Optimal neuronal tuning curves - an exact Bayesian study of dynamic adaptivity

Sets of neuronal tuning curves, which describe the responses of neurons as functions of a stimulus, can serve as a basis for approximating other functions of stimulus parameters. In a function-approximating network, synaptic weights determined by a correlation-based Hebbian rule are closely related to the coefficients that result when a function is expanded in an orthogonal basis. Although neuronal tuning curves typically are not orthogonal functions, the relationship between function approximation and correlation-based synaptic weights can be retained if the tuning curves satisfy the conditions of a tight frame. We examine whether the spatial receptive fields of simple cells in cat and monkey primary visual cortex (V1) form a tight frame, allowing them to serve as a basis for constructing more complicated extrastriate receptive fields using correlation-based synaptic weights. Our calculations show that the set of V1 simple cell receptive fields is not tight enough to account for the acuity observed psychophysically.

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AP tuning curves from normal and pathological human and guinea pig cochleas

The Journal of the Acoustical Society of America ◽

10.1121/1.385819 ◽

1981 ◽

Vol 69 (5) ◽

pp. 1374-1385 ◽

Cited By ~ 67

Author(s):

Robert V. Harrison ◽

Jean–Marie Aran ◽

Jean–Paul Erre

Keyword(s):

Guinea Pig ◽

Tuning Curves

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Population Vectors Can Provide Near Optimal Integration of Information

Neural Computation ◽

10.1162/neco_a_00992 ◽

2017 ◽

Vol 29 (8) ◽

pp. 2021-2029

Author(s):

Josue Orellana ◽

Jordan Rodu ◽

Robert E. Kass

Keyword(s):

Bayesian Inference ◽

Point Process ◽

Neural Mechanism ◽

Input Neuron ◽

Tuning Curves ◽

Optimal Integration ◽

Bayesian Integration ◽

Combine Information ◽

Input Spike ◽

Combine Evidence

Much attention has been paid to the question of how Bayesian integration of information could be implemented by a simple neural mechanism. We show that population vectors based on point-process inputs combine evidence in a form that closely resembles Bayesian inference, with each input spike carrying information about the tuning of the input neuron. We also show that population vectors can combine information relatively accurately in the presence of noisy synaptic encoding of tuning curves.

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Response Properties of Single Auditory Nerve Fibers in the Mouse

Journal of Neurophysiology ◽

10.1152/jn.00574.2004 ◽

2005 ◽

Vol 93 (1) ◽

pp. 557-569 ◽

Cited By ~ 268

Author(s):

Annette M. Taberner ◽

M. Charles Liberman

Keyword(s):

Auditory Nerve ◽

Dynamic Range ◽

Phase Locking ◽

Nerve Fibers ◽

Cochlear Function ◽

Response Properties ◽

Tuning Curves ◽

Rate Versus ◽

Interstrain Difference ◽

Mutant Lines

The availability of transgenic and mutant lines makes the mouse a valuable model for study of the inner ear, and a powerful window into cochlear function can be obtained by recordings from single auditory nerve (AN) fibers. This study provides the first systematic description of spontaneous and sound-evoked discharge properties of AN fibers in mouse, specifically in CBA/CaJ and C57BL/6 strains, both commonly used in auditory research. Response properties of 196 AN fibers from CBA/CaJ and 58 from C57BL/6 were analyzed, including spontaneous rates (SR), tuning curves, rate versus level functions, dynamic range, response adaptation, phase-locking, and the relation between SR and these response properties. The only significant interstrain difference was the elevation of high-frequency thresholds in C57BL/6. In general, mouse AN fibers showed similar responses to other mammals: sharpness of tuning increased with characteristic frequency, which ranged from 2.5 to 70 kHz; SRs ranged from 0 to 120 sp/s, and fibers with low SR (<1 sp/s) had higher thresholds, and wider dynamic ranges than fibers with high SR. Dynamic ranges for mouse high-SR fibers were smaller (<20 dB) than those seen in other mammals. Phase-locking was seen for tone frequencies <4 kHz. Maximum synchronization indices were lower than those in cat but similar to those found in guinea pig.

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A comparison of AP and ABR tuning curves in the guinea pig

Hearing Research ◽

10.1016/0378-5955(87)90091-8 ◽

1987 ◽

Vol 25 (2-3) ◽

pp. 193-204 ◽

Cited By ~ 15

Author(s):

Carolyn J. Browri ◽

Paul J. Abbas

Keyword(s):

Guinea Pig ◽

Tuning Curves

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The Physiologic and Psychophysical Consequences of Severe-to-Profound Hearing Loss

Seminars in Hearing ◽

10.1055/s-0038-1670698 ◽

2018 ◽

Vol 39 (04) ◽

pp. 349-363 ◽

Cited By ~ 3

Author(s):

Eric Hoover ◽

Pamela Souza

Keyword(s):

Hearing Loss ◽

Speech Recognition ◽

Treatment Options ◽

Sufficient Information ◽

Noisy Environments ◽

Cochlear Function ◽

Profound Hearing Loss ◽

Sound Perception ◽

Severe Hearing Loss ◽

Tuning Curves

AbstractSubstantial loss of cochlear function is required to elevate pure-tone thresholds to the severe hearing loss range; yet, individuals with severe or profound hearing loss continue to rely on hearing for communication. Despite the impairment, sufficient information is encoded at the periphery to make acoustic hearing a viable option. However, the probability of significant cochlear and/or neural damage associated with the loss has consequences for sound perception and speech recognition. These consequences include degraded frequency selectivity, which can be assessed with tests including psychoacoustic tuning curves and broadband rippled stimuli. Because speech recognition depends on the ability to resolve frequency detail, a listener with severe hearing loss is likely to have impaired communication in both quiet and noisy environments. However, the extent of the impairment varies widely among individuals. A better understanding of the fundamental abilities of listeners with severe and profound hearing loss and the consequences of those abilities for communication can support directed treatment options in this population.

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Statistical assessment of the stability of neural movement representations

Journal of Neurophysiology ◽

10.1152/jn.00626.2010 ◽

2011 ◽

Vol 106 (2) ◽

pp. 764-774 ◽

Cited By ~ 41

Author(s):

Ian H. Stevenson ◽

Anil Cherian ◽

Brian M. London ◽

Nicholas A. Sachs ◽

Eric Lindberg ◽

...

Keyword(s):

Primary Motor Cortex ◽

Tuning Curve ◽

Modulation Depth ◽

Measurement Noise ◽

Neural Representation ◽

Experimental Conditions ◽

Sensory Stimuli ◽

Tuning Curves ◽

Dynamic Tuning ◽

Preferred Direction

In systems neuroscience, neural activity that represents movements or sensory stimuli is often characterized by spatial tuning curves that may change in response to training, attention, altered mechanics, or the passage of time. A vital step in determining whether tuning curves change is accounting for estimation uncertainty due to measurement noise. In this study, we address the issue of tuning curve stability using methods that take uncertainty directly into account. We analyze data recorded from neurons in primary motor cortex using chronically implanted, multielectrode arrays in four monkeys performing center-out reaching. With the use of simulations, we demonstrate that under typical experimental conditions, the effect of neuronal noise on estimated preferred direction can be quite large and is affected by both the amount of data and the modulation depth of the neurons. In experimental data, we find that after taking uncertainty into account using bootstrapping techniques, the majority of neurons appears to be very stable on a timescale of minutes to hours. Lastly, we introduce adaptive filtering methods to explicitly model dynamic tuning curves. In contrast to several previous findings suggesting that tuning curves may be in constant flux, we conclude that the neural representation of limb movement is, on average, quite stable and that impressions to the contrary may be largely the result of measurement noise.

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