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Mutual information calculation using empirical classification

NEUROCOMPUTING 2002年 44卷 1083-1088页

作者： Gruner, CM Pronghorn Engn Casper WY 82604 USA

In the past decade. researchers working on understanding the neural code have turned to mutual information as a measure of how well a given stimulus, response set codes information despite numerous difficulties, including convoluted calculation methods and difficulty interpreting the results. In this work, we use a new method for calculating mutual information based on empirical classification to show how mutual information and the Kullback-Leibler distance summarize coding efficacy. Our results suggest that knowledge gained through mutual information methods could be more easily obtained and interpreted using the Kullback-Leibler distance. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： neural coding mutual information Kullback-Leibler distance

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Integrating Statistical and Machine Learning Approaches for neural Classification

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IEEE ACCESS 2022年 10卷 119106-119118页

作者： Sarmashghi, Mehrad Jadhav, Shantanu P. Eden, Uri T. Boston Univ Div Syst Engn Boston MA 02215 USA Brandeis Univ Dept Psychol Waltham MA 02453 USA Boston Univ Dept Math & Stat Boston MA 02215 USA

Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as neural recording technologies have advanced to produce simultaneous spiking data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analyses;however, they typically require massive training sets with balanced data, along with accurate labels to fit well. Additionally, model assessment and interpretation are often more challenging for ML than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to demonstrate this framework, we apply these methods to data from a population of neurons recorded from rat hippocampus to characterize the distribution of spatial receptive fields in this region.

关键词： Deep learning large-scale neural data machine learning neural coding receptive field statistical models

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Visual attention mitigates information loss in small- and large-scale neural codes

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TRENDS IN COGNITIVE SCIENCES 2015年第4期19卷 215-226页

作者： Sprague, Thomas C. Saproo, Sameer Serences, John T. Univ Calif San Diego Grad Program Neurosci La Jolla CA 92093 USA Columbia Univ Dept Biomed Engn New York NY USA Univ Calif San Diego Dept Psychol La Jolla CA 92093 USA

The visual system transforms complex inputs into robust and parsimonious neural codes that efficiently guide behavior. Because neural communication is stochastic, the amount of encoded visual information necessarily decreases with each synapse. This constraint requires that sensory signals are processed in a manner that protects information about relevant stimuli from degradation. Such selective processing - or selective attention - is implemented via several mechanisms, including neural gain and changes in tuning properties. However, examining each of these effects in isolation obscures their joint impact on the fidelity of stimulus feature representations by large-scale population codes. Instead, large-scale activity patterns can be used to reconstruct representations of relevant and irrelevant stimuli, thereby providing a holistic understanding about how neuron-level modulations collectively impact stimulus encoding.

关键词： vision visual attention stimulus reconstruction information theory neural coding

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Geodesic-based distance reveals nonlinear topological features in neural activity from mouse visual cortex

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BIOLOGICAL CYBERNETICS 2022年第1期116卷 53-68页

作者： Beshkov, Kosio Tiesinga, Paul Radboud Univ Nijmegen Donders Ctr Neurosci Neuroinformat Dept NL-6500 GL Nijmegen Netherlands Bulgarian Acad Sci Inst Neurobiol Acad G Bonchev StrBl 23 Sofia 1113 Bulgaria Univ Oslo Dept Biosci Blindernveien 310371 Oslo Norway

An increasingly popular approach to the analysis of neural data is to treat activity patterns as being constrained to and sampled from a manifold, which can be characterized by its topology. The persistent homology method identifies the type and number of holes in the manifold, thereby yielding functional information about the coding and dynamic properties of the underlying neural network. In this work, we give examples of highly nonlinear manifolds in which the persistent homology algorithm fails when it uses the Euclidean distance because it does not always yield a good approximation to the true distance distribution of a point cloud sampled from a manifold. To deal with this issue, we instead estimate the geodesic distance which is a better approximation of the true distance distribution and can therefore be used to successfully identify highly nonlinear features with persistent homology. To document the utility of the method, we utilize a toy model comprised of a circular manifold, built from orthogonal sinusoidal coordinate functions and show how the choice of metric determines the performance of the persistent homology algorithm. Furthermore, we explore the robustness of the method across different manifold properties, like the number of samples, curvature and amount of added noise. We point out strategies for interpreting its results as well as some possible pitfalls of its application. Subsequently, we apply this analysis to neural data coming from the Visual coding-Neuropixels dataset recorded at the Allen Institute in mouse visual cortex in response to stimulation with drifting gratings. We find that different manifolds with a non-trivial topology can be seen across regions and stimulus properties. Finally, we interpret how these changes in manifold topology along with stimulus parameters and cortical region inform how the brain performs visual computation.

关键词： Topological data analysis Persistent homology neural coding Geodesics neural manifolds Visual processing

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Finding neural codes using random projections

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NEUROCOMPUTING 2004年 58卷 19-25页

作者： Mumey, B Sarkar, A Gedeon, T Dimitrov, A Miller, J Montana State Univ Dept Comp Sci Bozeman MT 59717 USA Montana State Univ Dept Cell Biol & Neurosci Bozeman MT 59717 USA Montana State Univ Dept Math Sci Bozeman MT 59717 USA

A powerful approach to studying how information is transmitted in basic neural systems is based on finding stimulus-response classes that optimize the mutual information shared between the classes. The problem can be formally described in terms of finding a optimal quantization (A, B) of a large discrete joint (X, Y) distribution and various algorithms have been developed for this purpose. Recently, it has been proved that finding the optimal such quantization is NP-complete (optimal mutual information quantization is NP-complete, neural information coding indicating that exact solutions may be computationally infeasible to find in some circumstances. We have developed a new randomized algorithm to solve the joint quantization problem. Under assumptions about the underlying (X, Y) distribution, we prove that this algorithm converges to the "true" optimal quantization with high probability that can be increased by performing additional random trials. (C) 2004 Elsevier B.V. All rights reserved.

关键词： neural coding random projections information quantization

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Learning self-organized topology-preserving complex speech features at primary auditory cortex

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NEUROCOMPUTING 2005年第SPEC. ISS.期65卷 793-800页

作者： Kim, T Lee, SY Korea Adv Inst Sci & Technol Dept BioSyst Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept EECS Brain Sci Res Ctr Taejon 305701 South Korea

By applying independent component analysis (ICA) algorithm to auditory signals a computational model was developed for the speech feature extraction at the primary auditory cortex. Unlike the other ICA-based features with simple frequency selectivity at the basilar membrane and inner hair cells the learnt features represent complex signal characteristics at the primary auditory cortex such as onset/offset and frequency modulation in time. Also, the topology is preserved with the help of neighborhood coupling during the self-organization. The extracted complex features demonstrated good performance for the robust discrimination of speech phonemes. (c) 2004 Elsevier B.V. All rights reserved.

关键词： independent component analysis auditory cortex complex speech features topology-preserving self-organization neural coding

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Activity in Primary Motor Cortex Related to Visual Feedback

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CELL REPORTS 2019年第12期29卷 3872-+页

作者： Suway, Steven B. Schwartz, Andrew B. Univ Pittsburgh Ctr Neurosci Pittsburgh PA 15260 USA Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Univ Pittsburgh Pittsburgh PA 15213 USA Univ Pittsburgh Syst Neurosci Ctr Pittsburgh PA 15260 USA Univ Pittsburgh Dept Neurobiol Pittsburgh PA 15260 USA

neural modulation in primate motor cortex exhibits complex patterns. We found that modulation during reaching could be separated into discrete temporal epochs. To determine if these epochs are driven by behavioral events, monkeys performed variations of a center-out reaching task. Monkeys viewed a computer cursor matched to hand position and a radial target at 1 of 16 locations. In some trials, they performed a visuomotor rotation (the cursor moved at an angle to the hand). After adaptation, encoding changes for single units are temporally structured: adaptation could affect one temporal component of a unit's response but not another. In half the normal and perturbed trials, we removed visual feedback before movement. Adaptation-sensitive firing components toward the end of movement are often weak or absent during reaches without feedback. These results show that temporal structure in motor cortical activity is driven by behavior, with a discrete component related to visual feedback.

关键词： motor cortex neural coding reaching visual feedback visuomotor rotation

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Synaptic interactions increase optic flow specificity

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EUROPEAN JOURNAL OF NEUROSCIENCE 2000年第6期12卷 2157-2165页

作者： Horstmann, W Egelhaaf, M Warzecha, AK Univ Bielefeld Fak Biol Lehrstuhl Neurobiol D-33501 Bielefeld Germany

Representations of optic flow are encoded in fly tangential neurons by pooling the signals of many retinotopically organized local motion-sensitive inputs as well as of other tangential cells originating in the ipsi- and contralateral half of the brain. In the so called HSE cell, a neuron involved in optomotor course control, two contralateral input elements, the H1 and H2 cells, mediate distinct EPSPs. These EPSPs frequently elicit spike-like depolarizations in the HSE cell. The synaptic transmission between the H2 and the HSE cell is analysed in detail and shown to be very reliable with respect to the amplitude and time-course of the postsynaptic potential. As a consequence of its synaptic input, the HSE cell responds best to wide-field motion, such as that generated on the eyes when the animal turns about its vertical body axis. It is shown that the specificity of the HSE cell for this type of optic flow is much enhanced if rapid membrane depolarizations, such as large-amplitude EPSPs or spike-like depolarizations, are taken into account rather than the average membrane potential.

关键词： fly motion vision neural coding reliability

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Modulation of central gustatory coding by temperature

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JOURNAL OF NEUROPHYSIOLOGY 2013年第5期110卷 1117-1129页

作者： Wilson, David M. Lemon, Christian H. St Louis Univ Sch Med Dept Pharmacol & Physiol Sci St Louis MO 63104 USA

Changes in oral temperature can influence taste perception, indicating overlap among mechanisms for taste and oral somesthesis. Medullary gustatory neurons can show cosensitivity to temperature, albeit how these cells process combined taste and thermal input is poorly understood. Here, we electrophysiologically recorded orosensory responses (spikes) from 39 taste-sensitive neurons in the nucleus tractus solitarii of anesthetized mice during oral delivery of tastants adjusted to innocuous cool (16 and 18 degrees C), room (22 degrees C, baseline), and warm (30 and 37 degrees C) oral temperatures. Stimuli included (in mM) 100 sucrose, 30 NaCl, 3 HCl, 3 quinine, an umami mixture, and water. Although cooled water excited few cells, water warmed to 30 and 37 degrees C significantly excited 33% and 64% of neurons, respectively. Warmth induced responses of comparable magnitude to room temperature tastants. Furthermore, warming taste solutions influenced the distribution of gustatory responses among neurons and increased (P < 0.05) neuronal breadth of tuning across taste qualities. The influence of warmth on response magnitude was stimulus specific. Across neurons, warming facilitated responses to sucrose and umami in a superadditive manner, as these responses exceeded (P < 0.05) the arithmetic sum of activity to warming alone and the taste stimulus tested at room temperature. Superadditive increases (P < 0.05) in responding were also noted in some cells for warmed HCl. Yet warming induced only simple additive or subtractive effects on responses to quinine and NaCl. Data show temperature is a parameter of gustatory processing, like taste quality and concentration, in medullary circuits for taste.

关键词： temperature taste multimodal neural coding nucleus of the solitary tract

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Advances in Neuroscience, Not Devices, Will Determine the Effectiveness of Visual Prostheses

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SEMINARS IN OPHTHALMOLOGY 2021年第4期36卷 168-175页

作者： Abbasi, Bardia Rizzo, Joseph F. Massachusetts Eye & Ear Dept Ophthalmol Neuroophthalmol Serv Boston MA 02114 USA Harvard Med Sch Boston MA 02115 USA

Background: Innovations in engineering and neuroscience have enabled the development of sophisticated visual prosthetic devices. In clinical trials, these devices have provided visual acuities as high as 20/460, enabled coarse navigation, and even allowed for reading of short words. However, long-term commercial viability arguably rests on attaining even better vision and more definitive improvements in tasks of daily living and quality of life. Purpose: Here we review technological and biological obstacles in the implementation of visual prosthetics. Conclusions: Research in the visual prosthetic field has tackled significant technical challenges, including biocompatibility, signal spread through neural tissue, and inadvertent activation of passing axons;however, significant gaps in knowledge remain in the realm of neuroscience, including the neural code of vision and visual plasticity. We assert that further optimization of prosthetic devices alone will not provide markedly improved visual outcomes without significant advances in our understanding of neuroscience.

关键词： Vision restoration visual prosthetic neural stimulation neural coding neural plasticity

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