Light-Emitting Diode (LED) merupakan jenis lampu dengan teknologi terbaru dan merupakan komponen ... more Light-Emitting Diode (LED) merupakan jenis lampu dengan teknologi terbaru dan merupakan komponen elektronika yang dapat memancarkan cahaya monokromatik ketika diberikan tegangan maju. LED merupakan keluarga Dioda yang terbuat dari bahan semikonduktor. Berbeda dengan Lampu Pijar, LED tidak memerlukan pembakaran filamen sehingga tidak menimbulkan panas dalam menghasilkan cahaya. Teknologi LED memiliki berbagai kelebihan seperti tidak menimbulkan panas, tahan lama, tidak mengandung bahan berbahaya seperti merkuri, dan hemat listrik serta bentuknya yang kecil ini semakin popular dalam bidang teknologi pencahayaan. Berbagai produk yang memerlukan cahaya pun mengadopsi teknologi Light-Emitting Diode (LED) ini. Beberapa pengaplikasian LED dalam kehidupan sehari-hari antara lain: Lampu Penerangan Rumah, Lampu Penerangan Jalan, Papan Iklan (Advertising), Backlight LCD (TV, Display Handphone, Monitor), Lampu Dekorasi Interior maupun Exterior. Lampu merupakan alat yang paling penting sebagai fish attractor dalam kegiatan penangkapan ikan tipe night fishing. Konsumsi vi biaya operasional dalam night fishing yang terbesar adalah untuk kebutuhan lampu fish attractor. Selama ini, jenis lampu yang digunakan adalah lampu Metal Halide. Namun, seiring dengan berkembangnya penelitian ke arah lampu hemat energi, maka lampu fish attractor-pun turut mendapat perhatian. Lampu Metal Halide yang selama ini biasa digunakan, dianggap tidak lagi efektif karena beberapa hal yang kurang menguntungkan seperti boros bahan bakar, menghasilkan panas, dan mengandung merkuri. Oleh karena itu, penelitian ini bertujuan untuk mengkaji dan membandingkan lampu Light-Emitting Diode (LED) sebagai lampu fish attractor, untuk dikombinasikan dengan lampu Metal Halide (MH).
How smart is an octopus? Although we often associate intelligence with animals like dolphins, ape... more How smart is an octopus? Although we often associate intelligence with animals like dolphins, apes, elephants, parrots, and members of the crow family (jays and ravens), recent studies have revealed that octopuses and cuttlefish are also intelligent. These cephalopods have the largest brains amongst invertebrates, but their brains are completely different from our own human brains. Unlike many animals, an octopus grows up and learns on its own, without any instruction from its parents. An octopus quickly learns how to hide and camouflage itself to avoid predators. Octopuses have also been shown to use tools and sometimes they like to play. Cuttlefish, like their octopus cousins, are extremely intelligent. They are dazzling masters of camouflage, and have an excellent ability to remember past experiences, which helps guide their future behavior and decision-making. These clever cephalopods are revealing new insights that are helping scientists to understand how intelligence evolved.
Top-down signals from frontal cortex (FC) are conjectured to play a critical role in cognitive co... more Top-down signals from frontal cortex (FC) are conjectured to play a critical role in cognitive control of sensory processing. To explore this interaction, we compared activity in ferret FC and primary auditory cortex (A1) during auditory and visual tasks requiring discrimination between classes of reference and target stimuli. FC responses were behaviorally-gated, selectively encoded the timing and invariant behavioral meaning of target stimuli, could be rapid in onset, and sometimes persisted for hours following behavior. This mirrors earlier findings in A1that attention triggered rapid, selective, persistent, task-related changes in spectrotemporal receptive fields. Simultaneously recorded local field potentials (LFPs) revealed behaviorally-gated changes in inter-areal coherence, selectively modulated between FC and focal regions of A1 responsive to target sounds. These results suggest that A1 and FC dynamically establish a functional connection during auditory behavior that shapes the flow of sensory information and maintains a persistent trace of recent task-relevant stimulus features.
Journal of Computational Neuroscience, Aug 27, 2009
Receptive field properties of neurons in A1 can rapidly adapt their shapes during task performanc... more Receptive field properties of neurons in A1 can rapidly adapt their shapes during task performance in accord with specific task demands and salient sensory cues (
The brain is an extraordinarily adaptive and predictive machine, adapting to present demands and ... more The brain is an extraordinarily adaptive and predictive machine, adapting to present demands and predicting the future, and reveals tremendous plasticity in multiple diverse forms, from birth through adulthood. Neuronal plasticity is a fundamental property of neurons and neuronal circuits because it facilitates adaptation to new environments, dynamically adjusts cortical sensory filters to improve processing of salient stimuli to optimize task performance, enables prediction of reward, and provides the basis for learning from experience. Depending upon the time scales and mechanisms involved in induction and persistence of receptive field (RF) changes, these changes may be described as ephemeral stimulus-driven adaptive plasticity, rapid attention-driven plasticity, or consolidated learning-induced plasticity. There are likely to be common molecular and synaptic mechanisms underlying all rapid RF transformations, but also some striking differences that contribute to auditory perception and cognition.
In higher sensory cortices, there is a gradual transformation from sensation to perception and ac... more In higher sensory cortices, there is a gradual transformation from sensation to perception and action. In the auditory system, this transformation is revealed by responses in the rostral Ventral Posterior field (VPr), a tertiary area in ferret auditory cortex, which shows long-term learning in trained compared to naïve animals, arising from selectively enhanced responses to behaviorally relevant target stimuli. This enhanced representation is further amplified during active performance of spectral or temporal auditory discrimination tasks. VPr also shows sustained shortterm memory activity after target stimulus offset, correlated with task-response timing and action. These task-related changes in auditory filter properties enable VPr neurons to quickly and nimbly switch between different responses to the same acoustic stimuli, reflecting either spectrotemporal properties, timing or behavioral meaning of the sound. Furthermore, they demonstrate an interaction between dynamics of long-term learning and short-term attention as incoming sound is selectively attended, recognized and translated into action. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:
Highlights d Enhanced categorical representations in higher auditory areas during behavior d Dist... more Highlights d Enhanced categorical representations in higher auditory areas during behavior d Distinct sensory and categorical information flows during categorization tasks d Separate neuronal groups with intrinsic versus task-induced categorical responses d Similar dynamics in different categorization tasks with distinct acoustic features
Advances in Experimental Medicine and Biology, 2013
The context in which a stimulus occurs can influence its perception. We study contextual effects ... more The context in which a stimulus occurs can influence its perception. We study contextual effects in audition using the tritone paradox, where a pair of complex (Shepard) tones separated by half an octave can be perceived as ascending or descending. While ambiguous in isolation, they are heard with a clear upward or downward change in pitch, when preceded by spectrally matched biasing sequences. We presented these biased Shepard pairs to awake ferrets and obtained neuronal responses from primary auditory cortex. Using dimensionality reduction from the neural
Journal of the Acoustical Society of America, Mar 1, 2010
To explore the role of top-down projections from frontal cortex (FC) to auditory cortex (AC), whi... more To explore the role of top-down projections from frontal cortex (FC) to auditory cortex (AC), which may play a role in adaptive reshaping of A1 receptive fields to attended acoustic stimuli during behavior, simultaneous recordings were made from single neurons in FC and AC in behaving ferrets, trained on multiple auditory detection and discrimination tasks in positive and negative reinforcement paradigms. Performance required selective attention to different salient spectral frequency and/or temporal cues. Previous observations revealed task-specific transformations in receptive fields in AC [Fritz et al. (2003); (2005); (2007); (2009); Atiani et al. (2009)]. In contrast, FC neurons showed recognition responses, which categorically distinguished between acoustic foreground and background stimuli. FC responses to targets were often independent of the acoustic properties of the target and thus encoded an abstract representation of the class of target stimuli [Fritz et al. (2009)]. Stimulation of FC, paired with tones, lead to receptive field transformations similar to those observed in behavior. These results emphasize the importance of interactions between multiple areas during selective attention, and the tight coupling of target recognition and auditory attention that enhances auditory cortical filters for attended acoustic stimuli, thus creating a functional representation of task-salient sounds during behavior. [This work was supported by grants from NIDCD, NIH.]
IEEE Transactions on Signal Processing, Apr 1, 2016
We consider the problem of estimating the sparse time-varying parameter vectors of a point proces... more We consider the problem of estimating the sparse time-varying parameter vectors of a point process model in an online fashion, where the observations and inputs respectively consist of binary and continuous time series. We construct a novel objective function by incorporating a forgetting factor mechanism into the point process log-likelihood to enforce adaptivity and employ ℓ1-regularization to capture the sparsity. We provide a rigorous analysis of the maximizers of the objective function, which extends the guarantees of compressed sensing to our setting. We construct two recursive filters for online estimation of the parameter vectors based on proximal optimization techniques, as well as a novel filter for recursive computation of statistical confidence regions. Simulation studies reveal that our algorithms outperform several existing point process filters in terms of trackability, goodness-of-fit and mean square error. We finally apply our filtering algorithms to experimentally recorded spiking data from the ferret primary auditory cortex during attentive behavior in a click rate discrimination task. Our analysis provides new insights into the time-course of the spectrotemporal receptive field plasticity of the auditory neurons.
Sensory environments change over a wide dynamic range and sensory processing can change rapidly t... more Sensory environments change over a wide dynamic range and sensory processing can change rapidly to facilitate stable perception. While rapid changes may occur throughout the sensory processing pathway, cortical changes are believed to profoundly influence perception. Prior stimulation studies showed that orbitofrontal cortex (OFC) can modify receptive fields and sensory coding in A1, but the engagement of OFC during listening and the pathways mediating OFC influences on A1 are unknown. We show in mice that OFC neurons respond to sounds consistent with a role of OFC in audition. We then show in vitro that OFC axons are present in A1 and excite pyramidal and GABAergic cells in all layers of A1 via glutamatergic synapses. Optogenetic stimulation of OFC terminals in A1 in vivo evokes short-latency neural activity in A1 and pairing activation of OFC projections in A1 with sounds alters sound-evoked A1 responses. Together, our results identify a direct connection from OFC to A1 that can excite A1 neurons at the earliest stage of cortical processing, and thereby sculpt A1 receptive fields. These results are consistent with a role for OFC in adjusting to changing behavioral relevance of sensory inputs and modulating A1 receptive fields to enhance sound processing.
Population responses of cortical neurons encode considerable details about sensory stimuli, and t... more Population responses of cortical neurons encode considerable details about sensory stimuli, and the encoded information is likely to change with stimulus context and behavioral conditions. The details of encoding are difficult to discern across large sets of single neuron data because of the complexity of naturally occurring stimulus features and cortical receptive fields. To overcome this problem, we used the method of stimulus reconstruction to study how complex sounds are encoded in primary auditory cortex (AI). This method uses a linear spectro-temporal model to map neural population responses to an estimate of the stimulus spectrogram, thereby enabling a direct comparison between the original stimulus and its reconstruction. By assessing the fidelity of such reconstructions from responses to modulated noise stimuli, we estimated the range over which AI neurons can faithfully encode spectro-temporal features. For stimuli containing statistical regularities (typical of those found in complex natural sounds), we found that knowledge of these regularities substantially improves reconstruction accuracy over reconstructions that do not take advantage of this prior knowledge. Finally, contrasting stimulus reconstructions under different behavioral states showed a novel view of the rapid changes in spectro-temporal response properties induced by attentional and motivational state.
Acoustic filter properties of A1 neurons can dynamically adapt to stimulus statistics, classical ... more Acoustic filter properties of A1 neurons can dynamically adapt to stimulus statistics, classical conditioning, instrumental learning and the changing auditory attentional focus. We have recently developed an experimental paradigm that allows us to view cortical receptive field plasticity on-line as the animal meets different behavioral challenges by attending to salient acoustic cues and changing its cortical filters to enhance performance. We propose that attention is the key trigger that initiates a cascade of events leading to the dynamic receptive field changes that we observe. In our paradigm, ferrets were initially trained, using conditioned avoidance training techniques, to discriminate between background noise stimuli (temporally orthogonal ripple combinations) and foreground tonal target stimuli. They learned to generalize the task for a wide variety of distinct background and foreground target stimuli. We recorded cortical activity in the awake behaving animal and computed on-line spectrotemporal receptive fields (STRFs) of single neurons in A1. We observed clear, predictable task-related changes in STRF shape while the animal performed spectral tasks (including single tone and multi-tone detection, and two-tone discrimination) with different tonal targets. A different set of task-related changes occurred when the animal performed temporal tasks (including gap detection and click-rate discrimination). Distinctive cortical STRF changes may constitute a "task-specific signature". These spectral and temporal changes in cortical filters occur quite rapidly, within 2 minutes of task onset, and fade just as quickly after task completion, or in some cases, persisted for hours. The same cell could multiplex by differentially changing its receptive field in different task conditions. On-line dynamic task-related changes, as well as persistent plastic changes, were observed at a single-unit, multi-unit and population level. Auditory attention is likely to be pivotal in mediating these task-related changes since the magnitude of STRF changes correlated with behavioral performance on tasks with novel targets. Overall, these results suggest the presence of an attention-triggered plasticity algorithm in A1 that can swiftly change STRF shape by transforming receptive fields to enhance figure/ground separation, by using a contrast matched filter to filter out the background, while simultaneously enhancing the salient acoustic target in the foreground. These results favor the view of a nimble, dynamic, attentive and adaptive brain that can quickly reshape its sensory filter properties and sensori-motor links on a moment-to-moment basis, depending upon the current challenges the animal faces. In this review, we summarize our results in the context of a broader survey of the field of auditory attention, and then consider neuronal networks that could give rise to this phenomenon of attention-driven receptive field plasticity in A1.
Journal of the Acoustical Society of America, Mar 1, 2010
It is well known that humans can robustly perceive phonemes despite substantial variability acros... more It is well known that humans can robustly perceive phonemes despite substantial variability across speakers, context and natural distortions. This study examines the responses of neurons in primary auditory cortex (A1) to phonetically labeled speech stimuli in clean, additive noise and reverberant conditions. Using a linear decoder [Bialek (1991)] to reconstruct the input stimulus spectrogram from the population response, we observed that spectrograms reconstructed from the neural responses to noisy speech were closer to the original clean spectrograms than to the noisy ones. This indicates that sound representations in A1 serve to enhance information about natural speech signals relative to noise, thus extracting signal from noise. Examining the average reconstructed phoneme spectrograms in clean and noisy speech revealed a remarkable robustness in the encoding of features important for discrimination of different phonemes. In addition, it was found that the strict linear spectro-temporal receptive field (STRF) model of A1 neurons is insufficient to explain the noise robustness observed in the neural data. However, when a non-linear synaptic depression is integrated into the inputs for the STRF model, the noise was reduced in the reconstructed spectrograms similar to what observed with the actual neural data.
The auditory cortex plays a critical role in the perception and localization of complex sounds. I... more The auditory cortex plays a critical role in the perception and localization of complex sounds. It is functionally involved in the extraction and perception of the key auditory attributes of pitch, timbre, and localization. Its anatomical organization is reminiscent of other sensory cortical areas, with a tonotopic organization in multiple fields. Models of auditory function are becoming more sophisticated, attempting to account for the remarkable capabilities evident in segregating sounds in clutter, and overcoming noise and variability in sound recognition. The auditory cortex is also increasingly being viewed as a malleable structure that rapidly adapts to reflect task demands and environmental limitations, exhibiting plasticity in response to attention and other top-down influences.
Journal of the Acoustical Society of America, Dec 1, 2016
In order to explore the representation of sound features in auditory long-term memory, two groups... more In order to explore the representation of sound features in auditory long-term memory, two groups of ferrets were trained on Go vs Nogo, 3-zone classification tasks. The sound stimuli differed primarily along the spectral and temporal dimensions. In Group 1, two ferrets were trained to (i) classify tones based on their frequency (Tone-task), and subsequently learned to (ii) classify white noise based on its amplitude modulation rate (AM-task). In Group 2, two ferrets were trained to classify tones based on correlated combinations of their frequency and AM rate (AM-Tone task). Both groups of ferrets learned their tasks and were able to generalize performance along the trained spectral (tone frequency) or temporal (AM rate) dimensions. Insights into stimulus representations in memory were gained when the animals were tested with a diverse set of untrained probes that mixed features from the two dimensions. Animals exhibited a complex pattern of responses to the probes reflecting primarily the probes' spectral similarity with the training stimuli, and secondarily the temporal features of the stimuli. These diverse behavioral decisions could be well accounted for by a nearest-neighbor classifier model that relied on a multiscale spectrotemporal cortical representation of the training and probe sounds. V
We investigated the hypothesis that task performance can rapidly and adaptively reshape cortical ... more We investigated the hypothesis that task performance can rapidly and adaptively reshape cortical receptive field properties in accord with specific task demands and salient sensory cues. We recorded neuronal responses in the primary auditory cortex of behaving ferrets that were trained to detect a target tone of any frequency. Cortical plasticity was quantified by measuring focal changes in each cell's spectrotemporal response field (STRF) in a series of passive and active behavioral conditions. STRF measurements were made simultaneously with task performance, providing multiple snapshots of the dynamic STRF during ongoing behavior. Attending to a specific target frequency during the detection task consistently induced localized facilitative changes in STRF shape, which were swift in onset. Such modulatory changes may enhance overall cortical responsiveness to the target tone and increase the likelihood of 'capturing' the attended target during the detection task. Some receptive field changes persisted for hours after the task was over and hence may contribute to long-term sensory memory.
Proceedings of the International Symposium on Auditory and Audiological Research, Dec 15, 2007
We examined the responses of neurons in primary auditory cortex (A1) to phonetically labeled spee... more We examined the responses of neurons in primary auditory cortex (A1) to phonetically labeled speech stimuli. Sentences were taken from the TIMIT database and chosen to represent a diversity of male and female speakers. We presented these stimuli to awake ferrets while recording the activity of isolated A1 neurons. For analysis, we segmented the continuous speech samples into sequences of phonemes, which represent the smallest significant units of speech. We characterized the response properties of each neuron as the peristimulus time histogram (PSTH) response to each phoneme. Across a population of A1 neurons, we observed distinct patterns of phoneme selectivity that may provide a neural basis or low-level phoneme discrimination. We investigated how features of speech are encoded in A1 using a method for reconstructing the speech stimulus from the neural population responses. Stimuli were reconstructed using a linear spectro-temporal model to map the response to the stimulus spectrogram. We compared the accuracy of reconstruction across phonemes. One important factor involved in stimulus reconstruction is the presence of correlations in complex natural stimulus such as speech. Prior knowledge of regularities in the stimulus can benefit reconstruction in noise and when spectro-temporal coverage is limited. We studied the influence of prior knowledge of stimulus correlations, noise and spectro-temporal coverage on reconstruction accuracy in neural data and in simulation.
Light-Emitting Diode (LED) merupakan jenis lampu dengan teknologi terbaru dan merupakan komponen ... more Light-Emitting Diode (LED) merupakan jenis lampu dengan teknologi terbaru dan merupakan komponen elektronika yang dapat memancarkan cahaya monokromatik ketika diberikan tegangan maju. LED merupakan keluarga Dioda yang terbuat dari bahan semikonduktor. Berbeda dengan Lampu Pijar, LED tidak memerlukan pembakaran filamen sehingga tidak menimbulkan panas dalam menghasilkan cahaya. Teknologi LED memiliki berbagai kelebihan seperti tidak menimbulkan panas, tahan lama, tidak mengandung bahan berbahaya seperti merkuri, dan hemat listrik serta bentuknya yang kecil ini semakin popular dalam bidang teknologi pencahayaan. Berbagai produk yang memerlukan cahaya pun mengadopsi teknologi Light-Emitting Diode (LED) ini. Beberapa pengaplikasian LED dalam kehidupan sehari-hari antara lain: Lampu Penerangan Rumah, Lampu Penerangan Jalan, Papan Iklan (Advertising), Backlight LCD (TV, Display Handphone, Monitor), Lampu Dekorasi Interior maupun Exterior. Lampu merupakan alat yang paling penting sebagai fish attractor dalam kegiatan penangkapan ikan tipe night fishing. Konsumsi vi biaya operasional dalam night fishing yang terbesar adalah untuk kebutuhan lampu fish attractor. Selama ini, jenis lampu yang digunakan adalah lampu Metal Halide. Namun, seiring dengan berkembangnya penelitian ke arah lampu hemat energi, maka lampu fish attractor-pun turut mendapat perhatian. Lampu Metal Halide yang selama ini biasa digunakan, dianggap tidak lagi efektif karena beberapa hal yang kurang menguntungkan seperti boros bahan bakar, menghasilkan panas, dan mengandung merkuri. Oleh karena itu, penelitian ini bertujuan untuk mengkaji dan membandingkan lampu Light-Emitting Diode (LED) sebagai lampu fish attractor, untuk dikombinasikan dengan lampu Metal Halide (MH).
How smart is an octopus? Although we often associate intelligence with animals like dolphins, ape... more How smart is an octopus? Although we often associate intelligence with animals like dolphins, apes, elephants, parrots, and members of the crow family (jays and ravens), recent studies have revealed that octopuses and cuttlefish are also intelligent. These cephalopods have the largest brains amongst invertebrates, but their brains are completely different from our own human brains. Unlike many animals, an octopus grows up and learns on its own, without any instruction from its parents. An octopus quickly learns how to hide and camouflage itself to avoid predators. Octopuses have also been shown to use tools and sometimes they like to play. Cuttlefish, like their octopus cousins, are extremely intelligent. They are dazzling masters of camouflage, and have an excellent ability to remember past experiences, which helps guide their future behavior and decision-making. These clever cephalopods are revealing new insights that are helping scientists to understand how intelligence evolved.
Top-down signals from frontal cortex (FC) are conjectured to play a critical role in cognitive co... more Top-down signals from frontal cortex (FC) are conjectured to play a critical role in cognitive control of sensory processing. To explore this interaction, we compared activity in ferret FC and primary auditory cortex (A1) during auditory and visual tasks requiring discrimination between classes of reference and target stimuli. FC responses were behaviorally-gated, selectively encoded the timing and invariant behavioral meaning of target stimuli, could be rapid in onset, and sometimes persisted for hours following behavior. This mirrors earlier findings in A1that attention triggered rapid, selective, persistent, task-related changes in spectrotemporal receptive fields. Simultaneously recorded local field potentials (LFPs) revealed behaviorally-gated changes in inter-areal coherence, selectively modulated between FC and focal regions of A1 responsive to target sounds. These results suggest that A1 and FC dynamically establish a functional connection during auditory behavior that shapes the flow of sensory information and maintains a persistent trace of recent task-relevant stimulus features.
Journal of Computational Neuroscience, Aug 27, 2009
Receptive field properties of neurons in A1 can rapidly adapt their shapes during task performanc... more Receptive field properties of neurons in A1 can rapidly adapt their shapes during task performance in accord with specific task demands and salient sensory cues (
The brain is an extraordinarily adaptive and predictive machine, adapting to present demands and ... more The brain is an extraordinarily adaptive and predictive machine, adapting to present demands and predicting the future, and reveals tremendous plasticity in multiple diverse forms, from birth through adulthood. Neuronal plasticity is a fundamental property of neurons and neuronal circuits because it facilitates adaptation to new environments, dynamically adjusts cortical sensory filters to improve processing of salient stimuli to optimize task performance, enables prediction of reward, and provides the basis for learning from experience. Depending upon the time scales and mechanisms involved in induction and persistence of receptive field (RF) changes, these changes may be described as ephemeral stimulus-driven adaptive plasticity, rapid attention-driven plasticity, or consolidated learning-induced plasticity. There are likely to be common molecular and synaptic mechanisms underlying all rapid RF transformations, but also some striking differences that contribute to auditory perception and cognition.
In higher sensory cortices, there is a gradual transformation from sensation to perception and ac... more In higher sensory cortices, there is a gradual transformation from sensation to perception and action. In the auditory system, this transformation is revealed by responses in the rostral Ventral Posterior field (VPr), a tertiary area in ferret auditory cortex, which shows long-term learning in trained compared to naïve animals, arising from selectively enhanced responses to behaviorally relevant target stimuli. This enhanced representation is further amplified during active performance of spectral or temporal auditory discrimination tasks. VPr also shows sustained shortterm memory activity after target stimulus offset, correlated with task-response timing and action. These task-related changes in auditory filter properties enable VPr neurons to quickly and nimbly switch between different responses to the same acoustic stimuli, reflecting either spectrotemporal properties, timing or behavioral meaning of the sound. Furthermore, they demonstrate an interaction between dynamics of long-term learning and short-term attention as incoming sound is selectively attended, recognized and translated into action. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:
Highlights d Enhanced categorical representations in higher auditory areas during behavior d Dist... more Highlights d Enhanced categorical representations in higher auditory areas during behavior d Distinct sensory and categorical information flows during categorization tasks d Separate neuronal groups with intrinsic versus task-induced categorical responses d Similar dynamics in different categorization tasks with distinct acoustic features
Advances in Experimental Medicine and Biology, 2013
The context in which a stimulus occurs can influence its perception. We study contextual effects ... more The context in which a stimulus occurs can influence its perception. We study contextual effects in audition using the tritone paradox, where a pair of complex (Shepard) tones separated by half an octave can be perceived as ascending or descending. While ambiguous in isolation, they are heard with a clear upward or downward change in pitch, when preceded by spectrally matched biasing sequences. We presented these biased Shepard pairs to awake ferrets and obtained neuronal responses from primary auditory cortex. Using dimensionality reduction from the neural
Journal of the Acoustical Society of America, Mar 1, 2010
To explore the role of top-down projections from frontal cortex (FC) to auditory cortex (AC), whi... more To explore the role of top-down projections from frontal cortex (FC) to auditory cortex (AC), which may play a role in adaptive reshaping of A1 receptive fields to attended acoustic stimuli during behavior, simultaneous recordings were made from single neurons in FC and AC in behaving ferrets, trained on multiple auditory detection and discrimination tasks in positive and negative reinforcement paradigms. Performance required selective attention to different salient spectral frequency and/or temporal cues. Previous observations revealed task-specific transformations in receptive fields in AC [Fritz et al. (2003); (2005); (2007); (2009); Atiani et al. (2009)]. In contrast, FC neurons showed recognition responses, which categorically distinguished between acoustic foreground and background stimuli. FC responses to targets were often independent of the acoustic properties of the target and thus encoded an abstract representation of the class of target stimuli [Fritz et al. (2009)]. Stimulation of FC, paired with tones, lead to receptive field transformations similar to those observed in behavior. These results emphasize the importance of interactions between multiple areas during selective attention, and the tight coupling of target recognition and auditory attention that enhances auditory cortical filters for attended acoustic stimuli, thus creating a functional representation of task-salient sounds during behavior. [This work was supported by grants from NIDCD, NIH.]
IEEE Transactions on Signal Processing, Apr 1, 2016
We consider the problem of estimating the sparse time-varying parameter vectors of a point proces... more We consider the problem of estimating the sparse time-varying parameter vectors of a point process model in an online fashion, where the observations and inputs respectively consist of binary and continuous time series. We construct a novel objective function by incorporating a forgetting factor mechanism into the point process log-likelihood to enforce adaptivity and employ ℓ1-regularization to capture the sparsity. We provide a rigorous analysis of the maximizers of the objective function, which extends the guarantees of compressed sensing to our setting. We construct two recursive filters for online estimation of the parameter vectors based on proximal optimization techniques, as well as a novel filter for recursive computation of statistical confidence regions. Simulation studies reveal that our algorithms outperform several existing point process filters in terms of trackability, goodness-of-fit and mean square error. We finally apply our filtering algorithms to experimentally recorded spiking data from the ferret primary auditory cortex during attentive behavior in a click rate discrimination task. Our analysis provides new insights into the time-course of the spectrotemporal receptive field plasticity of the auditory neurons.
Sensory environments change over a wide dynamic range and sensory processing can change rapidly t... more Sensory environments change over a wide dynamic range and sensory processing can change rapidly to facilitate stable perception. While rapid changes may occur throughout the sensory processing pathway, cortical changes are believed to profoundly influence perception. Prior stimulation studies showed that orbitofrontal cortex (OFC) can modify receptive fields and sensory coding in A1, but the engagement of OFC during listening and the pathways mediating OFC influences on A1 are unknown. We show in mice that OFC neurons respond to sounds consistent with a role of OFC in audition. We then show in vitro that OFC axons are present in A1 and excite pyramidal and GABAergic cells in all layers of A1 via glutamatergic synapses. Optogenetic stimulation of OFC terminals in A1 in vivo evokes short-latency neural activity in A1 and pairing activation of OFC projections in A1 with sounds alters sound-evoked A1 responses. Together, our results identify a direct connection from OFC to A1 that can excite A1 neurons at the earliest stage of cortical processing, and thereby sculpt A1 receptive fields. These results are consistent with a role for OFC in adjusting to changing behavioral relevance of sensory inputs and modulating A1 receptive fields to enhance sound processing.
Population responses of cortical neurons encode considerable details about sensory stimuli, and t... more Population responses of cortical neurons encode considerable details about sensory stimuli, and the encoded information is likely to change with stimulus context and behavioral conditions. The details of encoding are difficult to discern across large sets of single neuron data because of the complexity of naturally occurring stimulus features and cortical receptive fields. To overcome this problem, we used the method of stimulus reconstruction to study how complex sounds are encoded in primary auditory cortex (AI). This method uses a linear spectro-temporal model to map neural population responses to an estimate of the stimulus spectrogram, thereby enabling a direct comparison between the original stimulus and its reconstruction. By assessing the fidelity of such reconstructions from responses to modulated noise stimuli, we estimated the range over which AI neurons can faithfully encode spectro-temporal features. For stimuli containing statistical regularities (typical of those found in complex natural sounds), we found that knowledge of these regularities substantially improves reconstruction accuracy over reconstructions that do not take advantage of this prior knowledge. Finally, contrasting stimulus reconstructions under different behavioral states showed a novel view of the rapid changes in spectro-temporal response properties induced by attentional and motivational state.
Acoustic filter properties of A1 neurons can dynamically adapt to stimulus statistics, classical ... more Acoustic filter properties of A1 neurons can dynamically adapt to stimulus statistics, classical conditioning, instrumental learning and the changing auditory attentional focus. We have recently developed an experimental paradigm that allows us to view cortical receptive field plasticity on-line as the animal meets different behavioral challenges by attending to salient acoustic cues and changing its cortical filters to enhance performance. We propose that attention is the key trigger that initiates a cascade of events leading to the dynamic receptive field changes that we observe. In our paradigm, ferrets were initially trained, using conditioned avoidance training techniques, to discriminate between background noise stimuli (temporally orthogonal ripple combinations) and foreground tonal target stimuli. They learned to generalize the task for a wide variety of distinct background and foreground target stimuli. We recorded cortical activity in the awake behaving animal and computed on-line spectrotemporal receptive fields (STRFs) of single neurons in A1. We observed clear, predictable task-related changes in STRF shape while the animal performed spectral tasks (including single tone and multi-tone detection, and two-tone discrimination) with different tonal targets. A different set of task-related changes occurred when the animal performed temporal tasks (including gap detection and click-rate discrimination). Distinctive cortical STRF changes may constitute a "task-specific signature". These spectral and temporal changes in cortical filters occur quite rapidly, within 2 minutes of task onset, and fade just as quickly after task completion, or in some cases, persisted for hours. The same cell could multiplex by differentially changing its receptive field in different task conditions. On-line dynamic task-related changes, as well as persistent plastic changes, were observed at a single-unit, multi-unit and population level. Auditory attention is likely to be pivotal in mediating these task-related changes since the magnitude of STRF changes correlated with behavioral performance on tasks with novel targets. Overall, these results suggest the presence of an attention-triggered plasticity algorithm in A1 that can swiftly change STRF shape by transforming receptive fields to enhance figure/ground separation, by using a contrast matched filter to filter out the background, while simultaneously enhancing the salient acoustic target in the foreground. These results favor the view of a nimble, dynamic, attentive and adaptive brain that can quickly reshape its sensory filter properties and sensori-motor links on a moment-to-moment basis, depending upon the current challenges the animal faces. In this review, we summarize our results in the context of a broader survey of the field of auditory attention, and then consider neuronal networks that could give rise to this phenomenon of attention-driven receptive field plasticity in A1.
Journal of the Acoustical Society of America, Mar 1, 2010
It is well known that humans can robustly perceive phonemes despite substantial variability acros... more It is well known that humans can robustly perceive phonemes despite substantial variability across speakers, context and natural distortions. This study examines the responses of neurons in primary auditory cortex (A1) to phonetically labeled speech stimuli in clean, additive noise and reverberant conditions. Using a linear decoder [Bialek (1991)] to reconstruct the input stimulus spectrogram from the population response, we observed that spectrograms reconstructed from the neural responses to noisy speech were closer to the original clean spectrograms than to the noisy ones. This indicates that sound representations in A1 serve to enhance information about natural speech signals relative to noise, thus extracting signal from noise. Examining the average reconstructed phoneme spectrograms in clean and noisy speech revealed a remarkable robustness in the encoding of features important for discrimination of different phonemes. In addition, it was found that the strict linear spectro-temporal receptive field (STRF) model of A1 neurons is insufficient to explain the noise robustness observed in the neural data. However, when a non-linear synaptic depression is integrated into the inputs for the STRF model, the noise was reduced in the reconstructed spectrograms similar to what observed with the actual neural data.
The auditory cortex plays a critical role in the perception and localization of complex sounds. I... more The auditory cortex plays a critical role in the perception and localization of complex sounds. It is functionally involved in the extraction and perception of the key auditory attributes of pitch, timbre, and localization. Its anatomical organization is reminiscent of other sensory cortical areas, with a tonotopic organization in multiple fields. Models of auditory function are becoming more sophisticated, attempting to account for the remarkable capabilities evident in segregating sounds in clutter, and overcoming noise and variability in sound recognition. The auditory cortex is also increasingly being viewed as a malleable structure that rapidly adapts to reflect task demands and environmental limitations, exhibiting plasticity in response to attention and other top-down influences.
Journal of the Acoustical Society of America, Dec 1, 2016
In order to explore the representation of sound features in auditory long-term memory, two groups... more In order to explore the representation of sound features in auditory long-term memory, two groups of ferrets were trained on Go vs Nogo, 3-zone classification tasks. The sound stimuli differed primarily along the spectral and temporal dimensions. In Group 1, two ferrets were trained to (i) classify tones based on their frequency (Tone-task), and subsequently learned to (ii) classify white noise based on its amplitude modulation rate (AM-task). In Group 2, two ferrets were trained to classify tones based on correlated combinations of their frequency and AM rate (AM-Tone task). Both groups of ferrets learned their tasks and were able to generalize performance along the trained spectral (tone frequency) or temporal (AM rate) dimensions. Insights into stimulus representations in memory were gained when the animals were tested with a diverse set of untrained probes that mixed features from the two dimensions. Animals exhibited a complex pattern of responses to the probes reflecting primarily the probes' spectral similarity with the training stimuli, and secondarily the temporal features of the stimuli. These diverse behavioral decisions could be well accounted for by a nearest-neighbor classifier model that relied on a multiscale spectrotemporal cortical representation of the training and probe sounds. V
We investigated the hypothesis that task performance can rapidly and adaptively reshape cortical ... more We investigated the hypothesis that task performance can rapidly and adaptively reshape cortical receptive field properties in accord with specific task demands and salient sensory cues. We recorded neuronal responses in the primary auditory cortex of behaving ferrets that were trained to detect a target tone of any frequency. Cortical plasticity was quantified by measuring focal changes in each cell's spectrotemporal response field (STRF) in a series of passive and active behavioral conditions. STRF measurements were made simultaneously with task performance, providing multiple snapshots of the dynamic STRF during ongoing behavior. Attending to a specific target frequency during the detection task consistently induced localized facilitative changes in STRF shape, which were swift in onset. Such modulatory changes may enhance overall cortical responsiveness to the target tone and increase the likelihood of 'capturing' the attended target during the detection task. Some receptive field changes persisted for hours after the task was over and hence may contribute to long-term sensory memory.
Proceedings of the International Symposium on Auditory and Audiological Research, Dec 15, 2007
We examined the responses of neurons in primary auditory cortex (A1) to phonetically labeled spee... more We examined the responses of neurons in primary auditory cortex (A1) to phonetically labeled speech stimuli. Sentences were taken from the TIMIT database and chosen to represent a diversity of male and female speakers. We presented these stimuli to awake ferrets while recording the activity of isolated A1 neurons. For analysis, we segmented the continuous speech samples into sequences of phonemes, which represent the smallest significant units of speech. We characterized the response properties of each neuron as the peristimulus time histogram (PSTH) response to each phoneme. Across a population of A1 neurons, we observed distinct patterns of phoneme selectivity that may provide a neural basis or low-level phoneme discrimination. We investigated how features of speech are encoded in A1 using a method for reconstructing the speech stimulus from the neural population responses. Stimuli were reconstructed using a linear spectro-temporal model to map the response to the stimulus spectrogram. We compared the accuracy of reconstruction across phonemes. One important factor involved in stimulus reconstruction is the presence of correlations in complex natural stimulus such as speech. Prior knowledge of regularities in the stimulus can benefit reconstruction in noise and when spectro-temporal coverage is limited. We studied the influence of prior knowledge of stimulus correlations, noise and spectro-temporal coverage on reconstruction accuracy in neural data and in simulation.
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Papers by Jonathan Fritz