On Parallel Streams through the Mouse Dorsal Lateral Geniculate Nucleus

Abstract

The mouse visual system is an emerging model for the study of cortical and thalamic circuit function. To maximize the usefulness of this model system, it is important to analyze the similarities and differences between the organization of all levels of the murid visual system with other, better studied systems (e.g., non-human primates and the domestic cat). While the understanding of mouse retina and cortex has expanded rapidly, less is known about mouse dorsal lateral geniculate nucleus (dLGN). Here, we study whether parallel processing streams exist in mouse dLGN. We use a battery of stimuli that have been previously shown to successfully distinguish parallel streams in other species: electrical stimulation of the optic chiasm, contrast-reversing stationary gratings at varying spatial phase, drifting sinusoidal gratings, dense noise for receptive field reconstruction, and frozen contrast-modulating noise. As in the optic nerves of domestic cats and non-human primates, we find evidence for multiple conduction velocity groups after optic chiasm stimulation. As in so-called "visual mammals", we find a subpopulation of mouse dLGN cells showing non-linear spatial summation. However, differences in stimulus selectivity and sensitivity do not provide sufficient basis for identification of clearly distinct classes of relay cells. Nevertheless, consistent with presumptively homologous status of dLGNs of all mammals, there are substantial similarities between response properties of mouse dLGN neurons and those of cats and primates.

Keywords: LGN; cell types; mouse models; mouse vision; parallel processing.

Figure 1

Optic chiasm stimulation reveals three compound field responses and corresponding spike responses in mouse dorsal lateral geniculate nucleus (dLGN). (A) Electrode track showing positioning of stimulating electrodes at optic chiasm fibers. (B) Electrode tracks showing multiple tetrode placement into dLGN. (C) Nissl stain of dLGN showing lack of obvious lamination. (D) Top traces, Local field activity in mouse dLGN following dLGN stimulation, at two stimulus intensities: 500 μA and 1.5 mA. Local field potential (LFP) shows four response components. Bottom traces, spikes recorded in LGN in response to same two intensities, overlaid traces to repeated stimuli show three latencies corresponding with the three LFP components. (E,F) Measurement of response component amplitudes (E) and latencies (F).

Figure 2

Examples of linear and non-linear spatial summation in mouse dLGN. (A) An example of a more common, linear summating unit. Cyclograms from peristimulus time histograms of responses to four spatial phases separated by 90° are shown in each row; rows show responses at different spatial frequencies indicated at left. Spike waveform on each wire shown at right along with a projection in cluster space showing cluster isolation. (B) An example of a unit showing non-linear spatial summation.

Figure 3

Linearity of spatial summation in mouse dLGN. (A,B) The DC, F1, and F2 components of the response to counterphased gratings across all spatial phases for the example cells shown in Figure 2. (C) The distribution of linearity index across our population of dLGN single units, with the examples in parts (A) and (B) indicated with arrows. A linearity index above 1 indicated non-linear spatial summation.

Figure 4

Tuning characteristics of linear and non-linear units in mouse dLGN. (A–C) Examples of opposing spatial frequency, temporal frequency, and contrast tuning. Two units shown, one a low-pass unit in black and bandpass in gray. Same two units in each panel. (D) Distributions of peak spatial frequency (left) and width of spatial frequency tuning (right) for cells classified as linear (purple bars) and non-linear (green bars) using the modified null test, measured from fits to spatial frequency tuning plots. (E) Distributions of peak temporal frequency (left) and width of temporal frequency tuning (right) for cells classified as linear (purple bars) and non-linear (green bars) using the modified null test, measured from fits to temporal frequency tuning plots. (F) Distributions of c₅₀ (left) and n parameters (right) of contrast response functions for cells classified as linear (purple bars) and non-linear (green bars) using the modified null test. (G) Correlation of spatial frequency tuning width with peak spatial frequency, taken from fit parameters. (H) Correlation of temporal frequency tuning width with peak spatial frequency, taken from fit parameters. (I) Correlation of the slope and c₅₀ of hyperbolic ratio fits of contrast response functions. (J) Correlation of peak spatial frequency with peak temporal frequency. (K) Correlation of peak spatial frequency with c₅₀. (L) Correlation of peak temporal frequency with c₅₀.

Figure 5

Receptive field properties of single units in mouse dLGN. (A) Example spatial receptive fields from an OFF-center (top) and OFF-center (bottom) cell. (B) All spatial receptive fields from our population. For each cell the 0.5 level contour from a two-dimensional Gaussian fit is shown. (C) Distribution of receptive field areas in mouse dLGN, calculated from two-dimensional Gaussian fit parameter. (D) No difference in linear, non-linear, and unclassified receptive field areas. (E) Impulse response function from the center of the example receptive fields shown in part (A). (F) All impulse responses from our population. (G) Bimodal distribution of impulse response absolute maxima, but unimodal distribution of maximum time.

Figure 6

Reliability and temporal precision of mouse dLGN single units. (A) Example response of a mouse dLGN cell to spatially uniform flicker. (B) All events from all recorded mouse dLGN units to flicker stimulus; each identified event has been fit with a Gaussian, aligned, and overlaid. (C) All events, normalized to the maximum of each event to show the temporal precision of each event. (D) Distribution of event reliability. (E) Distribution of width from the Gaussian fit to each event.