The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
The role of acetylcholine in learning and memory
Open accessAbstract
Pharmacological data clearly indicate that both muscarinic and nicotinic acetylcholine receptors have a role in the encoding of new memories. Localized lesions and antagonist infusions demonstrate the anatomical locus of these cholinergic effects, and computational modeling links the function of cholinergic modulation to specific cellular effects within these regions. Acetylcholine has been shown to increase the strength of afferent input relative to feedback, to contribute to theta rhythm oscillations, activate intrinsic mechanisms for persistent spiking, and increase the modification of synapses. These effects might enhance different types of encoding in different cortical structures. In particular, the effects in entorhinal and perirhinal cortex and hippocampus might be important for encoding new episodic memories.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Functionally, these receptors play a role in learning and memory (Hasselmo 2006).”
From The long-term cognitive consequences of adolescent exposure to recreational drugs of abuse (2018) · cites this paper as Hasselmo 2006
In the citing paper
“DA and ACh neurotransmission play critical roles in the regulation of memory via the ventral tegmental area and MSDB projections to the PFC-hippocampus-associated circuits”
In the citing paper
“An increase of acetylcholine concentration (ACh) at the synapse by inhibiting the acetylcholinesterase (AChE) is one of the approaches to slow down the progression of AD [18,19,20,21,22,23,24,25,26].”
From Acetylcholinesterase Inhibitory Potential of Various Sesquiterpene Analogues for Alzheimer’s Disease Therapy (2021) · cites this paper as [18]
Citations
Cites
Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo
Klausberger, T., Magill, P. J., Márton, L. F., Roberts, J. D. B., Cobden, P. M., Buzsáki, G., & Somogyi, P. (2003) · Nature · doi:10.1038/nature01374 · Cited by 1,345
Hasselmo, M. E., Bodelón, C., & Wyble, B. P. (2002) · Neural Computation · doi:10.1162/089976602317318965 · Cited by 875
Self-citationLevin, E. D., McClernon, F. J., & Rezvani, A. H. (2006) · Psychopharmacology · doi:10.1007/s00213-005-0164-7 · Cited by 820
Neuromodulation: acetylcholine and memory consolidation
Hasselmo, M. E. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01365-0 · Cited by 809
Self-citationHuerta, P. T., & Lisman, J. E. (1995) · Neuron · doi:10.1016/0896-6273(95)90094-2 · Cited by 673
Differential regulation of neocortical synapses by neuromodulators and activity
Gil, Z., Connors, B. W., & Amitai, Y. (1997) · Neuron · doi:10.1016/s0896-6273(00)80380-3 · Cited by 553
Hasselmo, M. E., & McGaughy, J. (2004) · Progress in Brain Research · doi:10.1016/s0079-6123(03)45015-2 · Cited by 525
Self-citationBland, B. H., & Oddie, S. D. (2001) · Behavioural Brain Research · doi:10.1016/s0166-4328(01)00358-8 · Cited by 520
Hasselmo, M., & Schnell, E. (1994) · The Journal of Neuroscience · doi:10.1523/jneurosci.14-06-03898.1994 · Cited by 466
Self-citationHasselmo, M. E., Schnell, E., & Barkai, E. (1995) · Journal of Neuroscience · doi:10.1523/jneurosci.15-07-05249.1995 · Cited by 463
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning
Buzsáki, G. (2015) · Hippocampus · doi:10.1002/hipo.22488 · Cited by 1,759
Acetylcholine as a Neuromodulator: Cholinergic Signaling Shapes Nervous System Function and Behavior
Picciotto, M. R., Higley, M. J., & Mineur, Y. S. (2012) · Neuron · doi:10.1016/j.neuron.2012.08.036 · Cited by 1,404
An opportunity cost model of subjective effort and task performance
Kurzban, R., Duckworth, A., Kable, J. W., & Myers, J. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12003196 · Cited by 1,381
Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval
Carr, M. F., Jadhav, S. P., & Frank, L. M. (2011) · Nature Neuroscience · doi:10.1038/nn.2732 · Cited by 958
Hippocampal GABAergic Inhibitory Interneurons
Pelkey, K. A., Chittajallu, R., Craig, M. T., Tricoire, L., Wester, J. C., & McBain, C. J. (2017) · Physiological Reviews · doi:10.1152/physrev.00007.2017 · Cited by 889
Cognitive impairment in schizophrenia: aetiology, pathophysiology, and treatment
McCutcheon, R. A., Keefe, R. S. E., & McGuire, P. K. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-01949-9 · Cited by 861
Modes and models of forebrain cholinergic neuromodulation of cognition
Hasselmo, M. E., & Sarter, M. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2010.104 · Cited by 733
Self-citationKlinkenberg, I., & Blokland, A. (2010) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2010.04.001 · Cited by 714
An oscillatory interference model of grid cell firing
Burgess, N., Barry, C., & O'Keefe, J. M. (2007) · Hippocampus · doi:10.1002/hipo.20327 · Cited by 708
The contribution of sleep to hippocampus-dependent memory consolidation
Marshall, L., & Born, J. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.09.001 · Cited by 667
Muscarinic acetylcholine receptors: mutant mice provide new insights for drug development
Wess, J., Eglen, R. M., & Gautam, D. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2379 · Cited by 637
Yassa, M. A., Lacy, J. W., Stark, S. M., Albert, M. S., Gallagher, M., & Stark, C. E. L. (2011) · Hippocampus · doi:10.1002/hipo.20808 · Cited by 595
Striatal circuits for reward learning and decision-making
Cox, J., & Witten, I. B. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0189-2 · Cited by 584
Dendritic Inhibition in the Hippocampus Supports Fear Learning
Lovett-Barron, M., Kaifosh, P., Kheirbek, M. A., Danielson, N., Zaremba, J. D., Reardon, T. R., Turi, G. F., Hen, R., Zemelman, B. V., & Losonczy, A. (2014) · Science · doi:10.1126/science.1247485 · Cited by 509
Plasticity of Cortical Excitatory-Inhibitory Balance
Froemke, R. C. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-034002 · Cited by 458
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules
Frémaux, N., & Wulfram Gerstner, B. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2015.00085 · Cited by 449
An optimized acetylcholine sensor for monitoring in vivo cholinergic activity
Jing, M., Li, Y., Zeng, J., Huang, P., Skirzewski, M., Kljakic, O., Peng, W., Qian, T., Tan, K., Zou, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-020-0953-2 · Cited by 408
Self-organization and neuronal avalanches in networks of dissociated cortical neurons
Pasquale, V., Massobrio, P., Bologna, L. L., Chiappalone, M., & Martinoia, S. (2008) · Neuroscience · doi:10.1016/j.neuroscience.2008.03.050 · Cited by 392
Episodic memory in normal aging and Alzheimer disease: Insights from imaging and behavioral studies
Tromp, D., Dufour, A., Lithfous, S., Pebayle, T., & Després, O. (2015) · Ageing Research Reviews · doi:10.1016/j.arr.2015.08.006 · Cited by 390
Bifidobacteria modulate cognitive processes in an anxious mouse strain
Savignac, H. M., Tramullas, M., Kiely, B., Dinan, T. G., & Cryan, J. F. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2015.02.044 · Cited by 365
Alves, P. N., Foulon, C., Karolis, V., Bzdok, D., Margulies, D. S., Volle, E., & Thiebaut de Schotten, M. (2019) · Communications Biology · doi:10.1038/s42003-019-0611-3 · Cited by 357
Hierarchical Prediction Errors in Midbrain and Basal Forebrain during Sensory Learning
Iglesias, S., Mathys, C., Brodersen, K. H., Kasper, L., Piccirelli, M., den Ouden, H. E. M., & Stephan, K. E. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.009 · Cited by 342
Crystal structures of the M1 and M4 muscarinic acetylcholine receptors
Thal, D. M., Sun, B., Feng, D., Nawaratne, V., Leach, K., Felder, C. C., Bures, M. G., Evans, D. A. P., Weis, W. I., Bachhawat, P., et al. (2016) · Nature · doi:10.1038/nature17188 · Cited by 342
Monteiro, A. R., Barbosa, D. J., Remião, F., & Silva, R. V. D. S. (2023) · Biochemical Pharmacology · doi:10.1016/j.bcp.2023.115522 · Cited by 375
Klinkenberg, I., Sambeth, A., & Blokland, A. (2010) · Behavioural Brain Research · doi:10.1016/j.bbr.2010.11.033 · Cited by 335
Wulfram Gerstner, B., Lehmann, M., Liakoni, V., Corneil, D., & Brea, J. (2018) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00053 · Cited by 354
Yang, H., Xiong, H., Mi, K., Xue, W., Wei, W., & Zhang, Y. (2020) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2020.122058 · Cited by 335
Neuromodulatory Influences on Integration and Segregation in the Brain
Shine, J. M. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.04.002 · Cited by 328
Functional significance of axonal Kv7 channels in hippocampal pyramidal neurons
Shah, M. M., Migliore, M., Valencia, I., Cooper, E. C., & Brown, D. A. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0802805105 · Cited by 298
Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks
English, D. F., McKenzie, S., Evans, T., Kim, K., Yoon, E., & Buzsáki, G. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.033 · Cited by 299
Sublayer-Specific Coding Dynamics during Spatial Navigation and Learning in Hippocampal Area CA1
Danielson, N. B., Zaremba, J. D., Kaifosh, P., Bowler, J., Ladow, M., & Losonczy, A. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.020 · Cited by 288
Subtype-selective allosteric modulators of muscarinic receptors for the treatment of CNS disorders
Conn, P. J., Jones, C. K., & Lindsley, C. W. (2009) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2008.12.002 · Cited by 286
Carter, C. S., & Barch, D. M. (2007) · Schizophrenia Bulletin · doi:10.1093/schbul/sbm081 · Cited by 278
Sensorimotor processing in the rodent barrel cortex
Petersen, C. C. H. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0200-y · Cited by 283
An opportunistic theory of cellular and systems consolidation
Mednick, S. C., Cai, D. J., Shuman, T., Anagnostaras, S., & Wixted, J. T. (2011) · Trends in Neurosciences · doi:10.1016/j.tins.2011.06.003 · Cited by 267
Fanselow, E. E., Richardson, K. A., & Connors, B. W. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90691.2008 · Cited by 262
Maurer, S. V., & Williams, C. L. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.01489 · Cited by 276
Timing is not everything: neuromodulation opens the STDP gate
Pawlak, V. (2010) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2010.00146 · Cited by 267
Organophosphate pesticide exposure and neurodegeneration
Sánchez-Santed, F., Colomina, M. T., & Hernández, E. H. (2015) · Cortex · doi:10.1016/j.cortex.2015.10.003 · Cited by 258
Incremental learning algorithms and applications
Gepperth, A., & Hammer, B. (2016) · HAL · Cited by 253
Nerve Agents: What They Are, How They Work, How to Counter Them
Costanzi, S., Machado, J., & Mitchell, M. (2018) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00148 · Cited by 273
Entorhinal cortex dysfunction in Alzheimer's disease
Igarashi, K. M. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.11.006 · Cited by 276
Hasselmo, M. E., Giocomo, L. M., & Zilli, E. A. (2007) · Hippocampus · doi:10.1002/hipo.20374 · Cited by 249
Self-citationQuercetin and rutin prevent scopolamine-induced memory impairment in zebrafish
Richetti, S. K., Blank, M., Capiotti, K. M., Piato, Â., Bogo, M. R., Vianna, M. R. M. R., & Bonan, C. D. (2010) · Behavioural Brain Research · doi:10.1016/j.bbr.2010.09.027 · Cited by 247
Neuromodulation of Spike-Timing-Dependent Plasticity: Past, Present, and Future
Brzosko, Z., Mierau, S. B., & Paulsen, O. (2019) · Neuron · doi:10.1016/j.neuron.2019.05.041 · Cited by 267
The role of REM sleep theta activity in emotional memory
Hutchison, I. C., & Rathore, S. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.01439 · Cited by 245
Modulation of Hippocampus-Dependent Learning and Synaptic Plasticity by Nicotine
Kenney, J. W., & Gould, T. J. (2008) · Molecular Neurobiology · doi:10.1007/s12035-008-8037-9 · Cited by 242
Parkinson's Disease Therapeutics: New Developments and Challenges Since the Introduction of Levodopa
Smith, Y., Wichmann, T., Factor, S. A., & DeLong, M. R. (2011) · Neuropsychopharmacology · doi:10.1038/npp.2011.212 · Cited by 266
A molecular approach in drug development for Alzheimer’s disease
Agatonović-Kuštrin, S., Kettle, C., & Morton, D. W. (2018) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2018.06.147 · Cited by 240
A neural-level model of spatial memory and imagery
Bicanski, A., & Burgess, N. (2018) · eLife · doi:10.7554/elife.33752 · Cited by 230
Neuron-glia networks: integral gear of brain function
Perea, G., Sur, M., & Araque, A. (2014) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2014.00378 · Cited by 241
Development of allosteric modulators of GPCRs for treatment of CNS disorders
Nickols, H. H., & Conn, P. J. (2013) · Neurobiology of Disease · doi:10.1016/j.nbd.2013.09.013 · Cited by 232
Learned spatiotemporal sequence recognition and prediction in primary visual cortex
Gavornik, J. P., & Bear, M. F. (2014) · Nature Neuroscience · doi:10.1038/nn.3683 · Cited by 227
Visual Working Memory Is Impaired when the Medial Temporal Lobe Is Damaged
Olson, I. R., Moore, K. S., Stark, M., & Chatterjee, A. K. (2006) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2006.18.7.1087 · Cited by 233
Srivastava, P., Tripathi, P. N., Sharma, P. A., Nand, S., Singh, S. P., Srivastava, R. K., Shankar, S., & Shrivastava, S. K. (2018) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2018.11.049 · Cited by 220
Anti-Alzheimer’s Studies on β-Sitosterol Isolated from Polygonum hydropiper L.
Ayaz, M., Junaid, M., Ullah, F., Subhan, F., Sadiq, A., Ali, G., Ovais, M., Shahid, M., Ahmad, A., Wadood, A., et al. (2017) · Frontiers in Pharmacology · doi:10.3389/fphar.2017.00697 · Cited by 220
Prediction and memory: A predictive coding account
Barron, H. C., Auksztulewicz, R., & Friston, K. (2020) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2020.101821 · Cited by 220
Milton, A. L., & Everitt, B. J. (2010) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2010.07249.x · Cited by 223
Short- and long-lasting consequences of novelty, deviance and surprise on brain and cognition
Schomaker, J., & Meeter, M. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.05.002 · Cited by 229
Nimgampalle, M., Chakravarthy, H., Sharma, S., Shree, S., Bhat, A. R., Pradeepkiran, J. A., & Devanathan, V. (2023) · Ageing Research Reviews · doi:10.1016/j.arr.2023.101994 · Cited by 207
Cognato, G. D. P., Bortolotto, J. W., Blazina, A. R., Christoff, R. R., Lara, D. R., Vianna, M. R. M. R., & Bonan, C. D. (2012) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2012.09.008 · Cited by 212
Cholinergic modulation of hippocampal network function
Ruivo, L. M. T.-G., & Mellor, J. R. (2013) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2013.00002 · Cited by 214
Yamasaki, M., Matsui, M., & Watanabe, M. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5719-09.2010 · Cited by 203
Specific basal forebrain-cortical cholinergic circuits coordinate cognitive operations
Záborszky, L., Gombkoto, P., Varsanyi, P., Gielow, M. R., Poe, G., Role, L. W., Ananth, M., Rajebhosale, P., Talmage, D. A., Hasselmo, M. E., et al. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.1676-18.2018 · Cited by 202
Self-citationUdakis, M., Pedrosa, V., Chamberlain, S. E. L., Clopath, C., & Mellor, J. R. (2020) · Nature Communications · doi:10.1038/s41467-020-18074-8 · Cited by 205
Pharmacological Foundations of Cannabis Chemovars
Lewis, M. A., Russo, E. B., & Smith, K. (2017) · Planta Medica · doi:10.1055/s-0043-122240 · Cited by 192
Koster, R., Chadwick, M. J., Chen, Y., Berron, D., Banino, A., Düzel, E., Hassabis, D., & Kumaran, D. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.009 · Cited by 191
Cholinergic modulation of cognitive processing: insights drawn from computational models
Newman, E. L., Gupta, K., Climer, J. R., Monaghan, C. K., & Hasselmo, M. E. (2012) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2012.00024 · Cited by 196
Self-citationHow do memory systems detect and respond to novelty?
Kafkas, A., & Montaldi, D. (2018) · Neuroscience Letters · doi:10.1016/j.neulet.2018.01.053 · Cited by 207
Twenty-Five Lessons from Computational Neuromodulation
Dayan, P. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.027 · Cited by 190
The role of metaplasticity mechanisms in regulating memory destabilization and reconsolidation
Finnie, P. S. B., & Nader, K. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.03.008 · Cited by 189
Environmental novelty is signaled by reduction of the hippocampal theta frequency
Jeewajee, A., Lever, C., Burton, S., O'Keefe, J. M., & Burgess, N. (2007) · Hippocampus · doi:10.1002/hipo.20394 · Cited by 196
Wnt Signaling: Role in Alzheimer Disease and Schizophrenia
Inestrosa, N. C., Montecinos-Oliva, C., & Fuenzalida, M. (2012) · Journal of Neuroimmune Pharmacology · doi:10.1007/s11481-012-9417-5 · Cited by 192
McNaughton, N., Kocsis, B., & Hajós, M. (2007) · Behavioural Pharmacology · doi:10.1097/fbp.0b013e3282ee82e3 · Cited by 187
Reimagining cholinergic therapy for Alzheimer’s disease
Giacobini, E., Cuello, A. C., & Fisher, A. (2022) · Brain · doi:10.1093/brain/awac096 · Cited by 187
Neuromodulation by acetylcholine: examples from schizophrenia and depression
Higley, M. J., & Picciotto, M. R. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.06.004 · Cited by 184
Neuromodulation of hippocampal long-term synaptic plasticity
Palacios-Filardo, J., & Mellor, J. R. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.08.009 · Cited by 184
Priming for Life: Early Life Nutrition and the Microbiota-Gut-Brain Axis
Ratsika, A., Codagnone, M. G., O’Mahony, S. M., Stanton, C., & Cryan, J. F. (2021) · Nutrients · doi:10.3390/nu13020423 · Cited by 194
Hybrid carbon based nanomaterials for electrochemical detection of biomolecules
Laurila, T. T., Sainio, S., & Miguel, A. (2017) · Progress in Materials Science · doi:10.1016/j.pmatsci.2017.04.012 · Cited by 177
Ruivo, L. M. T.-G., Baker, K. L., Conway, M. W., Kinsley, P. J., Gilmour, G., Phillips, K. G., Isaac, J. T. R., Lowry, J. P., & Mellor, J. R. (2017) · Cell Reports · doi:10.1016/j.celrep.2016.12.085 · Cited by 181
El-Agamy, S. E., Abdel‐Aziz, A. K., Wahdan, S. A., Esmat, A., & Azab, S. S. (2017) · Molecular Neurobiology · doi:10.1007/s12035-017-0797-7 · Cited by 173
Leach, K., Loiacono, R. E., Felder, C. C., McKinzie, D. L., Mogg, A. J., Shaw, D. B., Sexton, P. M., & Christopoulos, A. (2009) · Neuropsychopharmacology · doi:10.1038/npp.2009.194 · Cited by 167
Liu, A. A., Henin, S. E., Abbaspoor, S., Bragin, A., Buffalo, E. A., Farrell, J. S., Foster, D. J., Frank, L. M., Gedankien, T., Gotman, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33536-x · Cited by 188
Acetylcholine and memory: A long, complex and chaotic but still living relationship
Micheau, J., & Marighetto, A. (2010) · Behavioural Brain Research · doi:10.1016/j.bbr.2010.11.052 · Cited by 164
Volk, L. J., Pfeiffer, B. E., Gibson, J. R., & Huber, K. M. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.2266-07.2007 · Cited by 166
Assini, F. L., Duzzioni, M., & Takahashi, R. N. (2009) · Behavioural Brain Research · doi:10.1016/j.bbr.2009.06.005 · Cited by 161
Basal forebrain volume reliably predicts the cortical spread of Alzheimer’s degeneration
Fernández‐Cabello, S., Kronbichler, M., Dijk, K. R. A. V., Goodman, J. A., Spreng, R. N., Schmitz, T. W., & Initiative, O. B. O. T. A. D. N. (2020) · Brain · doi:10.1093/brain/awaa012 · Cited by 159
Chao, O. Y., de Souza Silva, M. A., Yang, Y.-M., & Huston, J. P. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.04.007 · Cited by 158
A model of episodic memory: Mental time travel along encoded trajectories using grid cells
Hasselmo, M. E. (2009) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2009.07.005 · Cited by 166
Self-citationChatterjee, M., Verma, R., Ganguly, S., & Palit, G. (2012) · Neuropharmacology · doi:10.1016/j.neuropharm.2012.05.041 · Cited by 152
Bloem, B., Poorthuis, R. B., & Mansvelder, H. D. (2014) · Frontiers in Neural Circuits · doi:10.3389/fncir.2014.00017 · Cited by 163
A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems
Gepperth, A., & Karaoguz, C. (2016) · Cognitive Computation · doi:10.1007/s12559-016-9389-5 · Cited by 148
Improved green and red GRAB sensors for monitoring dopaminergic activity in vivo
Zhuo, Y., Luo, B., Yi, X., Dong, H., Miao, X., Wan, J., Williams, J. T., Campbell, M. G., Cai, R., Qian, T., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02100-w · Cited by 174
Basal forebrain cholinergic signalling: development, connectivity and roles in cognition
Ananth, M. R., Rajebhosale, P., Kim, R., Talmage, D. A., & Role, L. W. (2023) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00677-x · Cited by 148
M1 muscarinic acetylcholine receptor in Alzheimer’s disease
Jiang, S., Li, Y., Zhang, C., Zhao, Y., Bu, G., Xu, H., & Zhang, Y. (2014) · Neuroscience Bulletin · doi:10.1007/s12264-013-1406-z · Cited by 147
Doxorubicin and cisplatin induced cognitive impairment: The possible mechanisms and interventions
Ongnok, B., Chattipakorn, N., & Chattipakorn, S. C. (2019) · Experimental Neurology · doi:10.1016/j.expneurol.2019.113118 · Cited by 151
Dennis, S. H., Pasqui, F., Colvin, E. M., Sanger, H., Mogg, A. J., Felder, C. C., Broad, L. M., Fitzjohn, S. M., Isaac, J. T. R., & Mellor, J. R. (2015) · Cerebral Cortex · doi:10.1093/cercor/bhv227 · Cited by 144
Cohen, J. D., Bolstad, M., & Lee, A. K. (2017) · eLife · doi:10.7554/elife.23040 · Cited by 146
Freezing revisited: coordinated autonomic and central optimization of threat coping
Roelofs, K., & Dayan, P. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00608-2 · Cited by 160
The long-term cognitive consequences of adolescent exposure to recreational drugs of abuse
Mooney-Leber, S. M., & Gould, T. J. (2018) · Learning & Memory · doi:10.1101/lm.046672.117 · Cited by 140
Metadata
- Authors
- Hasselmo, M. E.
- Affiliations
- Boston University (US)
- Corresponding author
- Hasselmo, Michael E
- ORCID
- Hasselmo, Michael E
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2006-12
- Volume
- 16
- Issue
- 6
- Pages
- 710-715
- ISSN
- 0959-4388 (ISSN-L)
- PMC
- PMC2659740
- OpenAlex
- W2053696585
- Cited by
- 1,693
- References
- 62
- Citations by year
- 2026: 77 · 2025: 122 · 2024: 154 · 2023: 126 · 2022: 128 · 2021: 98 · 2020: 104 · 2019: 102 · 2018: 99 · 2017: 90 · 2016: 75 · 2015: 73 · 2014: 63 · 2013: 69 · 2012: 75
- FWCI
- 2.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Opinion in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (MH 60013, MH61492, R01MH060013, R01MH061492)
- National Institute on Drug Abuse (DA 16454, R01DA016454)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Nicotinic Acetylcholine Receptors Study
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- acetylcholine, muscarinic receptors, nicotinic acetylcholine receptors, memory encoding, learning, cholinergic modulation, theta oscillations, synaptic plasticity, entorhinal cortex, perirhinal cortex, hippocampus, episodic memory, afferent feedback
- MeSH terms
- Acetylcholine, Action Potentials, Afferent Pathways, Animals, Brain, Feedback, Humans, Learning, Memory, Neural Inhibition, Synaptic Transmission, Theta Rhythm
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review