which focused on exploring causal links between neural responses and behavior, and another, on computational olfaction, in March 2005 at Snowbird, Utah.{{cite web|url=https://www.cshl.edu/labdish/if-you-thought-all-neuroscientists-work-with-neurons-youre-wrong/|title=If you thought all neuroscientists work with neurons, you're wrong}}

Koulakov is most known for his contributions to condensed matter physics, computational neuroscience, brain evolution, neural development, olfactory coding, neural stem cells, machine learning, and artificial intelligence. He and his team have studied neural computation, brain development and evolution, including how neurons process information, how brain networks assemble during development, and how brain architecture evolved to facilitate its function.{{cite web|url=https://facultyprofiles.cshl.edu/alexei.koulakov/grants|title=Alexei Koulakov - Faculty Profile - RESEARCH INTERESTS}}{{cite web|url=https://scholar.google.com/citations?user=vh5Mh8UAAAAJ&hl=en&oi=ao|title=Alexei Koulakov - Google Scholar}}

Koulakov and his colleagues developed a predictive quantitative model of neural development that integrated the effects of molecular guidance cues and activity-dependent neural plasticity (learning). This model explained the effects of genetic, surgical, and pharmacological manipulations on neural connectivity across species. Refined for the visual system, it predicted connectivity changes in genetically modified animals and was generalizable to other forms of connectivity.{{cite journal|title=Competition is a driving force in topographic mapping|date=2011 |pmc=3223436 |last1=Triplett |first1=J. W. |last2=Pfeiffenberger |first2=C. |last3=Yamada |first3=J. |author4=Stafford BK |last5=Sweeney |first5=N. T. |last6=Litke |first6=A. M. |last7=Sher |first7=A. |last8=Koulakov |first8=A. A. |last9=Feldheim |first9=D. A. |journal=Proceedings of the National Academy of Sciences of the United States of America |volume=108 |issue=47 |pages=19060–19065 |doi=10.1073/pnas.1102834108 |doi-access=free |pmid=22065784 |bibcode=2011PNAS..10819060T }}{{cite journal|url=https://pubmed.ncbi.nlm.nih.gov/24329485/|title=The molecular basis for the development of neural maps|date=2013 |pmid=24329485 |last1=Wei |first1=Y. |last2=Tsigankov |first2=D. |last3=Koulakov |first3=A. |journal=Annals of the New York Academy of Sciences |volume=1305 |issue=1 |pages=44–60 |doi=10.1111/nyas.12324 |bibcode=2013NYASA1305...44W }}

Koulakov's research has focused on quantitative models of brain evolution, including theoretical models of cortical maps,{{cite biorxiv |title=Encoding innate ability through a genomic bottleneck|date=2022 |biorxiv=10.1101/2021.03.16.435261 |last1=Koulakov |first1=Alexei |last2=Shuvaev |first2=Sergey |last3=Lachi |first3=Divyansha |last4=Zador |first4=Anthony }}{{cite journal|title=Mathematical Model of Evolution of Brain Parcellation|date=2016 |doi=10.3389/fncir.2016.00043 |doi-access=free |last1=Ferrante |first1=Daniel D. |last2=Wei |first2=Yi |last3=Koulakov |first3=Alexei A. |journal=Frontiers in Neural Circuits |volume=10 |page=43 |pmid=27378859 |pmc=4909755 }} the development of cortical areas and their connectivity, and the influence of the genomic bottleneck on cortical network evolution.{{cite journal|url=https://pubmed.ncbi.nlm.nih.gov/15217337/|title=Maps in the brain: what can we learn from them?|date=2004 |pmid=15217337 |last1=Chklovskii |first1=D. B. |last2=Koulakov |first2=A. A. |journal=Annual Review of Neuroscience |volume=27 |pages=369–392 |doi=10.1146/annurev.neuro.27.070203.144226 }}{{cite journal|url=https://www.sciencedirect.com/science/article/pii/S0896627301002239|title=Orientation Preference Patterns in Mammalian Visual Cortex: A Wire Length Minimization Approach|journal=Neuron |date=February 2001 |volume=29 |issue=2 |pages=519–527 |doi=10.1016/S0896-6273(01)00223-9 |last1=Koulakov |first1=Alexei A. |last2=Chklovskii |first2=Dmitri B. |pmid=11239440 }}

Examining the decline in hippocampal neurogenesis with age, Koulakov's quantitative analysis revealed that this decline was due to the depletion of the neural stem cell pool, which occurred as these cells were activated to generate new neurons. Activated stem cells underwent asymmetric divisions to produce neurons and eventually matured into astrocytes. His team proposed a "disposable stem cell" model which explains the age-related decrease in stem cells as resulting from production of new neurons.{{cite journal|title=Spatial geometry of stem cell proliferation in the adult hippocampus|date=2018 |doi=10.1038/s41598-018-21078-6 |last1=Mineyeva |first1=Olga A. |last2=Enikolopov |first2=Grigori |last3=Koulakov |first3=Alexei A. |journal=Scientific Reports |volume=8 |issue=1 |page=3444 |pmid=29467395 |pmc=5821870 |bibcode=2018NatSR...8.3444M }}{{cite journal|title=Division-coupled astrocytic differentiation and age-related depletion of neural stem cells in the adult hippocampus|date=2011 |pmid=21549330 |last1=Encinas |first1=J. M. |last2=Michurina |first2=T. V. |last3=Peunova |first3=N. |last4=Park |first4=J. H. |last5=Tordo |first5=J. |last6=Peterson |first6=D. A. |last7=Fishell |first7=G. |last8=Koulakov |first8=A. |last9=Enikolopov |first9=G. |journal=Cell Stem Cell |volume=8 |issue=5 |pages=566–579 |doi=10.1016/j.stem.2011.03.010 |pmc=3286186 }}

In collaboration with his colleagues, Koulakov devised theories for olfactory coding that helped build an understanding of olfactory information processing. These included theories for temporal processing in the early olfactory system,{{cite journal|title=In search of the structure of human olfactory space|date=2011 |doi=10.3389/fnsys.2011.00065 |doi-access=free |last1=Koulakov |first1=Alexei A. |last2=Kolterman |first2=B. E. |last3=Enikolopov |first3=A. G. |last4=Rinberg |first4=D. |journal=Frontiers in Systems Neuroscience |volume=5 |page=65 |pmid=21954378 |pmc=3173711 }} such as the primacy theory,{{cite journal|url=https://europepmc.org/article/pmc/pmc5684307|title=A primacy code for odor identity.|date=2017 |pmid=29133907 |last1=Wilson |first1=C. D. |last2=Serrano |first2=G. O. |last3=Koulakov |first3=A. A. |last4=Rinberg |first4=D. |journal=Nature Communications |volume=8 |issue=1 |page=1477 |doi=10.1038/s41467-017-01432-4 |pmc=5684307 |bibcode=2017NatCo...8.1477W }} structured connectivity theory,{{cite journal|title=High-throughput sequencing of single neuron projections reveals spatial organization in the olfactory cortex|date=2022 |pmid=36306734 |last1=Chen |first1=Y. |last2=Chen |first2=X. |last3=Baserdem |first3=B. |last4=Zhan |first4=H. |last5=Li |first5=Y. |last6=Davis |first6=M. B. |last7=Kebschull |first7=J. M. |last8=Zador |first8=A. M. |last9=Koulakov |first9=A. A. |last10=Albeanu |first10=D. F. |journal=Cell |volume=185 |issue=22 |pages=4117–4134.e28 |doi=10.1016/j.cell.2022.09.038 |pmc=9681627 }} and sparse representations in the early olfactory system.{{cite journal|title=Sparse incomplete representations: a potential role of olfactory granule cells|date=2011 |pmc=3202217 |last1=Koulakov |first1=A. A. |last2=Rinberg |first2=D. |journal=Neuron |volume=72 |issue=1 |pages=124–136 |doi=10.1016/j.neuron.2011.07.031 |pmid=21982374 }}

Koulakov's team has developed theories of decision-making in collaboration with experimental groups.{{cite journal|url=https://pubmed.ncbi.nlm.nih.gov/16880129/|title=Speed-accuracy tradeoff in olfaction|date=2006 |pmid=16880129 |last1=Rinberg |first1=D. |last2=Koulakov |first2=A. |last3=Gelperin |first3=A. |journal=Neuron |volume=51 |issue=3 |pages=351–358 |doi=10.1016/j.neuron.2006.07.013 }}{{cite arXiv|title=A normative theory of social conflict|eprint=2303.04285 |last1=Shuvaev |first1=Sergey |last2=Amelchenko |first2=Evgeny |last3=Smagin |first3=Dmitry |last4=Kudryavtseva |first4=Natalia |last5=Enikolopov |first5=Grigori |last6=Koulakov |first6=Alexei |date=2023 |class=q-bio.NC }} They have formulated a theory of decision confidence that has been experimentally tested.{{cite journal|title=Orbitofrontal cortex is required for optimal waiting based on decision confidence|date=2014 |pmid=25242219 |last1=Lak |first1=A. |last2=Costa |first2=G. M. |last3=Romberg |first3=E. |last4=Koulakov |first4=A. A. |last5=Mainen |first5=Z. F. |last6=Kepecs |first6=A. |journal=Neuron |volume=84 |issue=1 |pages=190–201 |doi=10.1016/j.neuron.2014.08.039 |pmc=4364549 }}

Koulakov and his colleagues established a deep neural network-based reinforcement learning model of motivational salience, allowing agents to quickly adapt their behavior to changing rewards based on dynamic needs.{{cite journal|title=Neural Networks With Motivation|date=2021 |doi=10.3389/fnsys.2020.609316 |doi-access=free |last1=Shuvaev |first1=Sergey A. |last2=Tran |first2=Ngoc B. |last3=Stephenson-Jones |first3=Marcus |last4=Li |first4=Bo |last5=Koulakov |first5=Alexei A. |journal=Frontiers in Systems Neuroscience |volume=14 |pmid=33536879 |pmc=7848953 }} This approach improved the interpretability of behavioral data by inferring motivational dynamics in the brain. By linking sequential decision-making theories—including the marginal value theorem, reinforcement learning, and Bayesian methods—his research team proposed an optimal decision rule for stay-or-leave choices in natural environments.{{cite web|url=https://proceedings.neurips.cc/paper/2020/hash/da97f65bd113e490a5fab20c4a69f586-Abstract.html|title=R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making|date=2020 |volume=33 |pages=18872–18882 }}

Through joint efforts, Koulakov formulated theories of robust short-term (working) memory systems using attractor neural networks,{{cite journal|title=Neural integrator: a sandpile model|date=2008 |pmid=18533820 |last1=Nikitchenko |first1=M. |last2=Koulakov |first2=A. |journal=Neural Computation |volume=20 |issue=10 |pages=2379–2717 |doi=10.1162/neco.2008.12-06-416 |pmc=2814182 }}{{cite journal|url=https://www.tandfonline.com/doi/abs/10.1080/net.12.1.47.74|title=Properties of synaptic transmission and the global stability of delayed activity states|date=2001 |doi=10.1080/net.12.1.47.74 |last1=Koulakov |first1=A.A. |journal=Network: Computation in Neural Systems |volume=12 |issue=1 |pages=47–74 |pmid=11254082 }}{{cite journal|url=https://pubmed.ncbi.nlm.nih.gov/12134153/|title=Model for a robust neural integrator|date=2002 |pmid=12134153 |last1=Koulakov |first1=A. A. |last2=Raghavachari |first2=S. |last3=Kepecs |first3=A. |last4=Lisman |first4=J. E. |journal=Nature Neuroscience |volume=5 |issue=8 |pages=775–782 |doi=10.1038/nn893 }} and discovered that attractor states can be stabilized in networks exhibiting long-term synaptic plasticity, a phenomenon that may result from memory consolidation.{{cite journal|title=Long-Term Memory Stabilized by Noise-Induced Rehearsal|date=2014 |pmc=4236406 |last1=Wei |first1=Y. |last2=Koulakov |first2=A. A. |journal=The Journal of Neuroscience |volume=34 |issue=47 |pages=15804–15815 |doi=10.1523/JNEUROSCI.3929-12.2014 |pmid=25411507 }}

Spanning 2019 to 2024, Koulakov worked at the intersection of neuroscience and AI, aiming to use insights from nervous system studies to develop improved AI algorithms.{{cite journal|title=Catalyzing next-generation Artificial Intelligence through NeuroAI|date=2023 |doi=10.1038/s41467-023-37180-x |last1=Zador |first1=Anthony |last2=Escola |first2=Sean |last3=Richards |first3=Blake |last4=Ölveczky |first4=Bence |last5=Bengio |first5=Yoshua |last6=Boahen |first6=Kwabena |last7=Botvinick |first7=Matthew |last8=Chklovskii |first8=Dmitri |last9=Churchland |first9=Anne |last10=Clopath |first10=Claudia |last11=Dicarlo |first11=James |last12=Ganguli |first12=Surya |last13=Hawkins |first13=Jeff |last14=Körding |first14=Konrad |last15=Koulakov |first15=Alexei |last16=Lecun |first16=Yann |last17=Lillicrap |first17=Timothy |last18=Marblestone |first18=Adam |last19=Olshausen |first19=Bruno |last20=Pouget |first20=Alexandre |last21=Savin |first21=Cristina |last22=Sejnowski |first22=Terrence |last23=Simoncelli |first23=Eero |last24=Solla |first24=Sara |last25=Sussillo |first25=David |last26=Tolias |first26=Andreas S. |last27=Tsao |first27=Doris |journal=Nature Communications |volume=14 |issue=1 |page=1597 |pmid=36949048 |pmc=10033876 |bibcode=2023NatCo..14.1597Z }}{{cite web|url= https://www.cshl.edu/research/neuroscience/neuroai/#faculty|title=NeuroAI}}

Koulakov and his colleagues theoretically identified a new quantum state of electrons in quantum Hall samples under weak magnetic fields, resembling a liquid crystal phase, which they described as "Quantum Hall Nematics". This theoretical discovery was later confirmed experimentally.{{cite journal|url=https://journals.aps.org/prb/abstract/10.1103/PhysRevB.54.1853|title=Ground state of a two-dimensional electron liquid in a weak magnetic field|date=1996 |doi=10.1103/PhysRevB.54.1853 |last1=Fogler |first1=M. M. |last2=Koulakov |first2=A. A. |last3=Shklovskii |first3=B. I. |journal=Physical Review B |volume=54 |issue=3 |pages=1853–1871 |pmid=9986033 |arxiv=cond-mat/9601110 |bibcode=1996PhRvB..54.1853F }}{{cite journal|url=https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.76.499|title=Charge Density Wave in Two-Dimensional Electron Liquid in Weak Magnetic Field|date=1996 |doi=10.1103/PhysRevLett.76.499 |last1=Koulakov |first1=A. A. |last2=Fogler |first2=M. M. |last3=Shklovskii |first3=B. I. |journal=Physical Review Letters |volume=76 |issue=3 |pages=499–502 |pmid=10061472 |arxiv=cond-mat/9508017 |bibcode=1996PhRvL..76..499K }} Within mesoscopic physics, they contributed to understanding the properties of small particles, such as quantum dots with a few electrons and superconducting vortex cores.{{cite journal|url=https://journals.aps.org/prb/abstract/10.1103/PhysRevB.55.9223|title=Statistics of the charging spectrum of a two-dimensional Coulomb-glass island|date=1997 |doi=10.1103/PhysRevB.55.9223 |last1=Koulakov |first1=A. A. |last2=Pikus |first2=F. G. |last3=Shklovskii |first3=B. I. |journal=Physical Review B |volume=55 |issue=15 |pages=9223–9226 |arxiv=cond-mat/9612236 |bibcode=1997PhRvB..55.9223K }}{{cite journal|url=https://www.tandfonline.com/doi/abs/10.1080/13642819808205015|title=Charging spectrum of a small Wigner crystal island|date=1998 |doi=10.1080/13642819808205015 |last1=Koulakov |first1=A. A. |last2=Shklovskii |first2=B. I. |journal=Philosophical Magazine B |volume=77 |issue=5 |pages=1235–1247 |arxiv=cond-mat/9705030 }}{{cite journal|url=https://journals.aps.org/prb/abstract/10.1103/PhysRevB.59.12021|title=Vortex-mediated microwave absorption in superclean layered superconductors|date=1999 |doi=10.1103/PhysRevB.59.12021 |last1=Koulakov |first1=A. A. |last2=Larkin |first2=A. I. |journal=Physical Review B |volume=59 |issue=18 |pages=12021–12032 |arxiv=cond-mat/9802002 |bibcode=1999PhRvB..5912021K }}

• 2018 – NIH Director's Transformative Research Award, NIH

• Koulakov, A. A., Fogler, M. M., & Shklovskii, B. I. (1996). Charge density wave in two-dimensional electron liquid in weak magnetic field. Physical review letters, 76(3), 499.
• Fogler, M. M., Koulakov, A. A., & Shklovskii, B. I. (1996). Ground state of a two-dimensional electron liquid in a weak magnetic field. Physical Review B, 54(3), 1853.
• Koulakov, A. A., Raghavachari, S., Kepecs, A., & Lisman, J. E. (2002). Model for a robust neural integrator. Nature neuroscience, 5(8), 775–782.
• Chklovskii, D. B., & Koulakov, A. A. (2004). Maps in the brain: what can we learn from them?. Annu. Rev. Neurosci., 27(1), 369–392.
• Encinas, J. M., Michurina, T. V., Peunova, N., Park, J. H., Tordo, J., Peterson, D. A., ... & Enikolopov, G. (2011). Division-coupled astrocytic differentiation and age-related depletion of neural stem cells in the adult hippocampus. Cell stem cell, 8(5), 566–579.

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Category:Theoretical physicists

Category:Neuroscientists

Category:Moscow Institute of Physics and Technology alumni

Category:University of Minnesota alumni

Category:Living people

Category:Year of birth missing (living people)