In the brain, groups of neurons that are activated together – also known as neuronal ensembles – are the basic units that underpin perception and behavior. Yet, exactly how these coactive circuits are established remains under investigation.

In 1949, Canadian psychologist Donald Hebb proposed that, when brains learn something new, the neurons which are activated together connect to form ensembles, and their connections become stronger each time this specific piece of knowledge is recalled. This idea that ‘neurons that fire together, wire together’ can explain how memories are acquired and recalled, by strengthening their wiring.

However, recent studies have questioned whether strengthening connections is the only mechanism by which neural ensembles can be created. Changes in the excitability of neurons (how easily they are to fire and become activated) may also play a role. In other words, ensembles could emerge because certain neurons become more excitable and fire more readily.

To solve this conundrum, Alejandre-García et al. examined both hypotheses in the same system. Neurons in slices of the mouse visual cortex were stimulated electrically or optically, via a technique that controls neural activity with light. The activity of individual neurons and their connections was then measured with electrodes.

Spontaneous activity among connected neurons increased after stimulation, indicative of the formation of neuronal ensembles. Connected neurons also showed small changes in the strength of their connections, which first decreased and then rebounded after an initial recovery period.

Intriguingly, cells also showed unexpected strong and persistent increases in neuronal excitability after stimulation, such that neurons fired more readily to the same stimulus. In other words, neurons maintained a cellular memory of having been stimulated. The authors conclude that ensembles form because connected neurons become more excitable, which in turn, may strengthen connections of the circuit at a later stage.

These results provide fresh insights about the neural circuits underpinning learning and memory. In time, the findings could also help to understand disorders such as Alzheimer’s disease and schizophrenia, which are characterised by memory impairments and disordered thinking.

The function of the brain is anchored on the activity of groups of connected neurons, forming microcircuits (Schüz and Braitenberg, 2001; Shepherd, 2004). In such circuits, groups of coactive neurons, or ensembles (also known as assemblies, synfire chains, or attractors), found in both spontaneous and evoked activity, exhibit synchronous or correlated activity (Abeles, 1991; Buzsáki, 2010; Carrillo-Reid et al., 2016; Cossart et al., 2003; Morris, 1999; Hopfield, 1982; Lorente De Nó, 1938; Marshel et al., 2019; Miller et al., 2014; Yuste, 2015) and could mediate memory formation (Morris, 1999), and functional brain states (Hoshiba et al., 2017). In the cerebral cortex, ensembles could arise from reverberating patterns of multineural activity, generated by recurrent connectivity, and occur spontaneously (Churchland and Sejnowski, 1992; Lorente De Nó, 1933). In mouse visual cortex, cortical ensembles are causally related to memory storage and percepts (Carrillo-Reid et al., 2019; Marshel et al., 2019; Miller et al., 2014). Moreover, disorganized neuronal ensembles are observed in mouse models of schizophrenia (Hamm et al., 2017).

A hypothesis of how a neuronal ensemble (i.e. a neuronal assembly) could be formed was initially proposed by Hebb in 1949. Ensembles could be formed by synaptic plasticity among coactive neurons, as a consequence of the Hebbian rule, whereby the “persistent and repeated activation of connected neurons induce metabolic changes or growth of processes in one or both cells that strengthen those connections”. One synaptic mechanism that fulfills Hebb’s rule is long-term potentiation (LTP), a long-lasting increase in synaptic efficacy after a high frequency burst electrical stimulation, which is postulated to be the cellular correlate of learning (Bliss and Gardner-Medwin, 1973). With LTP, increased synaptic weights enhances preferential connectivity that leads to an increased recurrent activity within ensembles (Hoshiba et al., 2017). Consistent with this, synchronous stimulation of groups of neurons 50–100 times in vivo, using two-photon optogenetics, bound them together into an ‘imprinted’ ensemble that became spontaneously coactive after the optogenetic imprinting (Carrillo-Reid et al., 2016). However, no direct evidence has yet demonstrated that the formation of ensembles depends on changes in synaptic connections.

As an alternative hypothesis, recent experiments have revealed widespread activity-dependent mechanisms in cortical neurons, suggesting that the formation and stability of an ensemble, or a memory engram, could be due to cell-autonomous intrinsic mechanisms, such as changes in neuronal excitability (Debanne and Poo, 2010; Gallistel and Balsam, 2014; Hansel and Disterhoft, 2020; Lisman et al., 2018; Titley et al., 2017). This hypothesis is consistent with experiments where specific patterns of activity in the hippocampus recalls stored memories, under protein synthesis inhibitors, which should prevent LTP (Pignatelli et al., 2019; Ryan et al., 2015; Tonegawa et al., 2015). Moreover, individual Purkinje cells can acquire and represent temporal pattern of inputs in a cell intrinsic manner, without synaptic plasticity (Johansson et al., 2014). Thus, ensemble formation could be due to intrinsic changes in the excitability state of the neurons, instead of, or in addition to, strengthening of its synapses (Disterhoft and Oh, 2006; Ganguly et al., 2000; Pignatelli et al., 2019).

To explore mechanisms of ensemble formation, we optogenetic and electrically stimulated neurons in mouse neocortical slices, replicating in vitro the optogenetic protocol used to imprint ensembles in vivo (Carrillo-Reid et al., 2016). We then used whole-cell and perforated patch recordings, from individual neurons and connected pairs, to electrophysiologically characterize the stimulated neurons. We find moderate biphasic changes in synaptic strength, in the midst of major and generalized increases in cell-intrinsic excitability, that can, at least partly, explain the synaptic changes observed. We conclude that changes in excitability play a major role in ensemble formation.

Since Hebb, the traditional view of how a neuronal ensemble (i.e., an assembly) is created by strengthening its synaptic connections after repeated coactivation (Bliss and Gardner-Medwin, 1973; Carrillo-Reid et al., 2016; Cossell et al., 2015; Morris, 1999; Hoshiba et al., 2017). However, several reports have questioned whether synaptic plasticity is the only mechanism for circuit plasticity, proposing instead cell-intrinsic mechanisms (Angelo et al., 2012; Brown et al., 2019; Disterhoft and Oh, 2006; Ganguly et al., 2000; Marder and Goaillard, 2006; Ryan et al., 2015; Zhang and Linden, 2003). Consistent with this, an in contradiction with our own predictions that synaptic plasticity mediated neuronal ensembles (Carrillo-Reid et al., 2016), we describe here that simultaneous optogenetic or electrical stimulation of neurons in vitro increases the excitability of the stimulated cells. The increases in correlation among neurons, generated by an increased excitability, could also contribute to generate or enhance long-term synaptic plasticity (Lisman et al., 2018; Titley et al., 2017). Together, cellular and synaptic mechanisms could contribute to neuronal ensemble development (Penn and Shatz, 1999; Zhang and Poo, 2001), flexibility, plasticity (Clopath et al., 2017; Kirkwood et al., 1996; Mrsic-Flogel et al., 2007; Pérez-Ortega et al., 2021; Wandell and Smirnakis, 2009; Wiesel and Hubel, 1963).

Role of synaptic mechanisms in ensemble formation

Our experimental procedures, similar to those that build (‘imprint’) neuronal ensembles in vivo (Carrillo-Reid et al., 2016), lead to increases in correlations of spontaneous activity, a hallmark of a neuronal ensemble, just as it does in vivo. Using paired recordings, we find similar rates of connectivity between neuronal pairs as previous reports (Cossell et al., 2015; Holmgren et al., 2003; Ko et al., 2011; Lefort et al., 2009; Levy and Reyes, 2012; Markram et al., 1997; Song et al., 2005). Also, consistent with many reports, connected neurons display higher correlation in spontaneous activity than unconnected ones (Cossell et al., 2015; Ko et al., 2011; Yoshimura et al., 2005). Interestingly, our results also showed that 50% of unconnected neurons can be as correlated as connected neurons, and contrary to our own expectations, we did not detect any new connections formed after optogenetic stimulation in previously unconnected neurons.

Based on previous studies, there is preferential connectivity between neurons with similar receptive fields (Cossell et al., 2015; Ko et al., 2011; Yoshimura et al., 2005). Increased synaptic connectivity also probably underlies pattern completion in cortical ensembles (Carrillo-Reid et al., 2019; Pignatelli et al., 2019), and a few strong connections could drive local excitation of the majority of neurons with weak connectivity (Cossell et al., 2015; Holmgren et al., 2003; Ko et al., 2011; Lefort et al., 2009; Levy and Reyes, 2012; Markram et al., 1997; Song et al., 2005; Figure 8). Consistent with these hypotheses, we expected to observe synaptic efficiency changes between connected neurons after co-stimulation (Bliss and Gardner-Medwin, 1973). But, rather than synaptic potentiation, our stimulation protocols induced a moderate biphasic plasticity of connected neurons and also in the local circuit, with an initial depression followed by a recovery potentiation of evoked EPSCs and EPSPs after 20 min of rest.

Figure 8

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This result agrees with reports that EPSP amplitude can decrease after high stimulation frequencies, but recovers from depression by using lower stimulation rates and, after a period of rest can rebound which is revealed as a potentiation (Feldmeyer et al., 2006). We cannot rule out that Hebbian synaptic plasticity contributes to this potentiation. However, an alternative explanation is that this could instead be due to a depletion, followed by a recovery, of the ready releasable pool of synaptic vesicles in the presynaptic neuron (Feldmeyer et al., 2006; Schneggenburger et al., 2002).

In addition to these synaptic changes, our results demonstrate that persistent and simultaneous stimulation robustly increases membrane resistance. Thus, neuronal excitability could increase the efficiency of existing synaptic connections between neurons in an ensemble, without significant synaptic plasticity. Indeed, we find that spontaneous EPSPs become amplified in single optogenetic and electrically stimulated neurons. A larger synaptic input could also naturally result from an overall increase in neuronal firing too, since increased neuronal excitability would result in increase in neurotransmitter release or amplified EPSPs. This agrees with reports that reveal that synaptic inputs can be enhanced by intrinsic membrane mechanisms (Campanac and Debanne, 2008; Hu et al., 2002; Li et al., 2020; Manabe et al., 1992; Marder and Goaillard, 2006; Narayanan and Johnston, 2008; Turrigiano et al., 1998). For all these reasons, we suspect the modest synaptic plasticity that we observe can be explained by the effects of the stimulation protocols in the ready releasable pool and intrinsic changes in excitability in the stimulated neurons and the network.

Primary visual cortex (V1) responds to sensory information. Thus, one of the main roles of V1 neurons is to integrate sensory inputs (Buonomano and Merzenich, 1998; Mountcastle, 1997; Figure 8). Unitary EPSP, that is, pyramidal to pyramidal monosynaptic connections, would not be expected to increase the firing probability of postsynaptic neurons, in vitro or in vivo (Holmgren et al., 2003; Jouhanneau et al., 2018). However, the joint activation of several neurons in the local circuit (in our case: neighbor neurons with opsin expression), could evoke compound EPSC and EPSP with amplitudes larger than unitary EPSC or EPSP, and could generate synfire chains of activity that propagate through the cortex (Abeles, 1991). Thus, increases in synaptic efficacy, mediated by an increase in neuronal excitability of connected neurons, could play a significant role in the local circuit, improving the efficiency of the flow of the information during visual stimulation and during the recall of neurons associated with a memory.

All procedures were performed by following the U.S. National Institutes of Health and Columbia University Institutional Animal Care and Use Committee guidelines (IACUC, Protocol #AC-AAAV3464). Experiments were carried out on C57BL/6 transgenic mice (Vglut1-Cre, Jackson Laboratories; RRID:IMSR_JAX:00064) and electroporated mice CD-1 (Charles River) of both sexes at postnatal 4–8 weeks-old. Animals were housed on a 12 hr light-dark cycle with food and water ad libitum.

Data have been deposited with Dryad (https://doi.org/10.5061/dryad.j6q573ngc).

1. 1. Alejandre-García T
   2. Kim S
   3. Pérez-Ortega J
   4. Yuste R

   (2022) Dryad Digital Repository

   Intrinsic excitability mechanisms of neuronal ensemble formation.

   https://doi.org/10.5061/dryad.j6q573ngc

1. Book

   1. Abeles M

   (1991) Corticonics

   Cambridge University Press.

   https://doi.org/10.1017/CBO9780511574566

   • Google Scholar
2. 1. Abraham WC
   2. Jones OD
   3. Glanzman DL

   (2019) Is plasticity of synapses the mechanism of long-term memory storage?

   NPJ Science of Learning 4:9.

   https://doi.org/10.1038/s41539-019-0048-y

   • PubMed
   • Google Scholar
3. 1. Aizenman CD
   2. Linden DJ

   (2000) Rapid, synaptically driven increases in the intrinsic excitability of cerebellar deep nuclear neurons

   Nature Neuroscience 3:109–111.

   https://doi.org/10.1038/72049

   • PubMed
   • Google Scholar
4. 1. Angelo K
   2. Rancz EA
   3. Pimentel D
   4. Hundahl C
   5. Hannibal J
   6. Fleischmann A
   7. Pichler B
   8. Margrie TW

   (2012) A biophysical signature of network affiliation and sensory processing in mitral cells

   Nature 488:375–378.

   https://doi.org/10.1038/nature11291

   • PubMed
   • Google Scholar
5. 1. Aou S
   2. Woody CD
   3. Birt D

   (1992) Increases in excitability of neurons of the motor cortex of cats after rapid acquisition of eye blink conditioning

   The Journal of Neuroscience 12:560–569.

   https://doi.org/10.1523/JNEUROSCI.12-02-00560.1992

   • PubMed
   • Google Scholar
6. 1. Armano S
   2. Rossi P
   3. Taglietti V
   4. D’Angelo E

   (2000) Long-term potentiation of intrinsic excitability at the mossy fiber-granule cell synapse of rat cerebellum

   The Journal of Neuroscience 20:5208–5216.

   https://doi.org/10.1523/JNEUROSCI.20-14-05208.2000

   • PubMed
   • Google Scholar
7. 1. Astman N
   2. Gutnick MJ
   3. Fleidervish IA

   (1998) Activation of protein kinase C increases neuronal excitability by regulating persistent Na+ current in mouse neocortical slices

   Journal of Neurophysiology 80:1547–1551.

   https://doi.org/10.1152/jn.1998.80.3.1547

   • PubMed
   • Google Scholar
8. 1. Bliss TVP
   2. Gardner-Medwin AR

   (1973) Long-lasting potentiation of synaptic transmission in the dentate area of the unanaestetized rabbit following stimulation of the perforant path

   The Journal of Physiology 232:357–374.

   https://doi.org/10.1113/jphysiol.1973.sp010274

   • PubMed
   • Google Scholar
9. 1. Brown APY
   2. Cossell L
   3. Margrie TW

   (2019) Visual Experience Regulates the Intrinsic Excitability of Visual Cortical Neurons to Maintain Sensory Function

   Cell Reports 27:685–689.

   https://doi.org/10.1016/j.celrep.2019.03.073

   • PubMed
   • Google Scholar
10. 1. Brumberg JC
    2. Nowak LG
    3. McCormick DA

    (2000) Ionic mechanisms underlying repetitive high-frequency burst firing in supragranular cortical neurons

    The Journal of Neuroscience 20:4829–4843.

    https://doi.org/10.1523/JNEUROSCI.20-13-04829.2000

    • PubMed
    • Google Scholar
11. 1. Buonomano DV
    2. Merzenich MM

    (1998) CORTICAL PLASTICITY: from synapses to maps

    Annual Review of Neuroscience 21:149–186.

    https://doi.org/10.1146/annurev.neuro.21.1.149

    • PubMed
    • Google Scholar
12. 1. Buzsáki G

    (2010) Neural syntax: cell assemblies, synapsembles, and readers

    Neuron 68:362–385.

    https://doi.org/10.1016/j.neuron.2010.09.023

    • PubMed
    • Google Scholar
13. 1. Campanac E
    2. Daoudal G
    3. Ankri N
    4. Debanne D

    (2008) Downregulation of dendritic I(h) in CA1 pyramidal neurons after LTP

    The Journal of Neuroscience 28:8635–8643.

    https://doi.org/10.1523/JNEUROSCI.1411-08.2008

    • PubMed
    • Google Scholar
14. 1. Campanac E
    2. Debanne D

    (2008) Spike timing-dependent plasticity: a learning rule for dendritic integration in rat CA1 pyramidal neurons

    The Journal of Physiology 586:779–793.

    https://doi.org/10.1113/jphysiol.2007.147017

    • PubMed
    • Google Scholar
15. 1. Carrillo-Reid L
    2. Yang W
    3. Bando Y
    4. Peterka DS
    5. Yuste R

    (2016) Imprinting and recalling cortical ensembles

    Science (New York, N.Y.) 353:691–694.

    https://doi.org/10.1126/science.aaf7560

    • PubMed
    • Google Scholar
16. 1. Carrillo-Reid L
    2. Han S
    3. Yang W
    4. Akrouh A
    5. Yuste R

    (2019) Controlling Visually Guided Behavior by Holographic Recalling of Cortical Ensembles

    Cell 178:447–457.

    https://doi.org/10.1016/j.cell.2019.05.045

    • PubMed
    • Google Scholar
17. 1. Carter CA
    2. Wojciechowski NJ
    3. Hayes JM
    4. Skoutakis VA
    5. Rickman LA

    (1985) Terfenadine, a Nonsedating Antihistamine

    Drug Intelligence & Clinical Pharmacy 19:812–817.

    https://doi.org/10.1177/106002808501901103

    • Google Scholar
18. Book

    1. Churchland PS
    2. Sejnowski TJ

    (1992) The Computational Brain., The Computational Brain., Computational Neuroscience

    Cambridge, MA, US: The MIT Press.

    https://doi.org/10.7551/mitpress/2010.001.0001

    • Google Scholar
19. 1. Clopath C
    2. Bonhoeffer T
    3. Hübener M
    4. Rose T

    (2017) Variance and invariance of neuronal long-term representations

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 372:20160161.

    https://doi.org/10.1098/rstb.2016.0161

    • PubMed
    • Google Scholar
20. 1. Cossart R
    2. Aronov D
    3. Yuste R

    (2003) Attractor dynamics of network UP states in the neocortex

    Nature 423:283–288.

    https://doi.org/10.1038/nature01614

    • PubMed
    • Google Scholar
21. 1. Cossell L
    2. Iacaruso MF
    3. Muir DR
    4. Houlton R
    5. Sader EN
    6. Ko H
    7. Hofer SB
    8. Mrsic-Flogel TD

    (2015) Functional organization of excitatory synaptic strength in primary visual cortex

    Nature 518:399–403.

    https://doi.org/10.1038/nature14182

    • PubMed
    • Google Scholar
22. 1. Cudmore RH
    2. Turrigiano GG

    (2004) Long-term potentiation of intrinsic excitability in LV visual cortical neurons

    Journal of Neurophysiology 92:341–348.

    https://doi.org/10.1152/jn.01059.2003

    • PubMed
    • Google Scholar
23. 1. Cui ED
    2. Strowbridge BW

    (2018) Modulation of Ether-à-Go-Go Related Gene (ERG) Current Governs Intrinsic Persistent Activity in Rodent Neocortical Pyramidal Cells

    The Journal of Neuroscience 38:423–440.

    https://doi.org/10.1523/JNEUROSCI.1774-17.2017

    • PubMed
    • Google Scholar
24. 1. dal Maschio M
    2. Ghezzi D
    3. Bony G
    4. Alabastri A
    5. Deidda G
    6. Brondi M
    7. Sato SS
    8. Zaccaria RP
    9. Di Fabrizio E
    10. Ratto GM
    11. Cancedda L

    (2012) High-performance and site-directed in utero electroporation by a triple-electrode probe

    Nature Communications 3:960.

    https://doi.org/10.1038/ncomms1961

    • PubMed
    • Google Scholar
25. 1. Debanne D
    2. Poo M-M

    (2010) Spike-timing dependent plasticity beyond synapse - pre- and post-synaptic plasticity of intrinsic neuronal excitability

    Frontiers in Synaptic Neuroscience 2:21.

    https://doi.org/10.3389/fnsyn.2010.00021

    • PubMed
    • Google Scholar
26. 1. Debanne D
    2. Inglebert Y
    3. Russier M

    (2019) Plasticity of intrinsic neuronal excitability

    Current Opinion in Neurobiology 54:73–82.

    https://doi.org/10.1016/j.conb.2018.09.001

    • PubMed
    • Google Scholar
27. 1. Desai NS
    2. Rutherford LC
    3. Turrigiano GG

    (1999) Plasticity in the intrinsic excitability of cortical pyramidal neurons

    Nature Neuroscience 2:515–520.

    https://doi.org/10.1038/9165

    • PubMed
    • Google Scholar
28. 1. Disterhoft JF
    2. Coulter DA
    3. Alkon DL

    (1986) Conditioning-specific membrane changes of rabbit hippocampal neurons measured in vitro

    PNAS 83:2733–2737.

    https://doi.org/10.1073/pnas.83.8.2733

    • PubMed
    • Google Scholar
29. 1. Disterhoft JF
    2. Oh MM

    (2006) Learning, aging and intrinsic neuronal plasticity

    Trends in Neurosciences 29:587–599.

    https://doi.org/10.1016/j.tins.2006.08.005

    • PubMed
    • Google Scholar
30. 1. Fan Y
    2. Fricker D
    3. Brager DH
    4. Chen X
    5. Lu HC
    6. Chitwood RA
    7. Johnston D

    (2005) Activity-dependent decrease of excitability in rat hippocampal neurons through increases in I(h

    Nature Neuroscience 8:1542–1551.

    https://doi.org/10.1038/nn1568

    • PubMed
    • Google Scholar
31. 1. Feldmeyer D
    2. Lübke J
    3. Sakmann B

    (2006) Efficacy and connectivity of intracolumnar pairs of layer 2/3 pyramidal cells in the barrel cortex of juvenile rats

    The Journal of Physiology 575:583–602.

    https://doi.org/10.1113/jphysiol.2006.105106

    • PubMed
    • Google Scholar
32. 1. Feria Pliego JA
    2. Pedroarena CM

    (2020) Kv1 potassium channels control action potential firing of putative GABAergic deep cerebellar nuclear neurons

    Scientific Reports 10:6954.

    https://doi.org/10.1038/s41598-020-63583-7

    • PubMed
    • Google Scholar
33. 1. Gallistel CR
    2. Balsam PD

    (2014) Time to rethink the neural mechanisms of learning and memory

    Neurobiology of Learning and Memory 108:136–144.

    https://doi.org/10.1016/j.nlm.2013.11.019

    • PubMed
    • Google Scholar
34. 1. Ganguly K
    2. Kiss L
    3. Poo M

    (2000) Enhancement of presynaptic neuronal excitability by correlated presynaptic and postsynaptic spiking

    Nature Neuroscience 3:1018–1026.

    https://doi.org/10.1038/79838

    • PubMed
    • Google Scholar
35. 1. Hamm JP
    2. Peterka DS
    3. Gogos JA
    4. Yuste R

    (2017) Altered Cortical Ensembles in Mouse Models of Schizophrenia

    Neuron 94:153–167.

    https://doi.org/10.1016/j.neuron.2017.03.019

    • PubMed
    • Google Scholar
36. 1. Hansel C
    2. Disterhoft JF

    (2020) Why is synaptic plasticity not enough?

    Neurobiology of Learning and Memory 176:107336.

    https://doi.org/10.1016/j.nlm.2020.107336

    • PubMed
    • Google Scholar
37. 1. Holmgren C
    2. Harkany T
    3. Svennenfors B
    4. Zilberter Y

    (2003) Pyramidal cell communication within local networks in layer 2/3 of rat neocortex

    The Journal of Physiology 551:139–153.

    https://doi.org/10.1113/jphysiol.2003.044784

    • PubMed
    • Google Scholar
38. 1. Hopfield JJ

    (1982) Neural networks and physical systems with emergent collective computational abilities

    PNAS 79:2554–2558.

    https://doi.org/10.1073/pnas.79.8.2554

    • PubMed
    • Google Scholar
39. 1. Hoshiba Y
    2. Wada T
    3. Hayashi-Takagi A

    (2017) Synaptic Ensemble Underlying the Selection and Consolidation of Neuronal Circuits during Learning

    Frontiers in Neural Circuits 11:12.

    https://doi.org/10.3389/fncir.2017.00012

    • PubMed
    • Google Scholar
40. 1. Hu H
    2. Vervaeke K
    3. Storm JF

    (2002) Two forms of electrical resonance at theta frequencies, generated by M-current, h-current and persistent Na+ current in rat hippocampal pyramidal cells

    The Journal of Physiology 545:783–805.

    https://doi.org/10.1113/jphysiol.2002.029249

    • PubMed
    • Google Scholar
41. 1. Ikegaya Y
    2. Aaron G
    3. Cossart R
    4. Aronov D
    5. Lampl I
    6. Ferster D
    7. Yuste R

    (2004) Synfire chains and cortical songs: temporal modules of cortical activity

    Science (New York, N.Y.) 304:559–564.

    https://doi.org/10.1126/science.1093173

    • PubMed
    • Google Scholar
42. 1. Johansson F
    2. Jirenhed DA
    3. Rasmussen A
    4. Zucca R
    5. Hesslow G

    (2014) Memory trace and timing mechanism localized to cerebellar Purkinje cells

    PNAS 111:14930–14934.

    https://doi.org/10.1073/pnas.1415371111

    • PubMed
    • Google Scholar
43. 1. Jouhanneau JS
    2. Kremkow J
    3. Poulet JFA

    (2018) Single synaptic inputs drive high-precision action potentials in parvalbumin expressing GABA-ergic cortical neurons in vivo

    Nature Communications 9:1540.

    https://doi.org/10.1038/s41467-018-03995-2

    • PubMed
    • Google Scholar
44. 1. Juárez-Vidales JDJ
    2. Pérez-Ortega J
    3. Lorea-Hernández JJ
    4. Méndez-Salcido F
    5. Peña-Ortega F

    (2021) Configuration and dynamics of dominant inspiratory multineuronal activity patterns during eupnea and gasping generation in vitro

    Journal of Neurophysiology 125:1289–1306.

    https://doi.org/10.1152/jn.00563.2020

    • PubMed
    • Google Scholar
45. 1. Kalmbach BE
    2. Buchin A
    3. Long B
    4. Close J
    5. Nandi A
    6. Miller JA
    7. Bakken TE
    8. Hodge RD
    9. Chong P
    10. de Frates R
    11. Dai K
    12. Maltzer Z
    13. Nicovich PR
    14. Keene CD
    15. Silbergeld DL
    16. Gwinn RP
    17. Cobbs C
    18. Ko AL
    19. Ojemann JG
    20. Koch C
    21. Anastassiou CA
    22. Lein ES
    23. Ting JT

    (2018) h-Channels Contribute to Divergent Intrinsic Membrane Properties of Supragranular Pyramidal Neurons in Human versus Mouse Cerebral Cortex

    Neuron 100:1194–1208.

    https://doi.org/10.1016/j.neuron.2018.10.012

    • PubMed
    • Google Scholar
46. 1. Kirkwood A
    2. Rioult MC
    3. Bear MF

    (1996) Experience-dependent modification of synaptic plasticity in visual cortex

    Nature 381:526–528.

    https://doi.org/10.1038/381526a0

    • PubMed
    • Google Scholar
47. 1. Ko H
    2. Hofer SB
    3. Pichler B
    4. Buchanan KA
    5. Sjöström PJ
    6. Mrsic-Flogel TD

    (2011) Functional specificity of local synaptic connections in neocortical networks

    Nature 473:87–91.

    https://doi.org/10.1038/nature09880

    • PubMed
    • Google Scholar
48. 1. Lefort S
    2. Tomm C
    3. Floyd Sarria JC
    4. Petersen CCH

    (2009) The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex

    Neuron 61:301–316.

    https://doi.org/10.1016/j.neuron.2008.12.020

    • PubMed
    • Google Scholar
49. 1. Levy RB
    2. Reyes AD

    (2012) Spatial profile of excitatory and inhibitory synaptic connectivity in mouse primary auditory cortex

    The Journal of Neuroscience 32:5609–5619.

    https://doi.org/10.1523/JNEUROSCI.5158-11.2012

    • PubMed
    • Google Scholar
50. 1. Li B
    2. Routh BN
    3. Johnston D
    4. Seidemann E
    5. Priebe NJ

    (2020) Voltage-Gated Intrinsic Conductances Shape the Input-Output Relationship of Cortical Neurons in Behaving Primate V1

    Neuron 107:185–196.

    https://doi.org/10.1016/j.neuron.2020.04.001

    • PubMed
    • Google Scholar
51. 1. Lisman JE

    (1997) Bursts as a unit of neural information: making unreliable synapses reliable

    Trends in Neurosciences 20:38–43.

    https://doi.org/10.1016/S0166-2236(96)10070-9

    • PubMed
    • Google Scholar
52. 1. Lisman J
    2. Cooper K
    3. Sehgal M
    4. Silva AJ

    (2018) Memory formation depends on both synapse-specific modifications of synaptic strength and cell-specific increases in excitability

    Nature Neuroscience 21:309–314.

    https://doi.org/10.1038/s41593-018-0076-6

    • PubMed
    • Google Scholar
53. 1. Lorente De Nó R

    (1933)

    Studies on the structure of the cerebral cortex. I. The area entorhinalis

    Journal Für Psychologie Und Neurologie 46:113–177.

    • Google Scholar
54. 1. Lorente De Nó R

    (1938) Analysis of the activity of the chains of internuncial neurons

    Journal of Neurophysiology 1:207–244.

    https://doi.org/10.1152/jn.1938.1.3.207

    • Google Scholar
55. 1. Mahon S
    2. Charpier S

    (2012) Bidirectional plasticity of intrinsic excitability controls sensory inputs efficiency in layer 5 barrel cortex neurons in vivo

    The Journal of Neuroscience 32:11377–11389.

    https://doi.org/10.1523/JNEUROSCI.0415-12.2012

    • PubMed
    • Google Scholar
56. 1. Malik R
    2. Chattarji S

    (2012) Enhanced intrinsic excitability and EPSP-spike coupling accompany enriched environment-induced facilitation of LTP in hippocampal CA1 pyramidal neurons

    Journal of Neurophysiology 107:1366–1378.

    https://doi.org/10.1152/jn.01009.2011

    • PubMed
    • Google Scholar
57. 1. Manabe T
    2. Renner P
    3. Nicoll RA

    (1992) Postsynaptic contribution to long-term potentiation revealed by the analysis of miniature synaptic currents

    Nature 355:50–55.

    https://doi.org/10.1038/355050a0

    • PubMed
    • Google Scholar
58. 1. Marder E
    2. Goaillard JM

    (2006) Variability, compensation and homeostasis in neuron and network function

    Nature Reviews. Neuroscience 7:563–574.

    https://doi.org/10.1038/nrn1949

    • PubMed
    • Google Scholar
59. 1. Mardinly AR
    2. Oldenburg IA
    3. Pégard NC
    4. Sridharan S
    5. Lyall EH
    6. Chesnov K
    7. Brohawn SG
    8. Waller L
    9. Adesnik H

    (2018) Precise multimodal optical control of neural ensemble activity

    Nature Neuroscience 21:881–893.

    https://doi.org/10.1038/s41593-018-0139-8

    • PubMed
    • Google Scholar
60. 1. Markram H
    2. Lübke J
    3. Frotscher M
    4. Roth A
    5. Sakmann B

    (1997) Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex

    The Journal of Physiology 500 (Pt 2):409–440.

    https://doi.org/10.1113/jphysiol.1997.sp022031

    • PubMed
    • Google Scholar
61. 1. Marshel JH
    2. Kim YS
    3. Machado TA
    4. Quirin S
    5. Benson B
    6. Kadmon J
    7. Raja C
    8. Chibukhchyan A
    9. Ramakrishnan C
    10. Inoue M
    11. Shane JC
    12. McKnight DJ
    13. Yoshizawa S
    14. Kato HE
    15. Ganguli S
    16. Deisseroth K

    (2019) Cortical layer-specific critical dynamics triggering perception

    Science (New York, N.Y.) 365:eaaw5202.

    https://doi.org/10.1126/science.aaw5202

    • PubMed
    • Google Scholar
62. 1. Mellor J
    2. Nicoll RA
    3. Schmitz D

    (2002) Mediation of hippocampal mossy fiber long-term potentiation by presynaptic Ih channels

    Science (New York, N.Y.) 295:143–147.

    https://doi.org/10.1126/science.1064285

    • PubMed
    • Google Scholar
63. 1. Miller JK
    2. Ayzenshtat I
    3. Carrillo-Reid L
    4. Yuste R

    (2014) Visual stimuli recruit intrinsically generated cortical ensembles

    PNAS 111:E4053–E4061.

    https://doi.org/10.1073/pnas.1406077111

    • PubMed
    • Google Scholar
64. 1. Morishima M
    2. Morita K
    3. Kubota Y
    4. Kawaguchi Y

    (2011) Highly differentiated projection-specific cortical subnetworks

    The Journal of Neuroscience 31:10380–10391.

    https://doi.org/10.1523/JNEUROSCI.0772-11.2011

    • PubMed
    • Google Scholar
65. 1. Morris RG

    (1999) D.O. Hebb: The Organization of Behavior, Wiley: New York; 1949

    Brain Research Bulletin 50:437.

    https://doi.org/10.1016/s0361-9230(99)00182-3

    • PubMed
    • Google Scholar
66. 1. Mountcastle VB

    (1997) The columnar organization of the neocortex

    Brain 120 (Pt 4):701–722.

    https://doi.org/10.1093/brain/120.4.701

    • PubMed
    • Google Scholar
67. 1. Mrsic-Flogel TD
    2. Hofer SB
    3. Ohki K
    4. Reid RC
    5. Bonhoeffer T
    6. Hübener M

    (2007) Homeostatic regulation of eye-specific responses in visual cortex during ocular dominance plasticity

    Neuron 54:961–972.

    https://doi.org/10.1016/j.neuron.2007.05.028

    • PubMed
    • Google Scholar
68. 1. Narayanan R
    2. Johnston D

    (2008) The h channel mediates location dependence and plasticity of intrinsic phase response in rat hippocampal neurons

    The Journal of Neuroscience 28:5846–5860.

    https://doi.org/10.1523/JNEUROSCI.0835-08.2008

    • PubMed
    • Google Scholar
69. 1. Nicoll A
    2. Larkman A
    3. Blakemore C

    (1993) Modulation of EPSP shape and efficacy by intrinsic membrane conductances in rat neocortical pyramidal neurons in vitro

    The Journal of Physiology 468:693–710.

    https://doi.org/10.1113/jphysiol.1993.sp019795

    • PubMed
    • Google Scholar
70. 1. Pang R
    2. Fairhall AL

    (2019) Fast and flexible sequence induction in spiking neural networks via rapid excitability changes

    eLife 8:e44324.

    https://doi.org/10.7554/eLife.44324

    • PubMed
    • Google Scholar
71. 1. Paz JT
    2. Mahon S
    3. Tiret P
    4. Genet S
    5. Delord B
    6. Charpier S

    (2009) Multiple forms of activity-dependent intrinsic plasticity in layer V cortical neurones in vivo

    The Journal of Physiology 587:3189–3205.

    https://doi.org/10.1113/jphysiol.2009.169334

    • PubMed
    • Google Scholar
72. 1. Penn AA
    2. Shatz CJ

    (1999) Brain waves and brain wiring: the role of endogenous and sensory-driven neural activity in development

    Pediatric Research 45:447–458.

    https://doi.org/10.1203/00006450-199904010-00001

    • PubMed
    • Google Scholar
73. 1. Pérez-Ortega J
    2. Alejandre-García T
    3. Yuste R

    (2021) Long-term stability of cortical ensembles

    eLife 10:e64449.

    https://doi.org/10.7554/eLife.64449

    • PubMed
    • Google Scholar
74. 1. Pignatelli M
    2. Ryan TJ
    3. Roy DS
    4. Lovett C
    5. Smith LM
    6. Muralidhar S
    7. Tonegawa S

    (2019) Engram Cell Excitability State Determines the Efficacy of Memory Retrieval

    Neuron 101:274–284.

    https://doi.org/10.1016/j.neuron.2018.11.029

    • PubMed
    • Google Scholar
75. 1. Ross ST
    2. Soltesz I

    (2001) Long-term plasticity in interneurons of the dentate gyrus

    PNAS 98:8874–8879.

    https://doi.org/10.1073/pnas.141042398

    • PubMed
    • Google Scholar
76. 1. Ryan TJ
    2. Roy DS
    3. Pignatelli M
    4. Arons A
    5. Tonegawa S

    (2015) Memory: Engram cells retain memory under retrograde amnesia

    Science (New York, N.Y.) 348:1007–1013.

    https://doi.org/10.1126/science.aaa5542

    • PubMed
    • Google Scholar
77. 1. Sasaki T
    2. Matsuki N
    3. Ikegaya Y

    (2007) Metastability of active CA3 networks

    The Journal of Neuroscience 27:517–528.

    https://doi.org/10.1523/JNEUROSCI.4514-06.2007

    • PubMed
    • Google Scholar
78. 1. Schneggenburger R
    2. Sakaba T
    3. Neher E

    (2002) Vesicle pools and short-term synaptic depression: lessons from a large synapse

    Trends in Neurosciences 25:206–212.

    https://doi.org/10.1016/s0166-2236(02)02139-2

    • PubMed
    • Google Scholar
79. 1. Schüz A
    2. Braitenberg V

    (2001) Cerebral Cortex: Organization and FunctionInternational Encyclopedia of the Social & Behavioral Sciences

    Elsevier 1:1634–1640.

    https://doi.org/10.1016/B0-08-043076-7/03443-4

    • Google Scholar
80. 1. Sempere-Ferràndez A
    2. Martínez S
    3. Geijo-Barrientos E

    (2019) Synaptic mechanisms underlying the intense firing of neocortical layer 5B pyramidal neurons in response to cortico-cortical inputs

    Brain Structure & Function 224:1403–1416.

    https://doi.org/10.1007/s00429-019-01842-8

    • PubMed
    • Google Scholar
81. Book

    1. Shepherd GM

    (2004) The Synaptic Organization of the Brain, The Synaptic Organization of the Brain

    Oxford University Press.

    https://doi.org/10.1093/acprof:oso/9780195159561.001.1

    • Google Scholar
82. Book

    1. Shepherd G
    2. Grillner S

    (2010) Handbook of Brain Microcircuits

    Oxford University Press.

    https://doi.org/10.1093/med/9780195389883.001.0001

    • Google Scholar
83. 1. Sjöström PJ
    2. Rancz EA
    3. Roth A
    4. Häusser M

    (2008) Dendritic excitability and synaptic plasticity

    Physiological Reviews 88:769–840.

    https://doi.org/10.1152/physrev.00016.2007

    • PubMed
    • Google Scholar
84. 1. Song S
    2. Sjöström PJ
    3. Reigl M
    4. Nelson S
    5. Chklovskii DB

    (2005) Highly nonrandom features of synaptic connectivity in local cortical circuits

    PLOS Biology 3:e68.

    https://doi.org/10.1371/journal.pbio.0030068

    • PubMed
    • Google Scholar
85. 1. Stringer C
    2. Pachitariu M
    3. Steinmetz N
    4. Carandini M
    5. Harris KD

    (2019) High-dimensional geometry of population responses in visual cortex

    Nature 571:361–365.

    https://doi.org/10.1038/s41586-019-1346-5

    • PubMed
    • Google Scholar
86. 1. Ting JT
    2. Lee BR
    3. Chong P
    4. Soler-Llavina G
    5. Cobbs C
    6. Koch C
    7. Zeng H
    8. Lein E

    (2018) Preparation of Acute Brain Slices Using an Optimized

    Journal of Visualized Experiments 1:e825.

    https://doi.org/10.3791/53825

    • Google Scholar
87. 1. Titley HK
    2. Brunel N
    3. Hansel C

    (2017) Toward a Neurocentric View of Learning

    Neuron 95:19–32.

    https://doi.org/10.1016/j.neuron.2017.05.021

    • PubMed
    • Google Scholar
88. 1. Tonegawa S
    2. Pignatelli M
    3. Roy DS
    4. Ryan TJ

    (2015) Memory engram storage and retrieval

    Current Opinion in Neurobiology 35:101–109.

    https://doi.org/10.1016/j.conb.2015.07.009

    • PubMed
    • Google Scholar
89. 1. Turrigiano G
    2. Abbott LF
    3. Marder E

    (1994) Activity-dependent changes in the intrinsic properties of cultured neurons

    Science (New York, N.Y.) 264:974–977.

    https://doi.org/10.1126/science.8178157

    • PubMed
    • Google Scholar
90. 1. Turrigiano GG
    2. Leslie KR
    3. Desai NS
    4. Rutherford LC
    5. Nelson SB

    (1998) Activity-dependent scaling of quantal amplitude in neocortical neurons

    Nature 391:892–896.

    https://doi.org/10.1038/36103

    • PubMed
    • Google Scholar
91. 1. Valero-Aracama MJ
    2. Sauvage MM
    3. Yoshida M

    (2015) Environmental enrichment modulates intrinsic cellular excitability of hippocampal CA1 pyramidal cells in a housing duration and anatomical location-dependent manner

    Behavioural Brain Research 292:209–218.

    https://doi.org/10.1016/j.bbr.2015.05.032

    • PubMed
    • Google Scholar
92. 1. van Aerde KI
    2. Feldmeyer D

    (2015) Morphological and physiological characterization of pyramidal neuron subtypes in rat medial prefrontal cortex

    Cerebral Cortex (New York, N.Y 25:788–805.

    https://doi.org/10.1093/cercor/bht278

    • PubMed
    • Google Scholar
93. 1. Wandell BA
    2. Smirnakis SM

    (2009) Plasticity and stability of visual field maps in adult primary visual cortex

    Nature Reviews. Neuroscience 10:873–884.

    https://doi.org/10.1038/nrn2741

    • PubMed
    • Google Scholar
94. 1. Wiesel TN
    2. Hubel DH

    (1963) EFFECTS OF VISUAL DEPRIVATION ON MORPHOLOGY AND PHYSIOLOGY OF CELLS IN THE CATS LATERAL GENICULATE BODY

    Journal of Neurophysiology 26:978–993.

    https://doi.org/10.1152/jn.1963.26.6.978

    • PubMed
    • Google Scholar
95. 1. Woody CD
    2. Gruen E
    3. Birt D

    (1991) Changes in membrane currents during Pavlovian conditioning of single cortical neurons

    Brain Research 539:76–84.

    https://doi.org/10.1016/0006-8993(91)90688-r

    • PubMed
    • Google Scholar
96. 1. Xu J
    2. Kang N
    3. Jiang L
    4. Nedergaard M
    5. Kang J

    (2005) Activity-dependent long-term potentiation of intrinsic excitability in hippocampal CA1 pyramidal neurons

    The Journal of Neuroscience 25:1750–1760.

    https://doi.org/10.1523/JNEUROSCI.4217-04.2005

    • PubMed
    • Google Scholar
97. 1. Yang Z
    2. Santamaria F

    (2016) Purkinje cell intrinsic excitability increases after synaptic long term depression

    Journal of Neurophysiology 116:1208–1217.

    https://doi.org/10.1152/jn.00369.2016

    • PubMed
    • Google Scholar
98. 1. Yoshimura Y
    2. Dantzker JLM
    3. Callaway EM

    (2005) Excitatory cortical neurons form fine-scale functional networks

    Nature 433:868–873.

    https://doi.org/10.1038/nature03252

    • PubMed
    • Google Scholar
99. 1. Yu XW
    2. Oh MM
    3. Disterhoft JF

    (2017) CREB, cellular excitability, and cognition: Implications for aging

    Behavioural Brain Research 322:206–211.

    https://doi.org/10.1016/j.bbr.2016.07.042

    • PubMed
    • Google Scholar
100. 1. Yuste R

     (2015) From the neuron doctrine to neural networks

     Nature Reviews. Neuroscience 16:487–497.

     https://doi.org/10.1038/nrn3962

     • PubMed
     • Google Scholar
101. 1. Zhang LI
     2. Poo MM

     (2001) Electrical activity and development of neural circuits

     Nature Neuroscience 4 Suppl:1207–1214.

     https://doi.org/10.1038/nn753

     • PubMed
     • Google Scholar
102. 1. Zhang W
     2. Linden DJ

     (2003) The other side of the engram: experience-driven changes in neuronal intrinsic excitability

     Nature Reviews. Neuroscience 4:885–900.

     https://doi.org/10.1038/nrn1248

     • PubMed
     • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Intrinsic excitability mechanisms of neuronal ensemble formation" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Ronald Calabrese as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Christian Hansel (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

The reviewers agreed this paper was impactful and important. Below are the key revisions needed for publication. The reviewers also noted some new experiments that could increase the impact of the conclusions but these are not required for the revision at eLife.

1. It is not entirely clear why optogenetic and electrical stimulations do not lead to identical results in terms of synaptic plasticity in Figure 2 but that optogenetic stimulation in Figure 3 produces synaptic plasticity. Please clarify.

2. A few key references about intrinsic plasticity are missing in this paper. First, Ganguly et al., Nat Neurosci 2000 (and confirmed by Li et al., Neuron 2004) showed that synaptic plasticity induced by pre-and postsynaptic activity was associated with an increase in presynaptic excitability. In addition, Campanac and Debanne J Physiol 2008 showed that positive pre-post correlation induced an increase in postsynaptic firing (that is selective to the paired input) in CA1 pyramidal neurons. These papers should be quoted in this manuscript as they are directly related to the purpose of this manuscript.

3. Line 79 f: It is not clear from this paragraph, which of the cited papers provide experimental details and which one presents the 'alternative hypothesis' (Titley et al., 2017; see above). This should be described more accurately to share the precise status quo of this research field with the audience.

4. In Figure 2, the authors show that both the optical stimulation as well as the electrical stimulation trigger synaptic plasticity, consisting of an immediate depression, followed after a pause by a potentiation. This potentiation is – in the case of electrical stimulation – significantly different from the baseline values (not significant for the opto group, but the number of recordings is quite low). It thus is conceivable that this effect contributes to the enhanced correlation of activity in the network that is shown in Figure 1. While new experiments could better assess whether the enhanced correlation can persist with only excitability changes being available as a cellular mechanism, these are not necessary for publication in eLife. Even so, we would request that the authors discuss this potential issue in the manuscript.

Reviewer #2 (Recommendations for the authors):

I believe that this is a strong paper with a lot of potential for very high impact, but it all depends on whether it can be demonstrated that in the true absence of synaptic gain changes (or with those being at least reduced) the observed ensemble effect persists -- only with intrinsic plasticity as an available cellular mechanism.

This will take a bit of time and work, but it could make the paper a game-changer for plasticity research.

Essential revisions:

The reviewers agreed this paper was impactful and important. Below are the key revisions needed for publication. The reviewers also noted some new experiments that could increase the impact of the conclusions but these are not required for the revision at eLife.

1. It is not entirely clear why optogenetic and electrical stimulations do not lead to identical results in terms of synaptic plasticity in Figure 2 but that optogenetic stimulation in Figure 3 produces synaptic plasticity. Please clarify.

The results are not identical but consistent, and the difference may be due to the low n for perforated recordings from pairs of connected opsin-expressing cells. The optogenetically-stimulated group revealed trend towards depression right after the stimulation, followed with a trend towards potentiation after 20 minutes of rest. These trends occurred in 6 to 7 neurons but were non-significant. These experiments are very challenging, since they involve patch-clamp recordings from connected, opsin-expressing neurons in adult animals. Moreover, based on our electrically stimulation experiments, perforated patch is necessary, making these experiments even more challenging. In 2 of 2 connected pairs with perforated patch from opsin expressing neurons, we observe depression, recovery and potentiation in evoked EPSC after 20 min post-stimulation, similar to electrically stimulated connected pairs and similar to the optogenetic stimulation of the local circuitry. Thus, we strongly suspect that adding more optogenetically stimulated neurons would reveal identical changes in synaptic plasticity as in the electrically stimulated cells. At the same time, finding pairs of healthy connected neurons with opsin expression and record for long periods of time, reduces the probability to success this specific experimental procedure. This is different to electrically stimulated connected pairs and optogenetic stimulation of the local circuit, where the opsin expression in the recorded neuron was not necessary. That is the reason why we buttress our claim on the larger n from electrical stimulated cells, but would still like to include the optogenetically stimulated neurons, with these caveats.

We include these caveats this in the Result section:

"Although average EPSC₁ peak currents of optogenetically stimulated neurons did not show significant changes with simulation, they revealed a trend towards amplitude depression after the stimulation, followed with a tendency towards potentiation, after a 20 minutes rest period…"

"We conclude that electrical coactivation leads to a moderate biphasic change in monosynaptic currents, with an initial depression followed by a potentiation. These results are consistent with the trends found in pairs of optogenetic stimulated neurons, where EPSC₁ amplitude after a post-stimulation recovery period increased from 9 ± 7 pA to 19 ± 3 pA, although no statistical comparison was possible due to the low n (Figure 2C). The difficulty in obtaining perforated whole cell recordings from connected pairs of neurons in opsin expressing cells from adult brain slices precluded us from increasing the n after many attempts. These difficulties were ameliorated in the electrical stimulation experiments.”

2. A few key references about intrinsic plasticity are missing in this paper. First, Ganguly et al., Nat Neurosci 2000 (and confirmed by Li et al., Neuron 2004) showed that synaptic plasticity induced by pre-and postsynaptic activity was associated with an increase in presynaptic excitability. In addition, Campanac and Debanne J Physiol 2008 showed that positive pre-post correlation induced an increase in postsynaptic firing (that is selective to the paired input) in CA1 pyramidal neurons. These papers should be quoted in this manuscript as they are directly related to the purpose of this manuscript.

We thank the reviewer for this suggestion. We have incorporated these references in the manuscript.

3. Line 79 f: It is not clear from this paragraph, which of the cited papers provide experimental details and which one presents the 'alternative hypothesis' (Titley et al., 2017; see above). This should be described more accurately to share the precise status quo of this research field with the audience.

We thank the reviewer for the suggestion. We have adjusted and incorporated the references that correspond and explains with more detail alternative hypothesis and experimental results.

4. In Figure 2, the authors show that both the optical stimulation as well as the electrical stimulation trigger synaptic plasticity, consisting of an immediate depression, followed after a pause by a potentiation. This potentiation is – in the case of electrical stimulation – significantly different from the baseline values (not significant for the opto group, but the number of recordings is quite low). It thus is conceivable that this effect contributes to the enhanced correlation of activity in the network that is shown in Figure 1. While new experiments could better assess whether the enhanced correlation can persist with only excitability changes being available as a cellular mechanism, these are not necessary for publication in eLife. Even so, we would request that the authors discuss this potential issue in the manuscript.

We thank the reviewer for this suggestion, we have incorporated the following paragraphs in the Discussion:

The increases in correlation among neurons, generated by an increased excitability, could also contribute to generate or enhance long-term synaptic plasticity (Lisman et al., 2018; Titley et al., 2017)"

"Primary visual cortex (V1) responds to sensory information. Thus, one of the main roles of V1 neurons is to integrate sensory inputs (Buonomano and Merzenich, 1998; Mountcastle, 1997) (Figure 8). Unitary EPSP, i.e., pyramidal to pyramidal monosynaptic connections, would not be expected to increase the firing probability of postsynaptic neurons, in vitro or in vivo (Holmgren et al., 2003; Jouhanneau et al., 2018). However, the joint activation of several neurons in the local circuit (in our case: neighbour neurons with opsin expression), could evoked compound EPSC and EPSP with amplitudes larger than unitary EPSC or EPSP, and could generate synfire chains of activity that propagate through the cortex (Abeles, 1991). Thus, increases in synaptic efficacy, mediated by an increase in neuronal excitability of connected neurons, could play a significant role in the local circuit, improving the efficiency of the flow of the information during visual stimulation and during the recall of neurons associated with a memory."

Reviewer #2 (Recommendations for the authors):

I believe that this is a strong paper with a lot of potential for very high impact, but it all depends on whether it can be demonstrated that in the true absence of synaptic gain changes (or with those being at least reduced) the observed ensemble effect persists -- only with intrinsic plasticity as an available cellular mechanism.

This will take a bit of time and work, but it could make the paper a game-changer for plasticity research.

We agree with the reviewer that the final dissection of synaptic plasticity vs. excitability could be a game changer. We are nevertheless excited to share these results that point to a major change in excitably, with the field. We are continuing future work in vivo to better define the potential role of synaptic plasticity and evaluating its importance.

Author details

1. Tzitzitlini Alejandre-García

   Department of Biological Sciences, Columbia University, New York, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   at3415@columbia.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2243-8703

2. Samuel Kim

   Department of Biological Sciences, Columbia University, New York, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Jesús Pérez-Ortega

   Department of Biological Sciences, Columbia University, New York, United States

   Contribution

   Conceptualization, Investigation, Software, Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8502-1692

4. Rafael Yuste

   Department of Biological Sciences, Columbia University, New York, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Validation, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4206-497X

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