Limitations of Neural Map Topography for Decoding Spatial Information.
نویسندگان
چکیده
UNLABELLED Topographic maps are common throughout the nervous system, yet their functional role is still unclear. In particular, whether they are necessary for decoding sensory stimuli is unknown. Here we examined this question by recording population activity at the cellular level from the larval zebrafish tectum in response to visual stimuli at three closely spaced locations in the visual field. Due to map imprecision, nearby stimulus locations produced intermingled tectal responses, and decoding based on map topography yielded an accuracy of only 64%. In contrast, maximum likelihood decoding of stimulus location based on the statistics of the evoked activity, while ignoring any information about the locations of neurons in the map, yielded an accuracy close to 100%. A simple computational model of the zebrafish visual system reproduced these results. Although topography is a useful initial decoding strategy, we suggest it may be replaced by better methods following visual experience. SIGNIFICANCE STATEMENT A very common feature of brain wiring is that neighboring points on a sensory surface (eg, the retina) are connected to neighboring points in the brain. It is often assumed that this "topography" of wiring is essential for decoding sensory stimuli. However, here we show in the developing zebrafish that topographic decoding performs very poorly compared with methods that do not rely on topography. This suggests that, although wiring topography could provide a starting point for decoding at a very early stage in development, it may be replaced by more accurate methods as the animal gains experience of the world.
منابع مشابه
Landforms identification using neural network-self organizing map and SRTM data
During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...
متن کاملDecoding Neurally Relevant Musical Features Using Canonical Correlation Analysis
Music Information Retrieval (MIR) has been dominated by computational approaches. The possibility of leveraging neural systems via brain-computer interfaces is an alternative approach to annotating music. Here we test this idea by measuring correlations between musical features and brain responses in a statistically optimal fashion. Using an extensive dataset of electroencephalographic (EEG) re...
متن کاملIntegrated Environmental Analysis using GIS for Rational Planning of Conservatory Management of Slopes Application in the Ouergha Basin (Morocco)
The objective of this work is the realization of a map spatialising proposals of management and planning of lands, with a view to their rational management within the framework of a sustainable development. It was based on a diagnosis of the natural environment that allowed the analysis and identification of constraints to the development of the watershed of Ouergha (North of MOROCCO). The meth...
متن کاملStatistical analysis of neural data: Maximum a posteriori techniques for decoding spike trains
2 Maximum a posteriori neural decoding 3 2.1 Gaussian approximations to the posterior p(~x|D) are tractable and useful . . 4 2.1.1 Moment-matching provides an alternative method for constructing the Gaussian approximation . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Numerical implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 MAP decoding examples: corre...
متن کاملUnderstanding the visual cortex by using classification techniques. (Améliorer la compréhension du cortex visuel à l'aide de techniques de classification)
In this thesis, we present different approaches for statistical learning that can be used for studying the neural code of cognitive functions, based on brain functional Magnetic Resonance Imaging (fMRI) data. In particular, we study the spatial organization of the neural code, i.e. the spatial localization and the respective weights of the different entities implied in the neural coding. In thi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- The Journal of neuroscience : the official journal of the Society for Neuroscience
دوره 36 19 شماره
صفحات -
تاریخ انتشار 2016