Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Inhibitory suppression of heterogeneously tuned excitation enhances spatial coding in CA1 place cells

Abstract

Place cells in the CA1 region of the hippocampus express location-specific firing despite receiving a steady barrage of heterogeneously tuned excitatory inputs that should compromise output dynamic range and timing. We examined the role of synaptic inhibition in countering the deleterious effects of off-target excitation. Intracellular recordings in behaving mice demonstrate that bimodal excitation drives place cells, while unimodal excitation drives weaker or no spatial tuning in interneurons. Optogenetic hyperpolarization of interneurons had spatially uniform effects on place cell membrane potential dynamics, substantially reducing spatial selectivity. These data and a computational model suggest that spatially uniform inhibitory conductance enhances rate coding in place cells by suppressing out-of-field excitation and by limiting dendritic amplification. Similarly, we observed that inhibitory suppression of phasic noise generated by out-of-field excitation enhances temporal coding by expanding the range of theta phase precession. Thus, spatially uniform inhibition allows proficient and flexible coding in hippocampal CA1 by suppressing heterogeneously tuned excitation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Predicted effects of spatially tuned inhibition in a simple model of synaptic integration.
Figure 2: Combined LFP and whole-cell recordings from pyramidal neurons and local GABAergic interneurons in awake, head-fixed mice.
Figure 3: Optogenetic suppression of local GABAergic interneurons.
Figure 4: Inhibitory modulation of spatial selectivity in CA1 place cells.
Figure 5: Modeling inhibitory modulation of CA1 place cell dynamics.
Figure 6: Summation of phase-precessing CA3 inputs with variable tuning limits inheritance of phase precession by CA1.
Figure 7: Inhibition expands the range of phase precession in CA1 place cells.

Similar content being viewed by others

References

  1. Moser, E.I., Kropff, E. & Moser, M.B. Place cells, grid cells, and the brain's spatial representation system. Annu. Rev. Neurosci. 31, 69–89 (2008).

    Article  CAS  PubMed  Google Scholar 

  2. Harvey, C.D., Collman, F., Dombeck, D.A. & Tank, D.W. Intracellular dynamics of hippocampal place cells during virtual navigation. Nature 461, 941–946 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bittner, K.C. et al. Conjunctive input processing drives feature selectivity in hippocampal CA1 neurons. Nat. Neurosci. 18, 1133–1142 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lee, D., Lin, B.J. & Lee, A.K. Hippocampal place fields emerge upon single-cell manipulation of excitability during behavior. Science 337, 849–853 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Bloss, E.B. et al. Structured dendritic inhibition supports branch-selective integration in CA1 pyramidal cells. Neuron 89, 1016–1030 (2016).

    Article  CAS  PubMed  Google Scholar 

  6. Gidon, A. & Segev, I. Principles governing the operation of synaptic inhibition in dendrites. Neuron 75, 330–341 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Milstein, A.D. et al. Inhibitory Gating of Input Comparison in the CA1 Microcircuit. Neuron 87, 1274–1289 (2015).

    Article  CAS  PubMed  Google Scholar 

  8. Lovett-Barron, M. et al. Regulation of neuronal input transformations by tunable dendritic inhibition. Nat. Neurosci. 15, 423–430 (2012).

    Article  CAS  PubMed  Google Scholar 

  9. Cutsuridis, V. & Hasselmo, M. GABAergic contributions to gating, timing, and phase precession of hippocampal neuronal activity during theta oscillations. Hippocampus 22, 1597–1621 (2012).

    Article  CAS  PubMed  Google Scholar 

  10. Burgess, N. & O'Keefe, J. Models of place and grid cell firing and theta rhythmicity. Curr. Opin. Neurobiol. 21, 734–744 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Shadlen, M.N. & Newsome, W.T. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18, 3870–3896 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hansel, D. & van Vreeswijk, C. The mechanism of orientation selectivity in primary visual cortex without a functional map. J. Neurosci. 32, 4049–4064 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Widloski, J. & Fiete, I.R. A model of grid cell development through spatial exploration and spike time-dependent plasticity. Neuron 83, 481–495 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Mariño, J. et al. Invariant computations in local cortical networks with balanced excitation and inhibition. Nat. Neurosci. 8, 194–201 (2005).

    Article  PubMed  CAS  Google Scholar 

  15. Wehr, M. & Zador, A.M. Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature 426, 442–446 (2003).

    Article  CAS  PubMed  Google Scholar 

  16. Wilson, R.I. & Nicoll, R.A. Endogenous cannabinoids mediate retrograde signalling at hippocampal synapses. Nature 410, 588–592 (2001).

    Article  CAS  PubMed  Google Scholar 

  17. Bock, D.D. et al. Network anatomy and in vivo physiology of visual cortical neurons. Nature 471, 177–182 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hofer, S.B. et al. Differential connectivity and response dynamics of excitatory and inhibitory neurons in visual cortex. Nat. Neurosci. 14, 1045–1052 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Vong, L. et al. Leptin action on GABAergic neurons prevents obesity and reduces inhibitory tone to POMC neurons. Neuron 71, 142–154 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Royer, S. et al. Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition. Nat. Neurosci. 15, 769–775 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Somogyi, P., Katona, L., Klausberger, T., Lasztóczi, B. & Viney, T.J. Temporal redistribution of inhibition over neuronal subcellular domains underlies state-dependent rhythmic change of excitability in the hippocampus. Phil. Trans. R. Soc. Lond. B 369, 20120518 (2013).

    Article  Google Scholar 

  22. Anderson, J.S., Lampl, I., Gillespie, D.C. & Ferster, D. The contribution of noise to contrast invariance of orientation tuning in cat visual cortex. Science 290, 1968–1972 (2000).

    Article  CAS  PubMed  Google Scholar 

  23. Kuhn, A., Aertsen, A. & Rotter, S. Neuronal integration of synaptic input in the fluctuation-driven regime. J. Neurosci. 24, 2345–2356 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mizuseki, K., Sirota, A., Pastalkova, E. & Buzsáki, G. Theta oscillations provide temporal windows for local circuit computation in the entorhinal-hippocampal loop. Neuron 64, 267–280 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chadwick, A., van Rossum, M.C. & Nolan, M.F. Independent theta phase coding accounts for CA1 population sequences and enables flexible remapping. Elife 4, 03542 (2015).

    Article  Google Scholar 

  26. Geisler, C. et al. Temporal delays among place cells determine the frequency of population theta oscillations in the hippocampus. Proc. Natl. Acad. Sci. USA 107, 7957–7962 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Harnett, M.T., Makara, J.K., Spruston, N., Kath, W.L. & Magee, J.C. Synaptic amplification by dendritic spines enhances input cooperativity. Nature 491, 599–602 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Losonczy, A. & Magee, J.C. Integrative properties of radial oblique dendrites in hippocampal CA1 pyramidal neurons. Neuron 50, 291–307 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Chance, F.S. Hippocampal phase precession from dual input components. J. Neurosci. 32, 16693–16703 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Jaramillo, J., Schmidt, R. & Kempter, R. Modeling inheritance of phase precession in the hippocampal formation. J. Neurosci. 34, 7715–7731 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cardin, J.A., Palmer, L.A. & Contreras, D. Stimulus feature selectivity in excitatory and inhibitory neurons in primary visual cortex. J. Neurosci. 27, 10333–10344 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ego-Stengel, V. & Wilson, M.A. Spatial selectivity and theta phase precession in CA1 interneurons. Hippocampus 17, 161–174 (2007).

    Article  PubMed  Google Scholar 

  33. Wilent, W.B. & Nitz, D.A. Discrete place fields of hippocampal formation interneurons. J. Neurophysiol. 97, 4152–4161 (2007).

    Article  PubMed  Google Scholar 

  34. Marshall, L. et al. Hippocampal pyramidal cell-interneuron spike transmission is frequency dependent and responsible for place modulation of interneuron discharge. J. Neurosci. 22, RC197 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Frank, L.M., Brown, E.N. & Wilson, M.A. A comparison of the firing properties of putative excitatory and inhibitory neurons from CA1 and the entorhinal cortex. J. Neurophysiol. 86, 2029–2040 (2001).

    Article  CAS  PubMed  Google Scholar 

  36. Csicsvari, J., Hirase, H., Czurko, A. & Buzsáki, G. Reliability and state dependence of pyramidal cell-interneuron synapses in the hippocampus: an ensemble approach in the behaving rat. Neuron 21, 179–189 (1998).

    Article  CAS  PubMed  Google Scholar 

  37. Megías, M., Emri, Z., Freund, T.F. & Gulyás, A.I. Total number and distribution of inhibitory and excitatory synapses on hippocampal CA1 pyramidal cells. Neuroscience 102, 527–540 (2001).

    Article  PubMed  Google Scholar 

  38. Li, X.G., Somogyi, P., Ylinen, A. & Buzsáki, G. The hippocampal CA3 network: an in vivo intracellular labeling study. J. Comp. Neurol. 339, 181–208 (1994).

    Article  CAS  PubMed  Google Scholar 

  39. Gulyás, A.I., Megías, M., Emri, Z. & Freund, T.F. Total number and ratio of excitatory and inhibitory synapses converging onto single interneurons of different types in the CA1 area of the rat hippocampus. J. Neurosci. 19, 10082–10097 (1999).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Bezaire, M.J. & Soltesz, I. Quantitative assessment of CA1 local circuits: knowledge base for interneuron-pyramidal cell connectivity. Hippocampus 23, 751–785 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Colgin, L.L., Moser, E.I. & Moser, M.B. Understanding memory through hippocampal remapping. Trends Neurosci. 31, 469–477 (2008).

    Article  CAS  PubMed  Google Scholar 

  42. Caporale, N. & Dan, Y. Spike timing-dependent plasticity: a Hebbian learning rule. Annu. Rev. Neurosci. 31, 25–46 (2008).

    Article  CAS  PubMed  Google Scholar 

  43. Takahashi, H. & Magee, J.C. Pathway interactions and synaptic plasticity in the dendritic tuft regions of CA1 pyramidal neurons. Neuron 62, 102–111 (2009).

    Article  CAS  PubMed  Google Scholar 

  44. Grienberger, C., Chen, X. & Konnerth, A. NMDA receptor-dependent multidendrite Ca2+ spikes required for hippocampal burst firing in vivo. Neuron 81, 1274–1281 (2014).

    Article  CAS  PubMed  Google Scholar 

  45. Mizuseki, K., Royer, S., Diba, K. & Buzsáki, G. Activity dynamics and behavioral correlates of CA3 and CA1 hippocampal pyramidal neurons. Hippocampus 22, 1659–1680 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Stuart, G.J. & Spruston, N. Dendritic integration: 60 years of progress. Nat. Neurosci. 18, 1713–1721 (2015).

    Article  CAS  PubMed  Google Scholar 

  47. Markram, H. et al. Interneurons of the neocortical inhibitory system. Nat. Rev. Neurosci. 5, 793–807 (2004).

    Article  CAS  PubMed  Google Scholar 

  48. Xu, N.L. et al. Nonlinear dendritic integration of sensory and motor input during an active sensing task. Nature 492, 247–251 (2012).

    Article  CAS  PubMed  Google Scholar 

  49. Gambino, F. et al. Sensory-evoked LTP driven by dendritic plateau potentials in vivo. Nature 515, 116–119 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. Yu, J., Gutnisky, D.A., Hires, S.A. & Svoboda, K. Layer 4 fast-spiking interneurons filter thalamocortical signals during active somatosensation. Nat. Neurosci. 19, 1647–1657 (2016).

    Article  CAS  PubMed  Google Scholar 

  51. Madisen, L. et al. A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing. Nat. Neurosci. 15, 793–802 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Google Scholar 

  53. Frey, B.J. & Dueck, D. Clustering by passing messages between data points. Science 315, 972–976 (2007).

    Article  CAS  PubMed  Google Scholar 

  54. Borst, A. & Theunissen, F.E. Information theory and neural coding. Nat. Neurosci. 2, 947–957 (1999).

    Article  CAS  PubMed  Google Scholar 

  55. Skaggs, W.E., McNaughton, B.L., Gothard, K.M. & Markus, E.J. An information-theoretic approach to deciphering the hippocampal code. Adv. Neural Inf. Process. Syst. 5, 1030–1037 (1993).

    Google Scholar 

  56. Hines, M.L., Davison, A.P. & Muller, E. NEURON and Python. Front. Neuroinform. 3, 1 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Leutgeb, S., Leutgeb, J.K., Treves, A., Moser, M.B. & Moser, E.I. Distinct ensemble codes in hippocampal areas CA3 and CA1. Science 305, 1295–1298 (2004).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank J. Osborne and S. Sawtelle for assistance in designing the experimental set up, W.-L. Sun and J. Macklin for developing the LFP fiber probe, M. Copeland for histology, D. Linaro and D. Jin for contributing software tools, J. Lou for help with the treadmill diagram, and members of the Spruston laboratory for discussions. This work was supported by the Howard Hughes Medical Institute.

Author information

Authors and Affiliations

Authors

Contributions

C.G. and J.C.M. designed the research. C.G. and K.C.B. performed in vivo recordings. C.G. and J.C.M. analyzed the experimental data. A.D.M. designed and implemented the computational model with critical input from J.C.M. and S.R. A.D.M. developed software and analyzed the results of the numerical simulations. All authors wrote the manuscript.

Corresponding author

Correspondence to Jeffrey C Magee.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Characterization of VGAT-ires-CrexAi35D and GAD2-ires-CrexAi35D mice.

(a) Fluorescence in situ hybridization in the dorsal hippocampus of the VGAT-ires-Cre driver line. Shown are probes labeling vgat (green), gad2 (red) and cre (blue). 20 μm-thick cryostat sections from perfusion-fixed brains were used for RNAscope fluorescent triple-label in situ hybridization. (b) Magnified view of the region depicted in a. (c-d) Same as a-b for the GAD2-ires-Cre driver line. This experiment was repeated twice for each genotype. (e) Comparison of place field sizes, derived from the firing rate (left) and the slow Vm (right). Place field size was calculated as the fraction of the linear track between two threshold (20% of the peak value of the activity map) crossings (n=32 place cells from 17 VGAT-ires-Cre x Ai35D animals and n=13 place cells from 10 GAD2-ires-Cre x Ai35D animals; firing rate: 44±3 vs. 52±5%, P = 0.157; slow Vm: 64±3 vs. 64±5%, P = 0.897). (f) Comparison of place field characteristics; left: in-field firing rate (8.1±0.8 vs. 9.4±1.8 Hz, P = 0.528); middle: in-field Δslow Vm (6.7±0.7 vs. 6.6±1.3 mV, P = 0.578); right: in-field intracellular theta Vm envelope (2.05±0.10 vs. 2.13±0.19 mV, P = 0.872). Grey circles represent individual neurons. Black lines represent mean ± s.e.m. Significance assessed with Wilcoxon rank sum tests. ns: not significant.

Supplementary Figure 2 Representative examples of firing rate maps.

(a) Pre-existing place cells. (b) Experimentally induced place cells. (c) Spatially selective interneurons. Shown are only laps without current injection or light application (lap numbers indicated on the left). Color maps show firing rates in Hz. #Experiment indicated in the upper left corner of the firing rate map.

Supplementary Figure 3 Characterization of interneuron subtypes: putative axo-axonic cell.

(a) Left: raw Vm trace (black) and extracellular theta oscillation (green) for 5 consecutive laps. Middle: Enlarged view of an AP with spike amplitude and spike width (mean ± s.e.m.). Right: Vm after AP removal. (b) Spatial modulation. Left: firing rate. Grey lines illustrate individual laps and the mean across laps is shown in black. Red line indicates Gaussian fit. Middle: mean slow Vm. Right: mean intracellular theta envelope. (c) AP firing properties. Left: ISI distribution. Middle: Theta phase preference. Red line indicates cosine fit. Right: AP frequency distribution. Bin size is 140 ms. (d) Subthreshold Vm characteristics. Left: Vm distribution. Bin size is 0.2 mV. Vm distributions were calculated from the raw Vm trace after AP removal. Middle: Power spectral density analysis with theta-to-gamma ratio. Inset shows enlarged view of the AP-deleted Vm trace indicating the presence of fluctuations in the theta and gamma-oscillation range. Right: Vm autocorrelation. (e) SPW/R-associated activity. Left: raw Vm trace and band-pass filtered (100-250 Hz) LFP (asterisk indicates ripple). Middle: Enlarged view. Right: mean number of APs and ΔVm associated with sharp wave/ripples. Shown are data from the neuron in a.

Supplementary Figure 4 Characterization of interneuron subtypes: putative basket cell.

(a) Left: raw Vm trace (black) and extracellular theta oscillation (green) for 5 consecutive laps. Middle: Enlarged view of an AP with spike amplitude and spike width (mean ± s.e.m.). Right: Vm after AP removal. (b) Spatial modulation. Left: firing rate. Grey lines illustrate individual laps and the mean across laps is shown in black. Red line indicates Gaussian fit. Middle: mean slow Vm. Right: mean intracellular theta envelope. (c) AP firing properties. Left: ISI distribution. Middle: Theta phase preference. Red line indicates cosine fit. Right: AP frequency distribution. Bin size is 140 ms. (d) Subthreshold Vm characteristics. Left: Vm distribution. Bin size is 0.2 mV. Vm distributions were calculated from the raw Vm trace after AP removal. Middle: Power spectral density analysis with theta-to-gamma ratio. Inset shows enlarged view of the AP-deleted Vm trace indicating the presence of fluctuations in the theta and gamma-oscillation range. Right: Vm autocorrelation. (e) SPW/R-associated activity. Left: raw Vm trace and band-pass filtered (100-250 Hz) LFP (asterisk indicates ripple). Middle: Enlarged view. Right: mean number of APs and ΔVm associated with sharp wave/ripples. Shown are data from the neuron in a.

Supplementary Figure 5 Characterization of interneuron subtypes: putative bistratified cell.

(a) Left: raw Vm trace (black) and extracellular theta oscillation (green) for 5 consecutive laps. Middle: Enlarged view of an AP with spike amplitude and spike width (mean ± s.e.m.). Right: Vm after AP removal. (b) Spatial modulation. Left: firing rate. Grey lines illustrate individual laps and the mean across laps is shown in black. Red line indicates Gaussian fit. Middle: mean slow Vm. Right: mean intracellular theta envelope. (c) AP firing properties. Left: ISI distribution. Middle: Theta phase preference. Red line indicates cosine fit. Right: AP frequency distribution. Bin size is 140 ms. (d) Subthreshold Vm characteristics. Left: Vm distribution. Bin size is 0.2 mV. Vm distributions were calculated from the raw Vm trace after AP removal. Middle: Power spectral density analysis with theta-to-gamma ratio. Inset shows enlarged view of the AP-deleted Vm trace indicating the presence of fluctuations in the theta and gamma-oscillation range. Right: Vm autocorrelation. (e) SPW/R-associated activity. Left: raw Vm trace and band-pass filtered (100-250 Hz) LFP (asterisk indicates ripple). Middle: Enlarged view. Right: mean number of APs and ΔVm associated with sharp wave/ripples. Shown are data from the neuron in a.

Supplementary Figure 6 Characterization of interneuron subtypes: putative O-LM cell.

(a) Left: raw Vm trace (black) and extracellular theta oscillation (green) for 5 consecutive laps. Middle: Enlarged view of an AP with spike amplitude and spike width (mean ± s.e.m.). Right: Vm after AP removal. (b) Spatial modulation. Left: firing rate. Grey lines illustrate individual laps and the mean across laps is shown in black. Red line indicates cosine fit. Middle: mean slow Vm. Right: mean intracellular theta envelope. (c) AP firing properties. Left: ISI distribution. Middle: Theta phase preference. Red line indicates cosine fit. Right: AP frequency distribution. Bin size is 140 ms. (d) Subthreshold Vm characteristics. Left: Vm distribution. Bin size is 0.2 mV. Vm distributions were calculated from the raw Vm trace after AP removal. Middle: Power spectral density analysis with theta-to-gamma ratio. Inset shows enlarged view of the AP-deleted Vm trace indicating the presence of fluctuations in the theta and gamma-oscillation range. Right: Vm autocorrelation. (e) SPW/R-associated activity. Left: raw Vm and band-pass filtered (100-250 Hz) LFP (asterisk indicates ripple). Middle: Enlarged view. Right: mean number of APs and ΔVm associated with sharp wave/ripples. Shown are data from the neuron in a.

Supplementary Figure 7 Comparison of interneuron subtypes.

Quantified are AP theta phase preference, Vm frequency modulation, Vm autocorrelation, and SPW/R-associated activity for each of the following putative interneuron subtypes: axo-axonic cells (n=3), basket cells (n=6), bistratified cells (n=11), and O-LM cells (n=7).

Supplementary Figure 8 Spatial modulation of CA1 interneurons.

(a) Representative examples for spatially unmodulated interneurons. (b) 7 spatially modulated interneurons were identified in our dataset of 27 interneurons. (a-b) Grey lines illustrate individual laps and the mean across laps is shown in black. Red lines indicate the 95% confidence intervals of the shuffled (1000 times) data. Asterisks mark locations (>3.6 cm, at least 3 spatial bins) that exceed the 95% confidence intervals of the shuffled data. (c) Spatial information per neuron (bits) for place cells and interneurons. (d) Information rate per neuron (left: bits per second, right: bits per spike) for place cells and interneurons.

Supplementary Figure 9 Effect of interneuron silencing on pyramidal cell activity.

(a) Activity maps (100 spatial bins of 1.8 cm each) for slow Vm depolarization (Δslow Vm, relative to baseline Vm), intracellular theta oscillation (theta Vm) envelope and firing rate. Top: control, bottom: Arch activation in interneurons. (b) Comparisons of Δslow Vm (left), theta Vm envelope (middle) and firing rate (right) with and without light. Shown are averages of ten spatial bins totaling 18 cm centered either at the peak of the place field (n=32 neurons; Δslow Vm: 6.2±0.7 vs. 9.2±0.7 mV, P = 2.6E-8; theta Vm: 1.91±0.08 vs. 2.25±0.08 mV, P = 0.0003; firing rate: 7.5±0.8 vs. 15.2±1.1 Hz, P = 4.7E-9) or at the minimum value (n=29 neurons; Δslow Vm: 0.3±0.1 vs. 3.1±0.4 mV, P = 9.3E-8; theta Vm: 1.07±0.04 vs. 1.38±0.08 mV, P = 1.4E-5; firing rate: 0.5±0.1 vs. 3.3±0.8 Hz, P = 2.3E-5). Grey circles represent individual neurons. Black and orange circles represent the population mean for control and light condition, respectively. (c) Comparison of changes in Δslow Vm and theta Vm induced by light stimulation between periods before and after experimental induction of place fields in silent cells (n=7; Δslow Vm in field: 3.0±0.3 vs. 3.1±0.6 mV, P = 0.998; Δslow Vm out of field: 3.3±0.3 vs. 3.1±0.5 mV, P = 0.688; theta Vm in field: 0.27±0.025 vs. 0.31±0.037 mV, P = 0.688; theta Vm out of field: 0.26±0.058 vs. 0.25±0.038 mV, P = 0.938). Values represent mean ± s.e.m. Significance assessed with Wilcoxon signed rank tests. Asterisks denote significant difference (p<0.05). ns: not significant.

Supplementary Figure 10 Comparison of inhibitory modulation of spatial selectivity in pre-existing and experimentally induced CA1 place cells.

(a) Pre-existing place cells. Mean activity maps (Δslow Vm, theta Vm, and firing rate) aligned to the firing field center (n=10-12 neurons; Δslow Vm P <0.05 for all spatial bins; theta Vm P <0.05 for bins 1 and 3-10, bin 2 P = 0.519; firing rate P <0.05 for bins 1 and 3-10, bin 2 P = 0.058) and comparison of Vm variance between position-matched periods with and without light (in field: n=11 neurons, 6.39±0.71 vs. 8.97±1.05, P = 0.001; out of field: n=11 neurons, 3.46±0.40 vs. 5.93±1.03, P = 0.007). (b) Induced place cells. Mean activity maps (Δslow Vm, theta Vm, and firing rate) aligned to the firing field center (n=14-20 neurons; Δslow Vm P <0.001 for all spatial bins; theta Vm P <0.05 for bins 1-9, bin 10 P = 0.268; firing rate P <0.05 for all spatial bins) and comparison of Vm variance between position-matched periods with and without light (in field: n=20 neurons, 6.99±0.49 vs. 9.64±0.61, P = 0.0003; out of field: n=13 neurons, 2.86±0.27 vs. 4.41±0.65, P = 0.005). (a-b) Grey circles represent individual neurons, black and orange circles represent population averages for control and light conditions, respectively. Significance assessed with Wilcoxon signed rank tests. Exact n and P values in Supplementary Table 3. (c) Difference in Δslow Vm, theta Vm, firing rate and Vm variance between control and light condition in pre-existing vs. induced place cells. Compared are averages of ten spatial bins totaling 18 cm centered either at the peak of the place field (Δslow Vm: 2.8±0.5 (n=12) vs. 3.2±0.6 mV (n=20), P = 0.774; theta Vm: 0.48±0.12 (n=12) vs. 0.26±0.11 mV (n=20), P = 0.158; firing rate: 8.0±1.9 (n=12) vs. 7.4±1.1 Hz (n=20), P = 0.985; Vm variance: 2.6±0.6 (n=11) vs. 2.6±0.6 (n=20), P = 0.792) or at the minimum value (Δslow Vm: 2.6±0.6 (n=13) vs. 2.8±0.5 mV (n=16); P = 0.714; theta Vm: 0.35±0.11 (n=13) vs. 0.27±0.09 mV (n=16), P = 0.619; firing rate: 3.2±1.5 (n=13) vs. 2.5±0.9 Hz (n=16), P = 0.846; Vm variance: 2.5±1.0 (n=11) vs. 1.6±0.5 (n=13), P = 0.424). Grey circles represent individual neurons. Significance assessed with Wilcoxon rank sum tests. Data shown as mean ± s.e.m.; asterisks denote significant difference (p<0.05); ns: not significant.

Supplementary Figure 11 Effect of interneuron silencing on input resistance and Vm autocorrelation.

(a-b) Current waveforms and resulting raw Vm traces (black) and Vm traces after AP removal (red) for one control (a) and one light-on (b) lap. Top: extracellular theta oscillation (green, theta LFP). Shaded orange region depicts light period. (c) Input resistance (Rn) as a function of theta phase. Data was binned in 10 bins of 36˚ each. Shown are mean Rn values (± s.e.m.) obtained for control and light periods (n=12 neurons; bin 1-7&9 P <0.05, bin 8 P = 0.244, bin 10 P = 0.122; exact P values in Supplementary Table 3). Two theta cycles are shown for better visibility. (d) Input resistance (Rn) as a function of position relative to the firing field center. Data was binned in 10 bins of 18 cm each. Shown are mean Rn values (± s.e.m.) obtained for control and light periods and pooled across all theta phases. Number of neurons included in each data point is indicated below the traces (for bin 1-10 P <0.05; exact P values in Supplementary Table 3). (e) In field and out of field Vm autocorrelation; black: control, orange: light. (f) Mean in field and out-of-field Vm autocorrelations and exponential fits for control (black) and light (orange) periods. Time constants τ are derived from exponential fits. (g) Population data for all neurons (in field: n=31, 40±2 vs. 46±2 ms, P = 0.004; out of field: n=24, 34±2 vs. 40±2 ms, P = 0.008). Grey circles represent individual neurons. Black and orange circles represent mean values (± s.e.m.) for control and light conditions, respectively. Significance assessed with Wilcoxon signed rank tests. Asterisks denote significant difference (p<0.05). ns: not significant.

Supplementary Figure 12 Excitatory and inhibitory synaptic inputs in the computational model of CA1 place cells.

(a) Circuit diagram depicts CA1 pyramidal neurons embedded in a local network containing six classes of inhibitory interneurons, and receiving two major excitatory input pathways. Each of the excitatory and inhibitory inputs to pyramidal neurons was modeled as targeting different somatodendritic domains. (b) The number of synapses made by each input source onto each type of subcellular compartment for a representative example simulated CA1 place cell.

Supplementary Figure 13 Modeling inhibitory modulation of CA1 place cell dynamics.

(a) Simulations run in the absence of amplification of synaptic signals by NMDA-Rs or Na+ channels (Fig. 5g) required that the number of AMPA-R-only excitatory synapses be approximately doubled in order to generate a comparable ΔVm (out: -0.014±0.0245 vs. 1.519±0.045, t(4)=38.674, P <0.0001; in: 5.457±0.0440 vs. 7.049±0.058, t(4)=55.004, P <0.0001). This manipulation resulted in decreases in theta Vm (out: 0.644±0.011 vs. 0.878±0.0298, t(4)=7.0963, P = 0.002083; in: 1.204±0.0325 vs. 1.402±0.041, t(4)=4.5917, P = 0.01009). (b-c) Probing somatic membrane input resistance in the model with step current injections revealed a weak dependence of input resistance on theta phase (b) consistent with inhibitory conductance peaking at the trough of extracellular theta (180°), and a weak dependence on spatial location (c), resulting from changes in the activation of voltage-dependent membrane conductances during increased spiking in field. Reducing inhibitory conductance in the model resulted in increases in apparent input resistance (mean ± s.e.m. across all theta phases and spatial bins: 44.715±0.280 (black) vs. 51.906±0.344 (orange), t(4)=15.584, P <0.0001). (a-c) Shown are averages across 5 simulated CA1 place cells (10 trials per condition per cell), with shading indicating ± s.e.m. Significance was assessed using two-tailed paired t-tests. (d) Shown are representative example traces from the model for recordings of the following parameters: soma Vm in and out of field (black), spine Vm (green) for individual spines either receiving a CA3 input with a place field located either outside (left) or inside (right) that of the CA1 place cell, branch Vm (red) for the parent branches of the corresponding spines, and currents through AMPA-Rs (cyan) and NMDA-Rs (black) in the corresponding spines. (e) In the model, complete removal of synaptic inhibition results in persistent AP firing throughout the simulated track, with bouts of somatic Na+-channel inactivation.

Supplementary Figure 14 Adding noise to CA1 place cell simulations.

(a-b) Traces depict simulated raw membrane potential (Vm) (black) and filtered Vm after AP removal (red: slow Vm, < 2Hz; blue: theta Vm, 5-10 Hz) recorded from the model place cell during simulated traversal of a linear track. Shown are recordings from individual trials either in the control condition (a), or with inhibitory firing rates reduced (b), with additional ~0.8 Hz theta-modulated background excitation from each active CA3 and ECIII input, resulting in increased spiking out of field compared to the results in Fig 5. Orange bar indicates period of reduced inhibition. (c) Mean activity maps for slow Vm depolarization, (left, Δslow Vm, relative to baseline Vm, out: 0.068±0.093 vs. 3.265±0.107, t(4)=60.72, P <0.0001; in: 6.550±0.076 vs. 10.239±0.080, t(4)=46.249, P <0.0001) and intracellular theta oscillation envelope (middle, theta Vm, out: 1.346±0.048 vs. 2.120±0.096, t(4)=7.001, P = 0.002191; in: 2.519±0.068 vs. 3.272±0.098, t(4)=6.658, P = 0.002643) obtained from the model (black: control; orange: reduced inhibition). (d-e) Mean activity map for AP firing rate (right, out: 0.983±0.226 vs. 9.658±0.400, t(4)=23.779, P <0.0001; in: 28.678±0.333 vs. 55.025±0.375, t(4)=47.621, P <0.0001). Same data is depicted at a different scale in (e). (f) Mean activity map for variance of simulated Vm fluctuations recorded from model place cells (mean ± s.e.m., out: 2.123±0.060 vs. 4.386±0.134, t(4)=17.068, P <0.0001; in: 5.508±0.090 vs. 7.647±0.174, t(4)=10.034, P = 0.0005546). (c-f) Solid lines and shading represent mean ± s.e.m. across five simulated cells (10 trials per condition per cell). Significance was assessed from data in 600 ms bins in or out of field using two-tailed paired t-tests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14, Supplementary Tables 1–3, and Supplementary Modeling (PDF 7809 kb)

Supplementary Methods Checklist (PDF 647 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Grienberger, C., Milstein, A., Bittner, K. et al. Inhibitory suppression of heterogeneously tuned excitation enhances spatial coding in CA1 place cells. Nat Neurosci 20, 417–426 (2017). https://doi.org/10.1038/nn.4486

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn.4486

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing