>>ITD.m running… IC 800Hz 40 sp/sec 34 O azim Neuron April 16, 2009 Bo Zhu HST.723 Spring 2009 Theme 3 Paper Presentation April 1, 2009

Background Reverberation poses sound localization challenge –Interaural Time Difference (ITD) fluctuates from “multiple” sources

Background Reverberation poses sound localization challenge –Interaural Time Difference (ITD) fluctuates from “multiple” sources Reverberation builds up over time –ITD cues are initially accurate, then degrade over time

Background Reverberation poses sound localization challenge –Interaural Time Difference (ITD) fluctuates from “multiple” sources Reverberation builds up over time –ITD cues are initially accurate, then degrade over time Cross-correlation model traditionally used for coincidence detection of ITDs –ITD processing initiated in MSO, and elaborated in Inferior Colliculus (IC)

Background Reverberation poses sound localization challenge –Interaural Time Difference (ITD) fluctuates from “multiple” sources Reverberation builds up over time –ITD cues are initially accurate, then degrade over time Cross-correlation model traditionally used for coincidence detection of ITDs –ITD processing initiated in MSO, and elaborated in Inferior Colliculus (IC) Human sound localization is robust in reverberation.

Purpose – Examine the IC If the IC is an important site of ITD sound localization, we need to see how reverberation affects it.

Purpose – Examine the IC If the IC is an important site of ITD sound localization, we need to see how reverberation affects it. What does reverberation do to the IC neurons?

Purpose – Examine the IC If the IC is an important site of ITD sound localization, we need to see how reverberation affects it. What does reverberation do to the IC neurons? Are there mechanisms within the IC itself to handle reverberation? Or does it require higher-level processes?

Purpose – Examine the IC If the IC is an important site of ITD sound localization, we need to see how reverberation affects it. What does reverberation do to the IC neurons? Are there mechanisms within the IC itself to handle reverberation? Or does it require higher-level processes? Does our current model of how the IC works account for reverb robustness?

Methods Recordings of 36 ITD-sensitive IC neurons in anesthetized cats. Binaural stimuli were virtually generated to simulate anechoic, moderate reverb, and strong reverb conditions.

Results I Reverb causes Rate Compression Directional Sensitivity defined by firing rate range, across whole range of azimuths - (another more rigorous definition in supplement)

Results I Reverb causes Rate Compression Directional Sensitivity defined by firing rate range, across whole range of azimuths - (another more rigorous definition in supplement) Compression -> Rate Saturation: Inability to correlate firing rates with unique azimuth locations. Sound localization challenge during reverb is already present at the IC neuron level.

Results I Reverb causes Rate Compression Directional Sensitivity defined by firing rate range, across whole range of azimuths - (another more rigorous definition in supplement) Compression -> Rate Saturation: Inability to correlate firing rates with unique azimuth locations. Sound localization challenge during reverb is already present at the IC neuron level. More reverb -> more rate compression

Results II Cross-correlation model Cross-correlation model was created for each single neuron recorded

Results II Cross-correlation model Cross-correlation model was created for each single neuron recorded Model parameters were adjusted to match anechoic rate-ITD data collected from the actual neuron.

Results II Cross-correlation model Cross-correlation model was created for each single neuron recorded Model parameters were adjusted to match anechoic rate-ITD data collected from the actual neuron. Reverberant stimuli were input, and model outputted rate-ITD curves.

Results II Cross-correlation model poorly predicts directional sensitivity during reverb Model generally behaves well: Similar rate compression evidenced during reverb

Results II Cross-correlation model poorly predicts directional sensitivity during reverb Model generally behaves well: Similar rate compression evidenced during reverb Model not accurate during reverb: Accurate prediction would produce points along the y=x diagonal line. Model underestimates performance: Real observed rate ranges (indicating directional sensitivity) are generally higher than the models’.

Results II Cross-correlation model poorly predicts directional sensitivity during reverb Model generally behaves well: Similar rate compression evidenced during reverb Model not accurate during reverb: Accurate prediction would produce points along the y=x diagonal line. Model underestimates performance: Real observed rate ranges (indicating directional sensitivity) are generally higher than the models’. This suggests existence of additional reverb robustness in IC neurons

Time Out (pun intended) So far, we have ignored time effects on rate responses. All spiking rates have been averaged across the duration of the 400 ms response.

Time Out (pun intended) So far, we have ignored time effects on rate responses. All spiking rates have been averaged across the duration of the 400 ms response. ITD degrades as response time progresses

Time Out (pun intended) So far, we have ignored time effects on rate responses. All spiking rates have been averaged across the duration of the 400 ms response. ITD degrades as response time progresses Instead of averaging firing rate over whole response, let’s split response into “early” and “ongoing”

Results III Reverb robustness in early IC response Directional sensitivity of IC neurons is better early in reverb response For this particular unit, we observe more rate compression later in the response

Results III Reverb robustness in early IC response Directional sensitivity of IC neurons is better early in reverb response For this particular unit, we observe more rate compression later in the response Across whole population of units, generally same result Early rate range > Ongoing rate range

Results III Reverb robustness in early IC response Directional sensitivity of IC neurons is better early in reverb response For this particular unit, we observe more rate compression later in the response Across whole population of units, generally same result Early rate range > Ongoing rate range Perhaps we give more “weight” to the early response in ITD calculation?

Results III Reverb robustness in early IC response

Onset Dominance: Early response mostly determines the full response

Results III Reverb robustness in early IC response Most of the spiking activity occurs early in the response

Results III Reverb robustness in early IC response Most of the spiking activity occurs early in the response T50: time after stimulus onset it takes to reach 50% of total spikes T50 ≈ 20 ms

Results III Reverb robustness in early IC response No onset dominance: Full response mostly determined by the ongoing response

Results III Reverb robustness in early IC response T50 ≈ 200 ms

Results III Reverb robustness in early IC response Do onset dominant neurons have significantly higher dir. sens.? - Negative corr. between T50 and full-response rate range

Results III Reverb robustness in early IC response Do onset dominant neurons have significantly higher dir. sens.? - Negative corr. between T50 and full-response rate range No, doesn’t pass significance test. (moderate reverb p=0.624, strong reverb p = 0.517) Possible Implications: 1.Other properties besides onset dominance affect dir. sens. 2.Full response rate range may not be best metric of dir. sens.

Results III Onset Dominance in Cross-Corr Model Cross-corr model didn’t predict directional sensitivity very well. - Averages across whole response (model < actual) (model > actual)

Results III Onset Dominance in Cross-Corr Model Cross-corr model didn’t predict directional sensitivity very well. - Averages across whole response Hypothesis: Onset dominance could account for model’s shortcomings. (model < actual) (model > actual)

Results III Onset Dominance in Cross-Corr Model Cross-corr model didn’t predict directional sensitivity very well. - Averages across whole response Hypothesis: Onset dominance could account for model’s shortcomings. Examine relationship b/t onset dominance (T50) and model error (ΔRR) [ΔRR = Obs. rel. range – model rel. range] (model < actual) (model > actual)

Results III Onset Dominance in Cross-Corr Model Cross-corr model didn’t predict directional sensitivity very well. - Averages across whole response Hypothesis: Onset dominance could account for model’s shortcomings. Examine relationship b/t onset dominance (T50) and model error (ΔRR) [ΔRR = Obs. rel. range – model rel. range] (model < actual) (model > actual) Onset dominant units (short T50): Model performs worse than actual. Late dominant units (long T50): Model performs better than actual.

Results III Onset Dominance in Cross-Corr Model Cross-corr model didn’t predict directional sensitivity very well. - Averages across whole response Hypothesis: Onset dominance could account for model’s shortcomings. Examine relationship b/t onset dominance (T50) and model error (ΔRR) [ΔRR = Obs. rel. range – model rel. range] (model < actual) (model > actual) Onset dominant units (short T50): Model performs worse than actual. Late dominant units (long T50): Model performs better than actual. There is “substantial spread” in data, but Onset dom. can improve directional sensitivity

Results IV IC reverb behavior ≈ Psychophysical results Reverb’s effects on human lateralization vs. Reverb’s effects on IC neural directional sensitivity If these are similar, we can suggest that reverb robustness is inherent in the IC neurons (or lower in auditory pathway)

Results IV IC reverb behavior ≈ Psychophysical results Reverb’s effects on human lateralization vs. Reverb’s effects on IC neural directional sensitivity If these are similar, we can suggest that reverb robustness is inherent in the IC neurons (or lower in auditory pathway) Human subjects lateralized narrowband noises in anechoic and reverberant (moderate and strong) conditions. This measured human directional sensitivity.

Results IV IC reverb behavior ≈ Psychophysical results Reverb’s effects on human lateralization vs. Reverb’s effects on IC neural directional sensitivity If these are similar, we can suggest that reverb robustness is inherent in the IC neurons (or lower in auditory pathway) Human subjects lateralized narrowband noises in anechoic and reverberant (moderate and strong) conditions. This measured human directional sensitivity. For a totalizing neural metric, Hemispheric Difference Signal was used: 1. Sum rate-azimuth curves for the entire population of each hemisphere. 2. Take difference between total hemisphere activities.

Results IV IC reverb behavior ≈ Psychophysical results Reverb’s effects on human lateralization vs. Reverb’s effects on IC neural directional sensitivity If these are similar, we can suggest that reverb robustness is inherent in the IC neurons (or lower in auditory pathway) Human subjects lateralized narrowband noises in anechoic and reverberant (moderate and strong) conditions. This measured human directional sensitivity. For a totalizing neural metric, Hemispheric Difference Signal was used: 1. Sum rate-azimuth curves for the entire population of each hemisphere. 2. Take difference between total hemisphere activities. Reverb’s compression trends are similar. Reverb robustness can be accounted for at IC level in pathway.

Discussion Causes of onset dominance

Discussion Causes of onset dominance - Spike rate adaptation - Vesicle depletion? - Intrinsic dynamics of active membrane channels - Adaptation already present from inputs to IC

Discussion Causes of onset dominance - Spike rate adaptation - Vesicle depletion? - Intrinsic dynamics of active membrane channels - Adaptation already present from inputs to IC Multiple onsets in natural sounds, especially speech

Discussion Causes of onset dominance - Spike rate adaptation - Vesicle depletion? - Intrinsic dynamics of active membrane channels - Adaptation already present from inputs to IC Multiple onsets in natural sounds, especially speech Hz - multiple onsets -> continuously accurate localization

Discussion Causes of onset dominance - Spike rate adaptation - Vesicle depletion? - Intrinsic dynamics of active membrane channels - Adaptation already present from inputs to IC Multiple onsets in natural sounds, especially speech Hz - multiple onsets -> continuously accurate localization Anesthesia's effects controversial - Findings that recovery from echo suppression is faster in unanesthetized animals. - Findings that dir. sens. in reverb approx. same between awake rabbit and anesth. cat, and spike-rate adaptation in IC (for onset dominance) not affected by anesthesia.

Discussion Causes of onset dominance - Spike rate adaptation - Vesicle depletion? - Intrinsic dynamics of active membrane channels - Adaptation already present from inputs to IC Multiple onsets in natural sounds, especially speech Hz - multiple onsets -> continuously accurate localization Anesthesia's effects controversial - Findings that recovery from echo suppression is faster in unanesthetized animals. - Findings that dir. sens. in reverb approx. same between awake rabbit and anesth. cat, and spike-rate adaptation in IC (for onset dominance) not affected by anesthesia. Simplifications of cross-corr model - reduces all inputs to simple interaural correlation, assumes single-input IC neurons. - evidence that IC neurons have multiple inputs, from multiple coincidence detectors. - reverb causes unilateral temporal envelope changes, which can change firing rates of IC neurons, even if cross-correlation signal is unchanged.

Discussion Causes of onset dominance - Spike rate adaptation - Vesicle depletion? - Intrinsic dynamics of active membrane channels - Adaptation already present from inputs to IC Multiple onsets in natural sounds, especially speech Hz - multiple onsets -> continuously accurate localization Anesthesia's effects controversial - Findings that recovery from echo suppression is faster in unanesthetized animals. - Findings that dir. sens. in reverb approx. same between awake rabbit and anesth. cat, and spike-rate adaptation in IC (for onset dominance) not affected by anesthesia. Simplifications of cross-corr model - reduces all inputs to simple interaural correlation, assumes single-input IC neurons. - evidence that IC neurons have multiple inputs, from multiple coincidence detectors. - reverb causes unilateral temporal envelope changes, which can change firing rates of IC neurons, even if cross-correlation signal is unchanged. Minimum human window for cross-corr integration: ~100 ms. Seems “suboptimal.”

Conclusion What does reverberation do to the IC neurons? Causes compression in the range of firing rates, which lowers directional sensitivity.

Conclusion What does reverberation do to the IC neurons? Causes compression in the range of firing rates, which lowers directional sensitivity. Are there mechanisms within the IC itself to handle reverberation? Or does it require higher-level processes? Onset dominance appears to be an important factor in reverb robustness, and we see it at the IC level. Similar reverb responses between IC neurons and human psychophysics suggest subcortical encoding is sufficient to account for human reverb robustness.

Conclusion What does reverberation do to the IC neurons? Causes compression in the range of firing rates, which lowers directional sensitivity. Are there mechanisms within the IC itself to handle reverberation? Or does it require higher-level processes? Onset dominance appears to be an important factor in reverb robustness, and we see it at the IC level. Similar reverb responses between IC neurons and human psychophysics suggest subcortical encoding is sufficient to account for human reverb robustness. Does our current model of how the IC works account for reverb robustness? Apparently not – cross-corr model underperforms during reverb. Its dependency on full-stim averaged cross-corr precludes taking advantage of onset dominance & other time-dependent factors.

Neuron April 16, 2009 Bo Zhu HST.723 Spring 2009 Theme 3 Paper Presentation April 1, 2009 THIS PAPER DOESN’T EXIST YET!

Cross-Correlation Model of IC neurons 1 0