Boundary vector cells in the goldfish central telencephalon encode spatial information

Abstract

Navigation is one of the most fundamental cognitive skills for the survival of fish, the largest vertebrate class, and almost all other animal classes. Space encoding in single neurons is a critical component of the neural basis of navigation. To study this fundamental cognitive component in fish, we recorded the activity of neurons in the central area of the goldfish telencephalon while the fish were freely navigating in a quasi-2D water tank embedded in a 3D environment. We found spatially modulated neurons with firing patterns that gradually decreased with the distance of the fish from a boundary in each cell's preferred direction, resembling the boundary vector cells found in the mammalian subiculum. Many of these cells exhibited beta rhythm oscillations. This type of spatial representation in fish brains is unique among space-encoding cells in vertebrates and provides insights into spatial cognition in this lineage.

Fig 1. Experimental setup and spike sorting.

(A) Schematic overview of the experimental setup: A fish swims freely in a water tank with a wireless recording system (see Materials and methods) mounted on its head. The fish’s movements are recorded by a Raspberry Pi camera positioned in front of the tank. Dashed circle presents the directions used in all data analyses in this paper. We used camera-east as zero with an anti-clockwise progression. (B) Example of the recording site in the goldfish central telencephalon and the corresponding brain region (right panel, anatomical diagram based on [40]). Black x’s show a location in which boundary vector cells were recorded. (C) Example of a raw recording from a tetrode (black traces) and a reference electrode (gray) in the fish’s central telencephalon. Neural activity can be seen in the tetrode alone. Blue and red asterisks correspond to the blue and red clusters in panels D and E. The blue cluster corresponds to the cell presented in S2B Fig. The red cluster corresponds to the cell in Fig 2A–2D. (D) Waveforms of the two neurons after spike sorting. (E) Projections on the main principal components of the data from the tetrode of all spike candidates that crossed the threshold. Other clusters were not distinguishable from other multiunit activity and neural noise. The underlying data supporting panels D and E can be found in a file named Fig 1_data.mat (see Data Availability). Dld, dorsal subdivision of lateral division of area dorsalis; Dlv, ventral subdivision of lateral division of area dorsalis; Dlv-d, dorsal part of Dlv; Dlv-v, ventral part of Dlv; Dmr, rostral part of medial subdivision of area dorsalis; Vd, dorsal nucleus of area ventralis; Vdi, intermediate subnucleus of Vd; Vv, ventral nucleus of area ventralis.

Fig 2. Boundary vector cells in the goldfish brain.

(A-D) An example of a boundary vector cell tuned to distance from the left wall of the water tank (experimental setup is shown in Fig 1A). (A) Left: Fish trajectory (black curve) is presented together with the location of the fish when each spike of a single cell occurred (red dots). The neuron was mainly active when the fish was near the left wall of the tank. Right: trajectory (top panel) and spike locations (bottom panel). (B) Firing rate heatmap of the cell in A, color coded from dark blue (zero firing rate) to dark red (maximal firing rate, indicated on the upper right side of the panel). The preferred boundary direction of this cell is indicated (Θ_max; see Materials and methods). The heatmap is occupancy corrected (see Materials and methods) to obtain a reliable estimate of the firing rate as a function of position. (C) In-session stability of the cell in A. The similar rate of the cell in the first (top panel) and second (bottom panel) halves of the experiment suggest stable activity within the recording session (correlation coefficient and p-value are indicated; see Materials and methods). (D) Spatial coherence (red arrow, top panel) and spatial information (red arrow, bottom panel) values of the cell in A are higher than those of 5,000 shuffled spike trains obtained from the same dataset (blue histograms; see Materials and methods). (E-H) Example of a boundary vector cell tuned to the bottom of the water tank. (I-P) Two other examples of boundary vector cells with a preferred boundary direction, which is neither horizontal nor vertical. The underlying data supporting all panels in this figure can be found in a file named Fig2_data.mat (see Data Availability).

Fig 3. Spatial characteristics of boundary vector cells.

(A-C) Spatial tuning properties of the cell presented in Fig 2A-2D. (A) 2D correlation coefficient of firing rate and the fish’s position of the cell (red dot) and 5,000 shuffled spike trains (gray dots, the 97.5 percentile is depicted), suggesting this cell had a left boundary tuning. Preferred and null tuning directions (Θ_max and Θ_min, respectively; see Materials and methods) and correlation coefficients (cc) are indicated. (B) Spiking activity (red dots) superimposed on the distance of the fish (black curve) from the preferred boundary. (C) Tuning curves (mean ± standard deviation) of the cell’s firing rate to the distance of the fish from the boundary in the preferred direction (Θ_max, blue curve) and its orthogonal direction (Θ_max+90^o, orange curve). A gradually decreasing firing pattern is shown for the Θ_max direction. (D-L) The spatial tuning properties of the boundary vector cells presented in Fig 2E–2H, 2I–2L and 2M–2P, respectively. The underlying data supporting all panels in this figure can be found in a file named Fig 3_data.mat (see Data Availability).

Fig 4. Boundary vector cells tuning to allocentric swimming direction.

(A) Rate maps of the cell in Fig 2A–2D calculated solely with the periods during which the fish swam towards the preferred boundary direction (left panel) and away from it (right panel; see Materials and methods). The same color code was used for both maps. (B) Corresponding tuning curve of firing rate to distance from the preferred boundary calculated solely with the periods during which the fish swam towards the preferred boundary direction (blue curve) and away from it (yellow curve). Modulation ratio index (see Materials and methods) is indicated. (C) Distribution of swimming speed (mean ± standard deviation) along distance from the preferred boundary for the cell in A, bisected into periods during which the fish swam towards the preferred boundary direction (blue curve) and away from it (yellow curve). (D-L) Allocentric swimming direction tuning properties of the boundary vector cells presented in Fig 2E–2H, 2I–2L and 2M–2P, respectively. (M) Modulation ratio index (see Materials and methods). These values describe the strength of modulation caused by the fish’s allocentric swimming direction on boundary vector cells. (N) Spatial information is greater than the corresponding directional information (see Materials and methods) for all boundary vector cells. The underlying data supporting all panels in this figure can be found in a file named Fig 4_data.mat (see Data Availability).

Fig 5. Changing environmental geometry.

(A) The first session was recorded in the main experimental water tank. (B) Before the second recording session, a shelf was inserted into the water tank to modulate the geometry of the environment. (C) Example rate map of a boundary vector cell tuned to distance from the bottom of the water tank, color coded from dark blue (zero firing rate) to dark red (maximal firing rate, indicated on the top right side of the panel). (D) Rate map over the water tank of the same cell after adding the shelf. Firing was modulated by both the bottom of the water tank and above the shelf. (E-H) Additional examples of boundary vector cells before and after the geometric change in the environment. The underlying data supporting all panels in this figure can be found in a file named Fig 5_data.mat (see Data Availability).

Fig 6. Population analysis of boundary vector cells.

(A) Distribution of receptive field widths (i.e., the width of the sigmoid curves fitted to the boundary vector cells; see Materials and methods) relative to the width of the water tank. Units: 1 [Aq] = 70 [cm]. (B) The population max-p values and preferred boundary direction Θ_max (see Materials and methods) of all 119 single units recorded from the brains of 15 fish. The dots are distributed on a radial plot where $Radius=log\left(\frac{1-\mathrm{m}\mathrm{a}\mathrm{x}\_p}{\mathrm{m}\mathrm{a}\mathrm{x}\_p}\right)$ folded to the range [0, 5] and Angle = Θ_max. The color bar on the right-hand side of panel B spans the population max-p values on a logarithmic scale and shows the distribution of cells in panels B-G. (C-G) Population statistics. Y-axis in panels C-F is the rank-order max-p value of each cell (see Materials and methods), so that the cells with the strongest spatial modulation are in the top of each panel. Statistics summary of these panels is indicated in the bottom right side of each panel. For each cell, we show the following properties: (C) in-session stability correlation coefficient, (D) spatial coherence, (E) spatial information, (F) mean firing rate, and (G) mean action potential amplitude and full width at half maximum for the electrode recorded the strongest spikes. Two-sample t test results are indicated. (H) Mean firing rate in the 30% strongest bins in the rate maps of the 35 boundary vector cells in the first vs. second half of the experiments. Paired t test results are indicated. (I) Preferred boundary direction (Θ_max) of the 35 boundary vector cells vs. peak firing rate. The angle in the polar plot is Θ_max, and the radius is the peak firing rate. Circular-linear correlation coefficient (r) and p-value are indicated. The underlying data supporting all panels in this figure can be found in a file named Fig 6_data.mat (see Data Availability).

Fig 7. Beta oscillations in boundary vector cells.

(A-C) Example of a boundary vector cell with a periodic interspike interval (ISI) pattern. (A) Firing rate heatmap of a boundary vector cell tuned to the bottom of the water tank, color coded from dark blue (zero firing rate) to dark red (maximal firing rate, indicated on the upper right side of the panel). (B) ISI histogram of the cell in A. Even spacing between the peaks suggests periodic oscillations of the neural activity. (C) Power spectral density of the histogram in B (normalized). Colored background marks the typical frequency range of beta waves (12.5–30 Hz). A local peak is shown at approximately 16 Hz, suggesting that this cell exhibited beta-rhythm oscillations. (D-F) A counter-example of a cell with no clear spatial tuning and no specific pattern in the ISI histogram. (G) Maximal power spectral density in the beta waves range for the entire population. The color bar spans the population’s max-p values (see Materials and methods) on a logarithmic scale. As shown, roughly half of the boundary vector cells (blue dots) exhibited beta rhythm oscillations at 15.25 ± 1.62 Hz (mean ± standard deviation). (H) Different thresholds were tested to estimate the prevalence of beta oscillations in the recorded population (see Materials and methods). The median value of the population’s max-p was used to divide the data into groups similar in size. Regardless of the threshold tested, the cells that were more spatially tuned (i.e., below median-max-p = 0.11, blue curve) were more abundant above the threshold than others (i.e., above median-max-p value, orange curve). The underlying data supporting all panels in this figure can be found in a file named Fig 7_data.mat (see Data Availability).