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. 2019 Oct 30;5(10):eaax2211.
doi: 10.1126/sciadv.aax2211. eCollection 2019 Oct.

Descending Pathways Mediate Adaptive Optimized Coding of Natural Stimuli in Weakly Electric Fish

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Free PMC article

Descending Pathways Mediate Adaptive Optimized Coding of Natural Stimuli in Weakly Electric Fish

Chengjie G Huang et al. Sci Adv. .
Free PMC article

Abstract

Biological systems must be flexible to environmental changes to survive. This is exemplified by the fact that sensory systems continuously adapt to changes in the environment to optimize coding and behavioral responses. However, the nature of the underlying mechanisms remains poorly understood in general. Here, we investigated the mechanisms mediating adaptive optimized coding of naturalistic stimuli with varying statistics depending on the animal's velocity during movement. We found that central neurons adapted their responses to stimuli with different power spectral densities such as to optimally encode them, thereby ensuring that behavioral responses are, in turn, better matched to the new stimulus statistics. Sensory adaptation further required descending inputs from the forebrain as well as the raphe nuclei. Our findings thus reveal a previously unknown functional role for descending pathways in mediating adaptive optimized coding of natural stimuli that is likely generally applicable across sensory systems and species.

Figures

Fig. 1
Fig. 1. Experimental setup and relevant circuitry.
Top: The fish is placed in an experimental tank. Behavioral responses consisting of changes in the EOD are recorded via two electrodes (E1 and E2) placed at the head and tail of the fish, respectively. Neural recordings were obtained by placing electrodes in the brain. Stimuli were delivered via two other electrodes (dark solid circles) placed on either side of the fish. We used stimuli whose power spectral density either decayed strongly with increasing frequency (i.e., characterized by a power law exponent of −2, green) or was constant (i.e., characterized by a power law exponent of 0, orange). Bottom: Simplified circuit diagram in which sensory input is transduced and sent via EAs to PCells within the ELL. PCells project to higher brain areas that mediate behavioral responses including the midbrain TS and indirectly to Tel. PCells also receive large amounts of descending input (i.e., feedback) indirectly from Tel via TS as well as neuromodulatory feedback from the raphe nuclei. Photo credit: Maurice Chacron at McGill University.
Fig. 2
Fig. 2. ELL PCells adapt their responses such as to optimally encode stimuli characterized by a power law exponent of −2.
(A) Schematic showing the adaptation stimulus power spectral density (left, solid black) together with that of natural stimuli (left, dashed black). Previous studies have shown that PCells optimally encode natural stimuli. Hence, their response power spectral density to these is constant (middle, dashed black). We predict that the PCell response power spectral density to the adaptation stimulus will initially not be independent of frequency (e.g., low pass; middle, solid black) but will gradually become more independent of frequency (middle, green) via adaption. Behavioral sensitivity is initially matched to natural stimulus statistics (right, dashed black). We predict that adaptation at the behavioral level will lead to changes in behavioral sensitivity such as to better match the statistics of the adaptation stimulus (right, solid green). (B) Spectrogram (i.e., running time power spectral density) of the response of an example PCell to the adaptation stimulus. The upper left and right insets show the response power spectral densities as a function of frequency for this cell early (left) and late (right) during stimulus presentation. (C) Left: Whiteness index as a function of time for this same PCell. Right: Whisker-box plots showing the population-averaged whiteness index values early and late during stimulus presentation. (D) Population-averaged behavioral sensitivity as a function of frequency before (black) and after (green) stimulus presentation. (E) Left: Population-averaged behavioral exponents before (black) and after (green) stimulus presentation. Right: Population-averaged matching index values before (black) and after (green) stimulus presentation. *P = 0.05 (Wilcoxon signed rank test).
Fig. 3
Fig. 3. ELL PCells adapt their responses such as to optimally encode stimuli characterized by a power law exponent of 0.
(A) Same as Fig. 2A but for an adaptation stimulus with exponent 0. (B) Spectrogram (i.e., running time power spectral density) of the response of an example PCell to the adaptation stimulus. The upper left and right insets show the response power spectral densities as a function of frequency for this cell early (left) and late (right) during stimulus presentation. (C) Left: Whiteness index as a function of time for this same PCell. Right: Whisker-box plots showing the population-averaged whiteness index values early and late during stimulus presentation. (D) Population-averaged behavioral sensitivity as a function of frequency before (black) and after (orange) stimulus presentation. (E) Left: Population-averaged behavioral exponents before (black) and after (orange) stimulus presentation. Right: Population-averaged matching index values before (black) and after (orange) stimulus presentation. *P = 0.05 (Wilcoxon signed rank test).
Fig. 4
Fig. 4. Stimulus statistics depend on the level of activity in weakly electric fish.
(A) Schematic of the tank setup with an infrared (IR) camera recording of a stationary fish inside a tube (fish 1) and another freely moving fish (fish 2) with trajectories in gray. A small dipole located close to the tube recorded the stimuli experienced by fish 1 (black circles). (B) Probability density of the velocities of fish 2 for all recordings (N = 4) with division of low (dark blue) versus high (light blue) velocities (see Materials and Methods). The dashed line indicates the median velocity. The inset shows the probability densities of epoch duration during which the velocity was low (dark blue) and high (light blue) velocities. These overlapped and were thus not significantly different from one another. (C) Power spectral densities of the stimuli as a function of stimulus frequency for the full (black), low (dark blue), and high (light blue) velocity stimuli. Inset: Whisker-box plots showing the best-fit power-law exponents for the envelopes of the corresponding stimuli. *P = 0.05 (Wilcoxon signed rank test).
Fig. 5
Fig. 5. Sensory adaptation requires descending input from the forebrain.
(A) Schematic showing the brain (left); we lesioned the forebrain (left: red cross) before presenting the adaptation stimulus characterized by a power law exponent of −2 (middle left, solid black). Also shown is the power spectral density of the natural stimulus (middle left, dashed black). We investigated whether neural response power spectral density (middle right) as well as behavioral sensitivity (right) changed throughout stimulus presentation. (B) Spectrogram (i.e., running time power spectral density) of the response of an example PCell to the adaptation stimulus after lesion. The upper left and right insets show the response power spectral densities as a function of frequency for this cell early (left) and late (right) during stimulus presentation. (C) Left: Whiteness index as a function of time for this same PCell. Right: Population-averaged whiteness index values early (black) and late (green) during stimulus presentation. (D) Population-averaged behavioral sensitivity as a function of frequency before lesion (gray), as well as before (black) and after (green) stimulus presentation after lesion. (E) Left: Population-averaged behavioral exponents under control before lesion (gray), control after lesion (black), and after presenting the adaptation stimulus (green). Right: Population-averaged matching index values under control before lesion (gray), control after lesion (black), and after presenting the adaptation stimulus (green).
Fig. 6
Fig. 6. Sensory adaptation requires descending serotonergic input from the raphe nuclei.
(A) Schematic showing the brain (left); we injected the serotonergic antagonist KET into the ELL (pipette) before presenting the adaptation stimulus characterized by a power law exponent of −2 (middle, solid black). Also shown is the power spectral density of the natural stimulus (middle left, dashed black). We investigated whether neural response power spectral density (middle right) as well as behavioral sensitivity (right) changed throughout stimulus presentation. (B) Spectrogram (i.e., running time power spectral density) of the response of an example PCell to the adaptation stimulus after KET injection. The upper left and right insets show the response power spectral densities as a function of frequency for this cell early (left) and late (right) during stimulus presentation. (C) Left: Whiteness index as a function of time for this same PCell. Right: Population-averaged whiteness index values early (black) and late (green) during stimulus presentation. (D) Population-averaged behavioral sensitivity as a function of frequency before lesion (gray), as well as before (black) and after (green) stimulus presentation after KET injection. (E) Left: Population-averaged behavioral exponents under control before KET injection (gray), control after KET injection (black), and after KET injection after presenting the adaptation stimulus (green). Right: Population-averaged matching index values under control before KET injection (gray), control after KET injection (black), and after KET injection after presenting the adaptation stimulus (green).

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