Cellular mechanisms of spatial navigation in the medial entorhinal cortex

Journal name:
Nature Neuroscience
Volume:
16,
Pages:
325–331
Year published:
DOI:
doi:10.1038/nn.3340
Received
Accepted
Published online

Abstract

Neurons in the medial entorhinal cortex exhibit a grid-like spatial pattern of spike rates that has been proposed to represent a neural code for path integration. To understand how grid cell firing arises from the combination of intrinsic conductances and synaptic input in medial entorhinal stellate cells, we performed patch-clamp recordings in mice navigating in a virtual-reality environment. We found that the membrane potential signature of stellate cells during firing field crossings consisted of a slow depolarization driving spike output. This was best predicted by network models in which neurons receive sustained depolarizing synaptic input during a field crossing, such as continuous attractor network models of grid cell firing. Another key feature of the data, phase precession of intracellular theta oscillations and spiking with respect to extracellular theta oscillations, was best captured by an oscillatory interference model. Thus, these findings provide crucial new information for a quantitative understanding of the cellular basis of spatial navigation in the entorhinal cortex.

At a glance

Figures

  1. Patch-clamp recordings from MEC neurons in navigating mice.
    Figure 1: Patch-clamp recordings from MEC neurons in navigating mice.

    (a) Schematic of the virtual reality setup. (b) Two views in the virtual reality environment along the long axis of the track. (c) Improvement in performance during successive training sessions, assessed by number of equidistantly spaced rewards collected per meter on the virtual reality track. Gray circles represent individual mice and black circles represent the average. (d) Fluorescence image of a biocytin-filled stellate cell in layer II of dorsal MEC. Scale bar represents 50 μm. (e) Sub- and suprathreshold responses to injected current pulses in a stellate cell. The insets show positive and negative membrane potential sag (top; dashed lines indicate steady-state voltage) and an action potential (bottom) at higher magnification. (fh) Bar graphs summarizing resting membrane potential (RMP; −62 ± 1 mV, mean ± s.e.m., (n = 26) versus −58 ± 4 mV (n = 7, P = 0.38) versus −63 ± 1 mV (n = 111, P = 0.22), f), input resistance (Rin; 34 ± 3 M (n = 26) versus 64 ± 9 M (n = 6, P < 0.005) versus 109 ± 4 M (n = 110, P < 10−4), g) and spontaneous firing frequency (f; 0.3 ± 0.1 Hz (n = 16) versus 9.6 ± 5.9 Hz (n = 6, P < 0.05) versus 3.8 ± 0.6 Hz (n = 38, P < 10−4), h) in stellate, fast-spiking (FS) and putative pyramidal neurons of MEC. *P < 0.05 for comparisons with stellate cells. Error bars denote s.e.m.

  2. Stellate cell membrane potential shows no prominent theta periodicity in resting mice.
    Figure 2: Stellate cell membrane potential shows no prominent theta periodicity in resting mice.

    (a) Representative membrane potential traces from six stellate cells recorded in awake, resting mice. Cells were depolarized close to spike threshold by sustained current injections. (b) Autocorrelograms corresponding to the traces shown in a. (c) Lomb spectra corresponding to membrane potential traces in a. Dashed lines indicate a significance value of 0.01. Asterisk indicates significant peaks (P < 0.01) in the theta frequency band (gray shaded area)36.

  3. Stellate cells exhibit theta membrane potential oscillations during running.
    Figure 3: Stellate cells exhibit theta membrane potential oscillations during running.

    (a) Mouse speed (blue) and membrane potential of a stellate cell (black) during a running period. Note that onset of oscillatory activity (black arrow) precedes onset of running (blue arrow). (b) Mouse speed (blue) and membrane potential of another stellate cell (black) during navigation in the virtual reality environment. (c) Membrane potential of a stellate cell and simultaneously recorded LFP when the mouse is resting (black) and during running (red). The traces were taken from the recording shown in b, as indicated by horizontal bars at the bottom of b. (d) Autocorrelograms of membrane potential and LFP during the resting (black) and running (red) periods shown in c. (e) Spectrograms of membrane potential (middle) and LFP (bottom) were normalized and aligned to the onset of movement (top) before computing the average for stellate cells (left, n = 9) and putative pyramidal cells (right, n = 24). (f) Average power spectra for stellate cells (top) and putative pyramidal cells (bottom) at rest (black) and while running (red).

  4. Sustained depolarizations drive stellate cell firing.
    Figure 4: Sustained depolarizations drive stellate cell firing.

    (a) Mouse speed (top), membrane potential of a stellate cell (middle) and LFP (bottom) during a firing field crossing. The corresponding trajectory of the mouse on the virtual reality track (black) with superimposed spikes during right (blue circles) and left (red circles) runs is shown at the bottom. Two firing fields were crossed twice in opposite directions. (b) Firing rate (top), subthreshold membrane potential (middle) and MPOθ amplitude (bottom) indicated by dashed lines in a were plotted against position in the firing field (black horizontal bar). (c) Mouse speed (top), membrane potential of another stellate cell (middle) and MPOθ amplitude (bottom) during a firing field crossing. (d) Firing rate (top), subthreshold membrane potential (middle, after removal of action potentials) and MPOθ amplitude changes (bottom) of the stellate cell shown in c were plotted against the position of the mouse. R, reward points. The field crossing shown in c is indicated by a horizontal gray bar. (e) The phases of action potentials (APs) with respect to LFP theta (left), MPOθ with respect to LFP theta (middle) and action potentials with respect to MPOθ (right) were plotted as a function of normalized position within a firing field. (f) Average firing rate (top), subthreshold membrane potential (middle) and MPOθ amplitude (bottom) were plotted against normalized position in a firing field. Shaded areas indicate ± s.e.m.

  5. Comparing experimental data with grid cell models.
    Figure 5: Comparing experimental data with grid cell models.

    (a) To implement an oscillatory interference (OI) model, six excitatory synapses (red circles) were driven by VCOs and interfered with a baseline inhibitory oscillation at the soma of a compartmental stellate cell model based on a previously described morphological reconstruction15. (b) To implement a CAN model, 128 neurons2 were arranged on a neural sheet. Circularly symmetric center-surround connectivity led to spontaneous pattern formation of synaptic activity si. (c) Plot of VCO timings (red), inhibitory conductance oscillation (blue) and simulated membrane potential (black) in the oscillatory interference model during a field crossing. (d) In the CAN model, constant feedforward excitation (red trace) and recurrent inhibition (blue trace) were converted to discrete excitatory (red bars) and inhibitory (blue bars) events. The black trace shows the simulated membrane potential during a field crossing. (e) Left, color-coded firing rate map of the oscillatory interference model neuron. Right, average firing rate (top), subthreshold membrane potential (middle) and MPOθ amplitude (bottom) were plotted against normalized position in a firing field. The experimental results are shown superimposed (gray). (f) Data are presented as in e, but for the CAN model.

  6. The spiking mechanism in stellate cells is compatible with a CAN model of grid cell firing.
    Figure 6: The spiking mechanism in stellate cells is compatible with a CAN model of grid cell firing.

    (a) Normalized firing rates, as determined from the inverse of the interspike interval, were plotted against deviations of MPOθ amplitudes (left) and sub-theta membrane potential (right) from the mean. Top, oscillatory interference model; middle, CAN model; bottom, experimental data (n = 5 stellate cells with firing fields). (b) Bar graphs summarizing maximal changes in membrane potential (top left), MPOθ amplitudes (top right), membrane potential theta power (bottom left), and ratio of maximal changes in MPOθ amplitude and maximal changes in membrane potential in the center of firing fields (bottom right). Changes were measured as the difference between the maximal value in the center (0.6 times the field width) of a firing field and the mean value preceding a firing field crossing over a distance of 0.6 times the field width (maximum ΔVm: oscillatory interference, 0.9 ± 0.1 mV, mean ± s.e.m. (n = 17 fields); CAN, 4.0 ± 0.2 mV (n = 18 fields); experiment, 5.0 ± 1.0 mV (n = 14 fields from 6 stellate cells); maximum ΔMPOθ: oscillatory interference, 4.1 ± 0.3 mV; CAN, 0.9 ± 0.1 mV; experiment, 2.0 ± 0.2 mV; maximum Δ theta power: oscillatory interference, 50 ± 5%; CAN, 25 ± 5%; experiment, 29 ± 5%; maximum ΔMPOθ/maximum ΔVm: oscillatory interference, 5.6 ± 0.9; CAN, 0.2 ± 0.0; experiment, 0.8 ± 0.3). Error bars denote s.e.m.

  7. Rate and temporal code of grid cell firing are reproduced by a hybrid CAN and oscillatory interference model.
    Figure 7: Rate and temporal code of grid cell firing are reproduced by a hybrid CAN and oscillatory interference model.

    We constructed two variants of the standard CAN model to produce phase precession. (a,c,e) In the first variant, we combined a CAN model with additional slow recurrent inhibition to generate a depolarizing voltage ramp. Excitation (red trace) and inhibition (blue trace) were converted to discrete inhibitory (blue bars) and theta-modulated excitatory (red bars) events (a). The black trace shows the simulated membrane potential during a firing field crossing. (c) Left, color-coded firing rate map of the model neuron. Right, average firing rate (top), subthreshold membrane potential (middle) and MPOθ amplitudes (bottom) were plotted against normalized position in a firing field. The experimental results are shown superimposed (gray). (e) The phase of spikes with reference to the LFP theta (left), subthreshold membrane potential oscillations with reference to the LFP theta (middle) and spikes with reference to the MPOθ were plotted as a function of normalized position in a firing field. Phase precession slope for spikes with reference to the LFP theta (−86° per field, r = −0.59, P < 10−4). Phase precession for MPOs with reference to the LFP theta (−5° per field, r = −0.11, P = 0.50). Phase precession for spikes with reference to MPOθ (−73° per field, r = −0.49, P < 0.0005). (b,d,f) In the second variant, we combined a CAN with an oscillatory interference model. In this hybrid model, feedforward excitatory events were provided by two VCOs (red bars) (b). Recurrent inhibition (blue trace) was converted to discrete inhibitory events (blue bars). The black trace shows the simulated membrane potential during a firing field crossing. (d) Data are presented as in c for the hybrid model. (f) Data are presented as in e for the hybrid model. Phase precession slope for spikes with reference to the LFP theta (−144° per field, r = −0.50, P < 10−4). Phase precession for MPOs with reference to the LFP theta (−136° per field, r = −0.84, P < 10−4). Phase precession for spikes with reference to MPOθ (−11° per field, r = −0.05, P = 0.39).

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Author information

Affiliations

  1. Wolfson Institute for Biomedical Research, University College London, London, UK.

    • Christoph Schmidt-Hieber &
    • Michael Häusser
  2. Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.

    • Christoph Schmidt-Hieber &
    • Michael Häusser

Contributions

C.S.-H. and M.H. designed the study, interpreted the results and wrote the paper. C.S.-H. performed the experiments, analysis and modeling.

Competing financial interests

The authors declare no competing financial interests.

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    Video of mouse navigation in virtual reality

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