Population coding of visual stimuli by cortical neurons tuned to more than one dimension

1992 ◽  
Vol 66 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Ehud Zohary
Keyword(s):  
Cortical Neurons ◽  
Visual Stimuli ◽  
Population Coding ◽  
One Dimension

scholarly journals Optimal Tuning Widths in Population Coding of Periodic Variables

2006 ◽  
Vol 18 (7) ◽  
pp. 1555-1576 ◽  
Author(s):  
Marcelo A. Montemurro ◽  
Stefano Panzeri
Keyword(s):  
Cortical Neurons ◽  
Neuronal Population ◽  
Population Coding ◽  
Model Parameters ◽  
Population Activity ◽  
Sensory Stimuli ◽  
Tuning Curves ◽  
Periodic Stimulus ◽  
Tuning Width ◽  
Stimulus Features

We study the relationship between the accuracy of a large neuronal population in encoding periodic sensory stimuli and the width of the tuning curves of individual neurons in the population. By using general simple models of population activity, we show that when considering one or two periodic stimulus features, a narrow tuning width provides better population encoding accuracy. When encoding more than two periodic stimulus features, the information conveyed by the population is instead maximal for finite values of the tuning width. These optimal values are only weakly dependent on model parameters and are similar to the width of tuning to orientation ormotion direction of real visual cortical neurons. A very large tuning width leads to poor encoding accuracy, whatever the number of stimulus features encoded. Thus, optimal coding of periodic stimuli is different from that of nonperiodic stimuli, which, as shown in previous studies, would require infinitely large tuning widths when coding more than two stimulus features.

scholarly journals Cortical synaptic diversity naturally arises with heterogeneous neural populations

2020 ◽  
Author(s):  
Jacob L. Yates ◽  
Benjamin Scholl
Keyword(s):  
Functional Diversity ◽  
Dendritic Spines ◽  
Cortical Neurons ◽  
Coding Theory ◽  
Population Coding ◽  
Functional Heterogeneity ◽  
Noisy Input ◽  
Neural Populations ◽  
Extract Information

AbstractSynaptic inputs onto single cortical neurons in vivo exhibit substantial functional diversity with respect to sensory-driven activity. However, it is unclear what this diversity reflects, appearing counter-productive in generating tuned responses to specific stimuli. We propose that functional diversity naturally arises if neurons extract information encoded from noisy input populations. Focusing on a single sensory variable, orientation, we construct a probabilistic decoder that estimates orientation from the responses of a realistic hypothetical input population of neurons. Analytically derived weights exhibit diversity when input populations consist of noisy, correlated, and heterogeneous neurons. Weight diversity was necessary to accurately decode orientation. Further, in silico weight diversity matched the functional heterogeneity of dendritic spines imaged in vivo. This suggests that synaptic diversity is expected when information is extracted from realistic input populations, highlighting the importance of studying weighting structures in population coding theory and consideration in pursuits of the cortical connectome.

Neural Population Coding of Natural Sounds in Non-flying Mammals

2017 ◽  
Author(s):  
Israel Nelken
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Cortical Neurons ◽  
Dominant Role ◽  
Primary Auditory Cortex ◽  
Population Coding ◽  
Neural Population ◽  
Good Effect ◽  
Natural Sounds ◽  
Complex Sounds

Understanding the principles by which sensory systems represent natural stimuli is one of the holy grails of neuroscience. In the auditory system, the study of the coding of natural sounds has a particular prominence. Indeed, the relationships between neural responses to simple stimuli (usually pure tone bursts)—often used to characterize auditory neurons—and complex sounds (in particular natural sounds) may be complex. Many different classes of natural sounds have been used to study the auditory system. Sound families that researchers have used to good effect in this endeavor include human speech, species-specific vocalizations, an “acoustic biotope” selected in one way or another, and sets of artificial sounds that mimic important features of natural sounds. Peripheral and brainstem representations of natural sounds are relatively well understood. The properties of the peripheral auditory system play a dominant role, and further processing occurs mostly within the frequency channels determined by these properties. At the level of the inferior colliculus, the highest brainstem station, representational complexity increases substantially due to the convergence of multiple processing streams. Undoubtedly, the most explored part of the auditory system, in term of responses to natural sounds, is the primary auditory cortex. In spite of over 50 years of research, there is still no commonly accepted view of the nature of the population code for natural sounds in the auditory cortex. Neurons in the auditory cortex are believed by some to be primarily linear spectro-temporal filters, by others to respond to conjunctions of important sound features, or even to encode perceptual concepts such as “auditory objects.” Whatever the exact mechanism is, many studies consistently report a substantial increase in the variability of the response patterns of cortical neurons to natural sounds. The generation of such variation may be the main contribution of auditory cortex to the coding of natural sounds.

scholarly journals Synthesis of a comprehensive population code for contextual features in the awake sensory cortex

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Evan H Lyall ◽  
Daniel P Mossing ◽  
Scott R Pluta ◽  
Yun Wen Chu ◽  
Amir Dudai ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Spatial Summation ◽  
Higher Order ◽  
Population Coding ◽  
Population Code ◽  
Complex Objects ◽  
Global Features ◽  
Sensory Stimuli ◽  
Stimulus Features ◽  
Complex Features

How cortical circuits build representations of complex objects is poorly understood. Individual neurons must integrate broadly over space, yet simultaneously obtain sharp tuning to specific global stimulus features. Groups of neurons identifying different global features must then assemble into a population that forms a comprehensive code for these global stimulus properties. Although the logic for how single neurons summate over their spatial inputs has been well-explored in anesthetized animals, how large groups of neurons compose a flexible population code of higher order features in awake animals is not known. To address this question, we probed the integration and population coding of higher order stimuli in the somatosensory and visual cortices of awake mice using two-photon calcium imaging across cortical layers. We developed a novel tactile stimulator that allowed the precise measurement of spatial summation even in actively whisking mice. Using this system, we found a sparse but comprehensive population code for higher order tactile features that depends on a heterogeneous and neuron-specific logic of spatial summation beyond the receptive field. Different somatosensory cortical neurons summed specific combinations of sensory inputs supra-linearly, but integrated other inputs sub-linearly, leading to selective responses to higher order features. Visual cortical populations employed a nearly identical scheme to generate a comprehensive population code for contextual stimuli. These results suggest that a heterogeneous logic of input-specific supra-linear summation may represent a widespread cortical mechanism for the synthesis of sparse higher order feature codes in neural populations. This may explain how the brain exploits the thalamocortical expansion of dimensionality to encode arbitrary complex features of sensory stimuli.

Temporally precise population coding of dynamic sounds by auditory cortex

2021 ◽  
Author(s):  
Joshua D Downer ◽  
James Bigelow ◽  
Melissa Runfeldt ◽  
Brian James Malone
Keyword(s):  
Auditory Cortex ◽  
Cortical Neurons ◽  
Frequency Tuning ◽  
Average Rate ◽  
Relative Effectiveness ◽  
Primary Auditory Cortex ◽  
Population Based ◽  
Population Coding ◽  
Complex Sounds ◽  
Animal Vocalizations

Fluctuations in the amplitude envelope of complex sounds provide critical cues for hearing, particularly for speech and animal vocalizations. Responses to amplitude modulation (AM) in the ascending auditory pathway have chiefly been described for single neurons. How neural populations might collectively encode and represent information about AM remains poorly characterized, even in primary auditory cortex (A1). We modeled population responses to AM based on data recorded from A1 neurons in awake squirrel monkeys and evaluated how accurately single trial responses to modulation frequencies from 4 to 512 Hz could be decoded as functions of population size, composition, and correlation structure. We found that a population-based decoding model that simulated convergent, equally weighted inputs was highly accurate and remarkably robust to the inclusion of neurons that were individually poor decoders. By contrast, average rate codes based on convergence performed poorly; effective decoding using average rates was only possible when the responses of individual neurons were segregated, as in classical population decoding models using labeled lines. The relative effectiveness of dynamic rate coding in auditory cortex was explained by shared modulation phase preferences among cortical neurons, despite heterogeneity in rate-based modulation frequency tuning. Our results indicate significant population-based synchrony in primary auditory cortex and suggest that robust population coding of the sound envelope information present in animal vocalizations and speech can be reliably achieved even with indiscriminate pooling of cortical responses. These findings highlight the importance of firing rate dynamics in population-based sensory coding.

How Precise is Neuronal Synchronization?

1995 ◽  
Vol 7 (3) ◽  
pp. 469-485 ◽  
Author(s):  
Peter König ◽  
Andreas K. Engel ◽  
Pieter R. Roelfsema ◽  
Wolf Singer
Keyword(s):  
Cortical Neurons ◽  
Visual Stimuli ◽  
Network Models ◽  
Movement Direction ◽  
Phase Lag ◽  
Graded Responses ◽  
Spatially Distributed ◽  
The Difference ◽  
Coarse Coding ◽  
Zero Phase

Recent work suggests that synchronization of neuronal activity could serve to define functionally relevant relationships between spatially distributed cortical neurons. At present, it is not known to what extent this hypothesis is compatible with the widely supported notion of coarse coding, which assumes that features of a stimulus are represented by the graded responses of a population of optimally and suboptimally activated cells. To resolve this issue we investigated the temporal relationship between responses of optimally and suboptimally stimulated neurons in area 17 of cat visual cortex. We find that optimally and suboptimally activated cells can synchronize their responses with a precision of a few milliseconds. However, there are consistent and systematic deviations of the phase relations from zero phase lag. Systematic variation of the orientation of visual stimuli shows that optimally driven neurons tend to lead over suboptimally activated cells. The observed phase lag depends linearly on the stimulus orientation and is, in addition, proportional to the difference between the preferred orientations of the recorded cells. Similar effects occur when testing the influence of the movement direction and the spatial frequency of visual stimuli. These results suggest that binding by synchrony can be used to define assemblies of neurons representing a coarse-coded stimulus. Furthermore, they allow a quantitative test of neuronal network models designed to reproduce physiological results on stimulus-specific synchronization.

Receptive-field properties of neurons in binocular and monocular segments of striate cortex in cats raised with binocular lid suture

1978 ◽  
Vol 41 (2) ◽  
pp. 322-337 ◽  
Author(s):  
D. W. Watkins ◽  
J. R. Wilson ◽  
S. M. Sherman
Keyword(s):  
Receptive Field ◽  
Cortical Neurons ◽  
Striate Cortex ◽  
Ocular Dominance ◽  
Visual Stimuli ◽  
Receptive Fields ◽  
Total Sample ◽  
Orientation Selectivity ◽  
Peak Response ◽  
Response Properties

1. We studied the receptive fields of 171 striate cortical neurons from 17 cats raised with binocular lid suture. Of these, 102 fields were within 10 degrees of the area centralis and the remaining 69 were at least 38 degrees from the vertical meridian. 2. Based on their different response properties, cells were divided into three broad groups: the mappable cells (49%) had clearly defined receptive fields, the unmappable cells (31%) were activated by visual stimuli but had diffuse fields which could not be hand plotted, and the visually inexcitable cells (20%) could not be activated by visual stimuli. Very few (less than or equal to 12% of the total sample) normal simple or complex cells could be found. 3. Orientation selectivity was assessed in these cells. Only 12% displayed orientation selectivity within normal bounds, and these were all mappable cells. None of the unmappable cells had discernible orientation selectivity. 4. Ocular dominance was assessed for 62 of the centrally located receptive fields. Among mappable cells, there was an abnormally low proportion of binocular fields, while no such abnormality was seen for unmappable cells. 5. For 47 of the neurons, average response histograms were compiled for moving stimuli of various parameters in an effort to evoke the maximum discharge or peak response. This peak response was normal for mappable cells but reduced for unmappable cells. 6. We devised a technique for studying potential inhibitory receptive-field zones in these neurons, validated the method in normal striate cortex, and used it to test 20 mappable cells in the lid-sutured cats. None showed the pattern of strong inhibitory side bands seen in normal simple cells, although six showed weak or abnormal inhibitory zones. Interestingly, six of the seven visually inexcitable cells tested by this method had purely inhibitory receptive fields. 7. The effects of binocular suture were essentially identical for the binocular and monocular segments since the cell types and their response properties did not differ between these two areas of cortex. Furthermore, the cortical monocular segments of these cats seemed qualitatively different from the deprived cortical monocular segment after monocular suture. This extends an analogous difference for these cats reported for the monocular segments of the lateral geniculate nucleus. We thus conclude that monocularly and binocularly sutured cats develop by qualitatively different mechanisms. For the former, competition between central synapses related to each eye is a prominent feature of geniculocortical development, whereas, for the latter, such specific forms of geniculocortical development may not obtain.

Optimizing a Linear Algorithm for Real-Time Robotic Control using Chronic Cortical Ensemble Recordings in Monkeys

2004 ◽  
Vol 16 (6) ◽  
pp. 1022-1035 ◽  
Author(s):  
Johan Wessberg ◽  
Miguel A. L. Nicolelis
Keyword(s):  
Real Time ◽  
Linear Model ◽  
Impulse Response ◽  
Cortical Neurons ◽  
Population Coding ◽  
Neural Population ◽  
Hand Position ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Optimal Parameters

Previous work in our laboratory has demonstrated that a simple linear model can be used to translate cortical neuronal activity into real-time motor control commands that allow a robot arm to mimic the intended hand movements of trained primates. Here, we describe the results of a comprehensive analysis of the contribution of single cortical neurons to this linear model. Key to the operation of this model was the observation that a large percentage of cortical neurons located in both frontal and parietal cortical areas are tuned for hand position. In most neurons, hand position tuning was time-dependent, varying continuously during a 1-sec period before hand movement onset. The relevance of this physiological finding was demonstrated by showing that maximum contribution of individual neurons to the linear model was only achieved when optimal parameters for the impulse response functions describing time-varying neuronal position tuning were selected. Optimal parameters included impulse response functions with 1.0-to 1.4-sec time length and 50-to 100-msec bins. Although reliable generalization and long-term predictions (60–90 min) could be achieved after 10-min training sessions, we noticed that the model performance degraded over long periods. Part of this degradation was accounted by the observation that neuronal position tuning varied significantly throughout the duration (60–90 min) of a recording session. Altogether, these results indicate that the experimental paradigm described here may be useful not only to investigate aspects of neural population coding, but it may also provide a test bed for the development of clinically useful cortical prosthetic devices aimed at restoring motor functions in severely paralyzed patients.

scholarly journals Ultrafast population coding and axo-somatic compartmentalization

2021 ◽  
Author(s):  
Chenfei Zhang ◽  
David Hofmann ◽  
Andreas Neef ◽  
Fred Wolf
Keyword(s):  
Ion Channel ◽  
High Frequency ◽  
Cortical Neurons ◽  
Molecular Design ◽  
Population Coding ◽  
Voltage Sensitivity ◽  
Common Input ◽  
Ion Channel Properties ◽  
Soma Size ◽  
Channel Properties

Populations of cortical neurons respond to common input within a millisecond. Morphological features and active ion channel properties were suggested to contribute to this astonishing processing speed. Here we report an exhaustive study of ultrafast population coding for varying axon initial segment (AIS) location, soma size, and axonal current properties. In particular, we studied their impact on two experimentally observed features 1) precise action potential timing, manifested in a wide-bandwidth dynamic gain, and 2) high-frequency boost under slowly fluctuating correlated input. While the density of axonal channels and their distance from the soma had a very small impact on bandwidth, it could be moderately improved by increasing soma size. When the voltage sensitivity of axonal currents was increased we observed ultrafast coding and high-frequency boost. We conclude that these computationally relevant features are strongly dependent on axonal ion channels' voltage sensitivity, but not their number or exact location. We point out that ion channel properties, unlike dendrite size, can undergo rapid physiological modification, suggesting that the temporal accuracy of neuronal population encoding could be dynamically regulated. Our results are in line with recent experimental findings in AIS pathologies and establish a framework to study structure-function relations in AIS molecular design.

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