@article {3563, title = {Adaptive Coding for Dynamic Sensory Inference}, journal = {eLife}, year = {2018}, month = {07/2018}, abstract = {

Behavior relies on the ability of sensory systems to infer properties of the environment from incoming stimuli. The accuracy of inference depends on the fidelity with which behaviorally-relevant properties of stimuli are encoded in neural responses. High-fidelity encodings can be metabolically costly, but low-fidelity encodings can cause errors in inference. Here, we discuss general principles that underlie the tradeoff between encoding cost and inference error. We then derive adaptive encoding schemes that dynamically navigate this tradeoff. These optimal encodings tend to increase the fidelity of the neural representation following a change in the stimulus distribution, and reduce fidelity for stimuli that originate from a known distribution. We predict dynamical signatures of such encoding schemes and demonstrate how known phenomena, such as burst coding and firing rate adaptation, can be understood as hallmarks of optimal coding for accurate inference.

Link to bioRxiv preprint: https://www.biorxiv.org/content/early/2018/04/01/189506

}, author = {Wiktor Mlynarski and Ann M. Hermundstad} } @article {3565, title = {Co-occurrence statistics of natural sound features predict perceptual grouping}, year = {2018}, month = {03/2018}, address = {Denver, Colorado}, url = {http://www.cosyne.org/c/index.php?title=Cosyne_18}, author = {Wiktor Mlynarski and Josh H. McDermott} } @article {3578, title = {Co-occurrence statistics of natural sound features predict perceptual grouping}, year = {2018}, author = {Wiktor Mlynarski and Josh H. McDermott} } @article {3562, title = {Learning Mid-Level Auditory Codes from Natural Sound Statistics}, journal = {Neural Computation}, volume = {30}, year = {2018}, month = {03/2018}, pages = {631-669}, author = {Wiktor Mlynarski and Josh H. McDermott} } @article {2668, title = {Adaptive Compression of Statistically Homogenous Sensory Signals}, year = {2017}, author = {Wiktor Mlynarski and Josh H. McDermott} } @article {2386, title = {Learning Mid-Level Auditory Codes from Natural Sound Statistics}, year = {2017}, month = {01/2017}, abstract = {

Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through cascades of neuronal processing stages in which neurons at each stage recode the output of preceding stages. Explanations of sensory coding may thus involve understanding how low-level patterns are combined into more complex structures. Although models exist in the visual domain to explain how mid-level features such as junctions and curves might be derived from oriented filters in early visual cortex, little is known about analogous grouping principles for mid-level auditory representations. We propose a hierarchical generative model of natural sounds that learns combinations of spectrotemporal features from natural stimulus statistics. In the first layer the model forms a sparse convolutional code of spectrograms using a dictionary of learned spectrotemporal kernels. To generalize from specific kernel activation patterns, the second layer encodes patterns of time-varying magnitude of multiple first layer coefficients. Because second-layer features are sensitive to combinations of spectrotemporal features, the representation they support encodes more complex acoustic patterns than the first layer. When trained on corpora of speech and environmental sounds, some second-layer units learned to group spectrotemporal features that occur together in natural sounds. Others instantiate opponency between dissimilar sets of spectrotemporal features. Such groupings might be instantiated by neurons in the auditory cortex, providing a hypothesis for mid-level neuronal computation.

}, author = {Wiktor Mlynarski and Josh H. McDermott} } @article {2666, title = {Learning Mid-Level Codes for Natural Sounds}, year = {2017}, author = {Wiktor Mlynarski and Josh H. McDermott} } @article {2667, title = {Lossy Compression of Uninformative Stimuli in the Auditory System}, year = {2017}, author = {Wiktor Mlynarski and Josh H. McDermott} } @article {1797, title = {Learning mid-level codes for natural sounds}, year = {2016}, month = {02/2016}, address = {Salt Lake City, UT}, abstract = {

Auditory perception depends critically on abstract and behaviorally meaningful representations of natural auditory scenes. These representations are implemented by cascades of neuronal processing stages in which neurons at each stage recode outputs of preceding units. Explanations of auditory coding strategies must thus involve understanding how low-level acoustic patterns are combined into more complex structures. While models exist in the visual domain to explain how phase invariance is achieved by V1 complex cells, and how curvature representations emerge in V2, little is known about analogous grouping principles for mid-level auditory representations.

We propose a hierarchical, generative model of natural sounds that learns combinations of spectrotemporal features from natural stimulus statistics. In the first layer the model forms a sparse, convolutional code of spectrograms. Features learned on speech and environmental sounds resemble spectrotemporal receptive fields (STRFs) of mid-brain and cortical neurons, consistent with previous findings [1]. To generalize from specific STRF activation patterns, the second layer encodes patterns of time-varying magnitude (i.e. variance) of multiple first layer coefficients. Because it forms a code of a non- stationary distribution of STRF activations, it is partially invariant to their specific values. Moreover, because second-layer features are sensitive to STRF combinations, the representation they support is more selective to complex acoustic patterns. The second layer substantially improved the model{\textquoteright}s performance on a denoising task, implying a closer match to the natural stimulus distribution.

Quantitative hypotheses emerge from the model regarding selectivity of auditory neurons characterized by multidimensional STRFs [2] and sensitivity to increasingly more abstract structure [3]. The model also predicts that the auditory system constructs representations progressively more invariant to noise, consistent with recent experimental findings [4]. Our results suggest that mid-level auditory representations may be derived from high-order stimulus dependencies present in the natural environment.\ 

}, url = {http://www.cosyne.org/c/index.php?title=Cosyne2016_posters_2}, author = {Wiktor Mlynarski and Josh H. McDermott} } @article {2664, title = {Learning Mid-Level Codes for Natural Sounds}, year = {2016}, author = {Wiktor Mlynarski and Josh H. McDermott} } @article {2665, title = {Lossy Compression of Sound Texture by the Human Auditory System}, year = {2016}, author = {Wiktor Mlynarski and Josh H. McDermott} }