Talia N. Lerner

3.8k total citations · 1 hit paper
28 papers, 2.4k citations indexed

About

Talia N. Lerner is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Molecular Biology. According to data from OpenAlex, Talia N. Lerner has authored 28 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Cellular and Molecular Neuroscience, 15 papers in Cognitive Neuroscience and 7 papers in Molecular Biology. Recurrent topics in Talia N. Lerner's work include Neurotransmitter Receptor Influence on Behavior (14 papers), Neural dynamics and brain function (7 papers) and Receptor Mechanisms and Signaling (7 papers). Talia N. Lerner is often cited by papers focused on Neurotransmitter Receptor Influence on Behavior (14 papers), Neural dynamics and brain function (7 papers) and Receptor Mechanisms and Signaling (7 papers). Talia N. Lerner collaborates with scholars based in United States, Japan and France. Talia N. Lerner's co-authors include Karl Deisseroth, Thomas J. Davidson, Anatol C. Kreitzer, Kelly A. Zalocusky, Ye Li, Robert C. Malenka, Liqun Luo, Kevin T. Beier, Charu Ramakrishnan and Joshua H. Jennings and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Talia N. Lerner

26 papers receiving 2.4k citations

Hit Papers

Intact-Brain Analyses Rev... 2015 2026 2018 2022 2015 100 200 300 400 500

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Talia N. Lerner United States 18 1.5k 1.1k 618 261 224 28 2.4k
Christophe D. Proulx Canada 16 1.7k 1.2× 1.0k 0.9× 707 1.1× 286 1.1× 202 0.9× 24 2.7k
Eleanor H. Simpson United States 31 1.6k 1.1× 1.1k 1.0× 1.1k 1.8× 347 1.3× 198 0.9× 59 3.1k
Nicholas Wall United States 9 1.6k 1.1× 1.0k 0.9× 685 1.1× 312 1.2× 137 0.6× 12 2.3k
Jonathan P. Britt United States 17 1.8k 1.2× 824 0.7× 942 1.5× 202 0.8× 208 0.9× 23 2.3k
Nicolas X. Tritsch United States 21 1.9k 1.3× 966 0.9× 1.1k 1.8× 257 1.0× 230 1.0× 29 3.1k
Katherine E. DeLoach United States 7 1.3k 0.9× 1.0k 0.9× 623 1.0× 247 0.9× 104 0.5× 8 2.1k
Sachie K. Ogawa United States 15 1.6k 1.1× 1.5k 1.3× 665 1.1× 251 1.0× 168 0.8× 36 2.6k
Thomas C. Jhou United States 25 2.3k 1.5× 1.4k 1.3× 977 1.6× 325 1.2× 258 1.2× 41 3.2k
Kevin J. Bender United States 29 1.8k 1.2× 1.1k 1.0× 966 1.6× 242 0.9× 105 0.5× 51 2.8k
Lucas Lecourtier France 18 1.1k 0.7× 670 0.6× 460 0.7× 180 0.7× 189 0.8× 33 1.6k

Countries citing papers authored by Talia N. Lerner

Since Specialization
Citations

This map shows the geographic impact of Talia N. Lerner's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Talia N. Lerner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Talia N. Lerner more than expected).

Fields of papers citing papers by Talia N. Lerner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Talia N. Lerner. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Talia N. Lerner. The network helps show where Talia N. Lerner may publish in the future.

Co-authorship network of co-authors of Talia N. Lerner

This figure shows the co-authorship network connecting the top 25 collaborators of Talia N. Lerner. A scholar is included among the top collaborators of Talia N. Lerner based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Talia N. Lerner. Talia N. Lerner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lerner, Talia N., et al.. (2026). Dopamine’s secret agent: serotonin. Trends in Neurosciences. 49(2). 77–79.
2.
Schaid, Michael D., et al.. (2025). Region-specific nucleus accumbens dopamine signals encode distinct aspects of avoidance learning. Current Biology. 35(10). 2433–2443.e5. 3 indexed citations
3.
Lerner, Talia N., et al.. (2024). How dopamine enables learning from aversion. Current Opinion in Behavioral Sciences. 61. 101476–101476. 4 indexed citations
4.
Seiler, Jillian L., et al.. (2024). Dopamine across timescales and cell types: Relevance for phenotypes in Parkinson's disease progression. Experimental Neurology. 374. 114693–114693.
5.
Lerner, Talia N., et al.. (2023). Hidden variables in stress neurobiology research. Trends in Neurosciences. 47(1). 9–17. 7 indexed citations
6.
Schaid, Michael D., et al.. (2023). Chronically dysregulated corticosterone impairs dopaminergic transmission in the dorsomedial striatum by sex-divergent mechanisms. Neuropsychopharmacology. 48(9). 1328–1337. 11 indexed citations
7.
Xu, Jian, Andrew Jo, J. Marshall, et al.. (2022). Intersectional mapping of multi-transmitter neurons and other cell types in the brain. Cell Reports. 40(1). 111036–111036. 17 indexed citations
8.
Schaid, Michael D., et al.. (2021). GuPPy, a Python toolbox for the analysis of fiber photometry data. Scientific Reports. 11(1). 24212–24212. 58 indexed citations
9.
Pamukcu, Arin, Qiaoling Cui, Harry S. Xenias, et al.. (2020). Parvalbumin + and Npas1 + Pallidal Neurons Have Distinct Circuit Topology and Function. Journal of Neuroscience. 40(41). 7855–7876. 48 indexed citations
10.
Lerner, Talia N., et al.. (2020). Dopamine, Updated: Reward Prediction Error and Beyond. Current Opinion in Neurobiology. 67. 123–130. 77 indexed citations
11.
Steinberg, Elizabeth E., Felicity Gore, Boris D. Heifets, et al.. (2020). Amygdala-Midbrain Connections Modulate Appetitive and Aversive Learning. Neuron. 106(6). 1026–1043.e9. 60 indexed citations
12.
Andalman, Aaron S., Matthew Lovett-Barron, Michael Broxton, et al.. (2019). Neuronal Dynamics Regulating Brain and Behavioral State Transitions. Cell. 177(4). 970–985.e20. 158 indexed citations
13.
Kim, Christina K., Samuel Yang, Nandini Pichamoorthy, et al.. (2016). Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain. Nature Methods. 13(4). 325–328. 315 indexed citations
14.
Zalocusky, Kelly A., Charu Ramakrishnan, Talia N. Lerner, et al.. (2016). Nucleus accumbens D2R cells signal prior outcomes and control risky decision-making. Nature. 531(7596). 642–646. 154 indexed citations
15.
Lerner, Talia N., Ye Li, & Karl Deisseroth. (2016). Communication in Neural Circuits: Tools, Opportunities, and Challenges. Cell. 164(6). 1136–1150. 120 indexed citations
16.
Lerner, Talia N., Carrie Shilyansky, Thomas J. Davidson, et al.. (2015). Intact-Brain Analyses Reveal Distinct Information Carried by SNc Dopamine Subcircuits. Cell. 162(3). 635–647. 524 indexed citations breakdown →
17.
Adhikari, Avishek, Talia N. Lerner, Joel Finkelstein, et al.. (2015). Basomedial amygdala mediates top-down control of anxiety and fear. Nature. 527(7577). 179–185. 358 indexed citations
18.
Lerner, Talia N. & Anatol C. Kreitzer. (2012). RGS4 Is Required for Dopaminergic Control of Striatal LTD and Susceptibility to Parkinsonian Motor Deficits. Neuron. 73(2). 347–359. 146 indexed citations
19.
Lerner, Talia N. & Anatol C. Kreitzer. (2011). Neuromodulatory control of striatal plasticity and behavior. Current Opinion in Neurobiology. 21(2). 322–327. 56 indexed citations
20.
Lerner, Talia N., Eric A. Horne, Nephi Stella, & Anatol C. Kreitzer. (2010). Endocannabinoid Signaling Mediates Psychomotor Activation by Adenosine A 2A Antagonists. Journal of Neuroscience. 30(6). 2160–2164. 62 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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