This is how the Neocortex Learns — part 3/3

Cleaned-text code points [28384, 42625). Bibliography omitted here; source.md includes it.

This is how the Neocortex Learns 10
Blei, D.M., Kucukelbir, A., & McAuliffe, J.D. (2017). Variational Inference: A Review for Statisticians. 
Journal of the American Statistical Association, 112, 859–877. https.//doi:org/
10.1080/01621459.2017.1285773 http.//doi:org/10.1080/01621459.2017.1285773
Campbell, E.P., Martin, L., Magee, J.C., & Grienberger, C. (2026). Learning-dependent feedback by OLM 
interneurons shapes CA1 representations. 2025.12.21.695825. https.//www:biorxiv.org/content/
10.64898/2025.12.21.695825v2 http.//doi:org/10.64898/2025.12.21.695825
Cook, S.G., Buonarati, O.R., Coultrap, S.J., & Bayer, K.U. (2021). CaMKII holoenzyme mechanisms that 
govern the LTP versus LTD decision. Science Advances, https.//www:science.org/doi/abs/10.1126/
sciadv.abe2300 http.//doi:org/10.1126/sciadv.abe2300
Crick, F. (1989). The recent excitement about neural networks. Nature, 337, 129–132. http.//
www:ncbi.nlm.nih.gov/pubmed/2911347
Debanne, D., & Inglebert, Y. (2023). Spike timing-dependent plasticity and memory. Current Opinion in 
Neurobiology, 80, 102707. https.//www:sciencedirect.com/science/article/pii/S0959438823000326 http.//
doi:org/10.1016/j.conb.2023.102707
Diehl, P.U., & Cook, M. (2015). Unsupervised learning of digit recognition using spike-timing-dependent 
plasticity. Frontiers in Computational Neuroscience, 9, https.//www:frontiersin.org/articles/10.3389/
fncom.2015.00099/full http.//doi:org/10.3389/fncom.2015.00099
Economo, M.N., Viswanathan, S., Tasic, B., Bas, E., Winnubst, J., Menon, V., Graybuck, L.T., Nguyen, 
T.N., Smith, K.A., Yao, Z., Wang, L., Gerfen, C.R., Chandrashekar, J., Zeng, H., Looger, L.L., & Svoboda, 
K. (2018). Distinct descending motor cortex pathways and their roles in movement. Nature, 563(7729), 79–
84. https.//www:nature.com/articles/s41586-018-0642-9 http.//doi:org/10.1038/s41586-018-0642-9
Fiebelkorn, I.C., & Kastner, S. (2021). Spike timing in the attention network predicts behavioral outcome 
prior to target selection. Neuron, 109, 177-188.e4. https.//www:sciencedirect.com/science/article/pii/
S0896627320307637 http.//doi:org/10.1016/j.neuron.2020.09.039
Goodell, D.J., Tullis, J.E., & Bayer, K.U. (2021). Young DAPK1 knockout mice have altered presynaptic 
function. Journal of Neurophysiology, 125, 1973–1981. http.//journals.physiology.org/doi/full/10.1152/
jn.00055.2021 http.//doi:org/10.1152/jn.00055.2021
Goodell, D.J., Zaegel, V., Coultrap, S.J., Hell, J.W., & Bayer, K.U. (2017). DAPK1 mediates LTD by 
making CaMKII/GluN2B binding LTP specific. Cell Reports, 19, 2231–2243. http.//
www:sciencedirect.com/science/article/pii/S2211124717307258 http.//doi:org/10.1016/j.celrep.2017.05.068
He, K., Huertas, M., Hong, S.Z., Tie, X., Hell, J.W., Shouval, H., & Kirkwood, A. (2015). Distinct 
eligibility traces for LTP and LTD in cortical synapses. Neuron, 88, 528–538. https.//
www:sciencedirect.com/science/article/pii/S0896627315008260 http.//doi:org/10.1016/
j.neuron.2015.09.037
Heilbron, M., & Chait, M. (2018). Great Expectations: Is there Evidence for Predictive Coding in Auditory 
Cortex? Neuroscience, 389, 54–73. https.//www:sciencedirect.com/science/article/pii/S030645221730547X 
http.//doi:org/10.1016/j.neuroscience.2017.07.061
Hinton, G.E., & McClelland, J.L. (1988). Learning representations by recirculation. In D.Z. Anderson 
(Ed.), Neural Information Processing Systems (NIPS 1987 (pp. 358–366)) American Institute of Physics. 
http.//papers.nips.cc/paper/78-learning-representations-by-recirculation.pdf

This is how the Neocortex Learns 11
Hopfield, J.J., & Tank, D.W. (1985). {`Neural'} computation of decisions in optimization problems. 
Biological Cybernetics, 52, 141–152. http.//www:ncbi.nlm.nih.gov/pubmed/4027280
Intrator, N., & Cooper, L.N. (1992). Objective function formulation of the BCM theory of visual cortical 
plasticity: Statistical connections, stability conditions. Neural Networks, 5, 3–17. https.//
www:sciencedirect.com/science/article/pii/S0893608005800036 http.//doi:org/10.1016/
S0893-6080(05)80003-6
Izhikevich, E.M., & Desai, N.S. (2003). Relating STDP to BCM. Neural computation, 15, 1511–1524. 
http.//www:ncbi.nlm.nih.gov/pubmed/12816564
Jang, J., Flores, J.C., Zito, K., & O'Reilly, R.C. (2026). Synaptic Plasticity as a Function of the Temporal 
Derivative. 2026.06.05.730489. https.//www:biorxiv.org/content/10.64898/2026.06.05.730489v1 http.//
doi:org/10.64898/2026.06.05.730489
Khan, M.E., & Rue, H. (2023). The Bayesian Learning Rule. Journal of Machine Learning Research, 24, 
1–46. http.//jmlr.org/papers/v24/22-0291.html
Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., & Masquelier, T. (2018). STDP-based spiking deep 
convolutional neural networks for object recognition. Neural Networks, 99, 56–67. https.//
www:sciencedirect.com/science/article/pii/S0893608017302903 http.//doi:org/10.1016/
j.neunet.2017.12.005
Kuramoto, E., Ohno, S., Furuta, T., Unzai, T., Tanaka, Y.R., Hioki, H., & Kaneko, T. (2015). Ventral medial 
nucleus neurons send thalamocortical afferents more widely and more preferentially to layer 1 than 
neurons of the ventral anterior–ventral lateral nuclear complex in the rat. Cerebral Cortex, 25, 221–235. 
https.//academic.oup.com/cercor/article/25/1/221/369709 http.//doi:org/10.1093/cercor/bht216
Le Cun, Y. (1986). Learning Process in an Asymmetric Threshold Network. In E. Bienenstock, F.F. Soulié, 
& G. Weisbuch (Eds.), Disordered Systems and Biological Organization (pp. 233–240). Springer.  http.//
doi:org/10.1007/978-3-642-82657-3_24
Lillicrap, T.P., Santoro, A., Marris, L., Akerman, C.J., & Hinton, G. (2020). Backpropagation and the brain. 
Nature Reviews Neuroscience, 21(6), 335–346. https.//www:nature.com/articles/s41583-020-0277-3 http.//
doi:org/10.1038/s41583-020-0277-3
Lisman, J. (1989). A mechanism for the Hebb and the anti-Hebb processes underlying learning and 
memory. Proceedings of the National Academy of Sciences, 86, 9574–9578. https.//www:pnas.org/doi/abs/
10.1073/pnas.86.23.9574 http.//doi:org/10.1073/pnas.86.23.9574
Magee, J.C. (2026). Behavioral timescale synaptic plasticity: properties, elements and functions. Nature 
Neuroscience, 29, 520–534. https.//www:nature.com/articles/s41593-026-02214-2 http.//doi:org/10.1038/
s41593-026-02214-2
Markov, N.T., Ercsey-Ravasz, M., Lamy, C., Ribeiro Gomes, A.R., Magrou, L., Misery, P., Giroud, P., 
Barone, P., Dehay, C., Toroczkai, Z., Knoblauch, K., Van Essen, D.C., & Kennedy, H. (2013). The role of 
long-range connections on the specificity of the macaque interareal cortical network. Proceedings of the 
National Academy of Sciences U. S. A., 110, 5187–5192. http.//www:ncbi.nlm.nih.gov/pubmed/23479610
Marr, D. (1982). Vision.  Freeman.

This is how the Neocortex Learns 12
McClelland, J.L., McNaughton, B.L., & O'Reilly, R.C. (1995). Why There Are Complementary Learning 
Systems in the Hippocampus and Neocortex: Insights from the Successes and Failures of Connectionist 
Models of Learning and Memory. Psychological Review, 102, 419–457. http.//www:ncbi.nlm.nih.gov/
pubmed/7624455
Oja, E. (1982). A simplified neuron model as a principal component analyzer. Journal of mathematical 
biology, 15, 267–273. http.//www:ncbi.nlm.nih.gov/pubmed/7153672
O'Reilly, R.C. (1996). Biologically plausible error-driven learning using local activation differences: The 
generalized recirculation algorithm. Neural Computation, 8, 895–938. https.//www:mitpressjournals.org/
doi/abs/10.1162/neco.1996.8.5.895 http.//doi:org/https.//doi:org/10.1162/neco.1996.8.5.895
O'Reilly, R.C., Bhattacharyya, R., Howard, M.D., & Ketz, N. (2014). Complementary Learning Systems. 
Cognitive Science, 38, 1229–1248. http.//www:ncbi.nlm.nih.gov/pubmed/22141588
O'Reilly, R.C., Russin, J.L., Zolfaghar, M., & Rohrlich, J. (2021). Deep Predictive Learning in Neocortex 
and Pulvinar. Journal of Cognitive Neuroscience, 33, 1158–1196. https.//doi:org/10.1162/jocn_a_01708 
http.//doi:org/10.1162/jocn_a_01708
O'Reilly, R.C., Wyatte, D., Herd, S.A., Mingus, B., & Jilk, D.J. (2013). Recurrent Processing during Object 
Recognition. Frontiers in Psychology, 4, http.//www:ncbi.nlm.nih.gov/pubmed/23554596
Phillips, J.M., Kambi, N.A., Redinbaugh, M.J., Mohanta, S., & Saalmann, Y.B. (2021). Disentangling the 
influences of multiple thalamic nuclei on prefrontal cortex and cognitive control. Neuroscience & 
Biobehavioral Reviews, 128, 487–510. https.//www:sciencedirect.com/science/article/pii/
S0149763421002955 http.//doi:org/10.1016/j.neubiorev.2021.06.042
Rao, R.P., & Ballard, D.H. (1999). Predictive coding in the visual cortex: A functional interpretation of 
some extra-classical receptive-field effects. Nature Neuroscience, 2, 79–87. http.//www:ncbi.nlm.nih.gov/
pubmed/10195184 http.//doi:org/10.1038/4580
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning representations by back-propagating 
errors. Nature, 323, 533–536.
Rumelhart, D.E., & Zipser, D. (1985). Feature discovery by competitive learning* Cognitive Science, 9, 75–
112. http.//onlinelibrary.wiley.com/doi/10.1207/s15516709cog0901_5/abstract http.//doi:org/10.1207/
s15516709cog0901_5
Scellier, B., & Bengio, Y. (2017). Equilibrium propagation: Bridging the gap between energy-based models 
and backpropagation. Frontiers in Computational Neuroscience, 11, http.//www:ncbi.nlm.nih.gov/pmc/
articles/PMC5415673/ http.//doi:org/10.3389/fncom.2017.00024
Sherman, S.M., & Guillery, R.W. (2006). Exploring the Thalamus and Its Role in Cortical Function.  MIT 
Press. http.//www:scholarpedia.org/article/Thalamus
Sherman, S.M., & Usrey, W.M. (2024). Transthalamic Pathways for Cortical Function. Journal of 
Neuroscience, 44, https.//www:jneurosci.org/content/44/35/e0909242024 http.//doi:org/10.1523/
JNEUROSCI.0909-24.2024
Shouval, H.Z., & Kirkwood, A. (2025). Eligibility traces as a synaptic substrate for learning. Current 
Opinion in Neurobiology, 91, 102978. https.//www:sciencedirect.com/science/article/pii/
S0959438825000091 http.//doi:org/10.1016/j.conb.2025.102978

This is how the Neocortex Learns 13
Shouval, H.Z., Wang, S.S., & Wittenberg, G.M. (2010). Spike timing dependent plasticity: A consequence 
of more fundamental learning rules. Frontiers in Computational Neuroscience, 4, http.//
www:ncbi.nlm.nih.gov/pubmed/20725599
Tanaka, G., Yamane, T., Héroux, J.B., Nakane, R., Kanazawa, N., Takeda, S., Numata, H., Nakano, D., & 
Hirose, A. (2019). Recent advances in physical reservoir computing: A review. Neural Networks, 115, 100–
123. https.//www:sciencedirect.com/science/article/pii/S0893608019300784 http.//doi:org/10.1016/
j.neunet.2019.03.005
Tullis, J.E., & Bayer, K.U. (2023). Distinct synaptic pools of DAPK1 differentially regulate activity-
dependent synaptic CaMKII accumulation. iScience, 26, https.//www:cell.com/iscience/abstract/
S2589-0042(23)00800-3 http.//doi:org/10.1016/j.isci.2023.106723
Usrey, W.M., & Sherman, S.M. (2018). Corticofugal circuits: Communication lines from the cortex to the 
rest of the brain. Journal of Comparative Neurology, 0, https.//onlinelibrary.wiley.com/doi/abs/10.1002/
cne.24423 http.//doi:org/10.1002/cne.24423
Vaidya, S.P., Li, G., Chitwood, R.A., Li, Y., & Magee, J.C. (2025). Formation of an expanding memory 
representation in the hippocampus. Nature Neuroscience, 28, 1510–1518. https.//www:nature.com/articles/
s41593-025-01986-3 http.//doi:org/10.1038/s41593-025-01986-3
Van Essen, D.C., & Maunsell, J.H.R. (1983). Hierarchical organization and functional streams in the visual 
cortex. Trends in Neurosciences, 6, 370–375.
Vastola, J., Gershman, S.J., & Rajan, K. (2026). Gradient Descent as Loss Landscape Navigation: a 
Normative Framework for Deriving Learning Rules. Advances in Neural Information Processing Systems, 
38, 119609–119650. https.//proceedings.neurips.cc/paper_files/paper/2025/hash/
ad557daf1552a14dd0c26c11d3a72676-Abstract-Conference.html
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser,  ., & Polosukhin, I. 
(2017). Attention is all you need. In I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. 
Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 30 (pp. 5998–
6008). Curran Associates, Inc. http.//papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
Verstraeten, D., Schrauwen, B., D'Haene, M., & Stroobandt, D. (2007). An experimental unification of 
reservoir computing methods. Neural Networks, 20, 391–403. http.//www:ncbi.nlm.nih.gov/pubmed/
17517492 http.//doi:org/10.1016/j.neunet.2007.04.003
Walsh, K.S., McGovern, D.P., Clark, A., & O'Connell, R.G. (2020). Evaluating the neurophysiological 
evidence for predictive processing as a model of perception. Annals of the New York Academy of Sciences, 
1464, 242–268. https.//www:ncbi.nlm.nih.gov/pmc/articles/PMC7187369/ http.//doi:org/10.1111/
nyas.14321
Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. 
[unpublished thesis, Harvard University].
Widrow, B., & Hoff, M.E. (1960). Adaptive Switching Circuits. In Institute of Radio Engineers, Western 
Electronic Show and Convention, Convention Record, Part 4 (pp. 96–104).
Xiao, K., Li, Y., Sullivan, B.J., Li, G., & Magee, J.C. (2025). Rapid neocortical network modifications via 
dendritic plateau potential induced plasticity. 2025.11.19.689338. https.//www:biorxiv.org/content/
10.1101/2025.11.19.689338v1 http.//doi:org/10.1101/2025.11.19.689338

This is how the Neocortex Learns 14
Xiao, D., Zikopoulos, B., & Barbas, H. (2009). Laminar and modular organization of prefrontal projections 
to multiple thalamic nuclei. Neuroscience, 161, 1067–1081. http.//www:sciencedirect.com/science/article/
pii/S0306452209006411 http.//doi:org/10.1016/j.neuroscience.2009.04.034
Xie, X., & Seung, H.S. (2003). Equivalence of backpropagation and Contrastive Hebbian Learning in a 
layered network. Neural Computation, 15, 441–454. http.//www:ncbi.nlm.nih.gov/pubmed/12590814
Yaeger, C.E., Soto-Albors, R.M., Liu, W., Beltramini, A., & Harnett, M.T. (2025). Plateau potentials are 
instructive signals for behavioral timescale synaptic plasticity in the neocortex. 2025.11.07.687250. https.//
www:biorxiv.org/content/10.1101/2025.11.07.687250v1 http.//doi:org/10.1101/2025.11.07.687250