Sparse coding of harmonic vocalization in monkey auditory cortex

Terashima, Hiroki; Hosoya, Haruo; Tani, Toshiki; Ichinohe, Noritaka; Okada, Masato

doi:10.1016/j.neucom.2012.07.009

Cited by 10 publications

(7 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This general approach of a parsimonious representation of the sensory information is the main premise of this paper and this finding resonates with ideas of a canonical computation by the nervous system [ 23 , 74 , 93 – 98 ]. Specifically, sparse representation of information can explain neural activity in the visual cortex [ 24 , 34 , 95 , 99 ]; the olfactory system of insects [ 100 , 101 ]; and findings in the mammalian auditory cortex [ 102 – 105 ] (however, see [ 106 ]). Hopefully, this paper is one small step in searching for such a generalized theory.…”

Section: Discussionmentioning

confidence: 99%

A New Approach to Model Pitch Perception Using Sparse Coding

Barzelay

Furst

Barak³

2017

PLoS Comput Biol

View full text Add to dashboard Cite

Our acoustical environment abounds with repetitive sounds, some of which are related to pitch perception. It is still unknown how the auditory system, in processing these sounds, relates a physical stimulus and its percept. Since, in mammals, all auditory stimuli are conveyed into the nervous system through the auditory nerve (AN) fibers, a model should explain the perception of pitch as a function of this particular input. However, pitch perception is invariant to certain features of the physical stimulus. For example, a missing fundamental stimulus with resolved or unresolved harmonics, or a low and high-level amplitude stimulus with the same spectral content–these all give rise to the same percept of pitch. In contrast, the AN representations for these different stimuli are not invariant to these effects. In fact, due to saturation and non-linearity of both cochlear and inner hair cells responses, these differences are enhanced by the AN fibers. Thus there is a difficulty in explaining how pitch percept arises from the activity of the AN fibers. We introduce a novel approach for extracting pitch cues from the AN population activity for a given arbitrary stimulus. The method is based on a technique known as sparse coding (SC). It is the representation of pitch cues by a few spatiotemporal atoms (templates) from among a large set of possible ones (a dictionary). The amount of activity of each atom is represented by a non-zero coefficient, analogous to an active neuron. Such a technique has been successfully applied to other modalities, particularly vision. The model is composed of a cochlear model, an SC processing unit, and a harmonic sieve. We show that the model copes with different pitch phenomena: extracting resolved and non-resolved harmonics, missing fundamental pitches, stimuli with both high and low amplitudes, iterated rippled noises, and recorded musical instruments.

show abstract

Section: Discussionmentioning

confidence: 99%

A New Approach to Model Pitch Perception Using Sparse Coding

Barzelay

Furst

Barak³

2017

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

“…The model included a non-negativity constraint that encouraged sparseness. Sensory processing research has often connected neural representations with sparse codes (34)(35)(36)(37). The Hoyer sparseness measure, a normalized ratio of 𝓁 1 and 𝓁 2 norms, is often preferred, on the basis of criteria discussed in the literature ( 46)…”

Section: Evaluating the Sparseness Of The Encodingmentioning

confidence: 99%

Compression of Dynamic Tactile Information in the Human Hand

Shao

2022

Springer Series on Touch and Haptic Systems

View full text Add to dashboard Cite

A key problem in the study of the senses is to describe how sense organs extract perceptual information from the physics of the environment. We previously observed that dynamic touch elicits mechanical waves that propagate throughout the hand. Here, we show that these waves produce an efficient encoding of tactile information. The computation of an optimal encoding of thousands of naturally occurring tactile stimuli yielded a compact lexicon of primitive wave patterns that sparsely represented the entire dataset, enabling touch interactions to be classified with an accuracy exceeding 95%. The primitive tactile patterns reflected the interplay of hand anatomy with wave physics. Notably, similar patterns emerged when we applied efficient encoding criteria to spiking data from populations of simulated tactile afferents. This finding suggests that the biomechanics of the hand enables efficient perceptual processing by effecting a preneuronal compression of tactile information.

show abstract

“…The encoding efficiency was also evaluated by measuring the sparseness of the activation weights h. The model included a non-negativity constraint that encouraged sparseness. Sensory processing research has often connected neural representations with sparse codes [187,188,189,190]. The Hoyer sparseness measure [191], a normalized ratio of 1 and 2 norms, is often preferred, based on criteria discussed in the literature [192],…”

Section: Encoding Efficiencymentioning

confidence: 99%

Tactile Sensing, Information, and Feedback via Wave Propagation

Shao

2022

Springer Series on Touch and Haptic Systems

View full text Add to dashboard Cite

Through my entire PhD study, I learned a lot from my colleagues in the RE Touch Lab. Discussions with Gregory Reardon about technical problems, like in physics or statistics, are always constructive and inspiring. Shantonu Biswas and Thanh Nho Do influenced me with their expertise in nanoengineering and material science. Also, I am lucky to have many supportive colleagues and friends in the lab. Anzu Kawazoe can always cheer me up with her lighthearted mood, unveiling the optimistic side of life to me. Zach Wells and Harald Schafer, beside research collaborations, brought me a lot of fun outside the lab, directing me to the ocean around Santa Barbara. I also very enjoyed the company of Yufei Hao, Taku Hachisu, Simone Fani, Jasper van der Lagemaat and Behnam Khojasteh during their visit to my lab. Starting from the time I joined the lab in Philadelphia, I have been gaining support from Marco Janko and Bin Li. I am really v grateful to have all of them as my colleagues. My friends outside the lab also helped me passing through the challenging paths toward the finish line of my PhD. I want to give special thanks to Mingwei Tang, my friend since childhood, who also obtained a PhD degree in the US. We traveled together to many sights and cities. Those trips reduced my anxiety and rebuilt my courage to march forward. I also value the friendship with Yan Kong and Zhenyu Yang, who provided me after-work entertainment in Harold Frank hall, and emotional support. I miss and appreciate the time with Pingge Jiang, Lei Song, Tao Sun, Yaou Zhou and Yanan Yang during my study in Philadelphia.I appreciate the love, care and support from my girlfriend, Huihui Zhang, during the last two years of my PhD. A delightful, sweet talk with her can always relieve my stress and hearten me, motivating me to look beyond the difficulties.Finally, I like to thank my family, especially my grandmother. I grew up bathing in her unconditional love. She was always proud of me and had the strongest belief in me, from preschool till PhD, granting me the faith to overcome any challenges. I deeply regret not being able to spend enough time with her at home.

show abstract

Sparse coding of harmonic vocalization in monkey auditory cortex

Cited by 10 publications

References 46 publications

A New Approach to Model Pitch Perception Using Sparse Coding

A New Approach to Model Pitch Perception Using Sparse Coding

Compression of Dynamic Tactile Information in the Human Hand

Tactile Sensing, Information, and Feedback via Wave Propagation

Contact Info

Product

Resources

About