scholarly journals Synthesis of higher order feature codes through stimulus-specific supra-linear summation

2020 ◽  
Author(s):  
Evan H. Lyall ◽  
Daniel P. Mossing ◽  
Scott R. Pluta ◽  
Amir Dudai ◽  
Hillel Adesnik
Keyword(s):  
Cortical Neurons ◽  
Object Identification ◽  
Higher Order ◽  
Population Coding ◽  
Complex Objects ◽  
Linear Summation ◽  
Specific Stimulus ◽  
Sensory Stimuli ◽  
Stimulus Features ◽  
Complex Features

AbstractHow cortical circuits build representations of complex objects is poorly understood. The massive dimensional expansion from the thalamus to the primary sensory cortex may enable sparse, comprehensive representations of higher order features to facilitate object identification. To generate such a code, cortical neurons must integrate broadly over space, yet simultaneously obtain sharp tuning to specific stimulus features. The logic of cortical integration that may synthesize such a sparse, high dimensional code for complex features 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 found that somatosensory and visual cortical neurons sum highly specific combinations of sensory inputs supra-linearly, but integrate other inputs sub-linearly, leading to selective responses to higher order features. This integrative process generates a sparse, but comprehensive code for complex stimuli from the earliest stages of cortical processing. These results from multiple sensory modalities imply that input-specific supra-linear summation may represent a widespread cortical mechanism for the synthesis of higher order feature codes. This new mechanism may explain how the brain exploits the thalamocortical expansion of dimensionality to encode arbitrary complex features of sensory stimuli.

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.

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 The Role of V1 Surround Suppression in MT Motion Integration

2010 ◽  
Vol 103 (6) ◽  
pp. 3123-3138 ◽  
Author(s):  
James M. G. Tsui ◽  
J. Nicholas Hunter ◽  
Richard T. Born ◽  
Christopher C. Pack
Keyword(s):  
Cortical Neurons ◽  
Temporal Dynamics ◽  
Macaque Monkey ◽  
Direction Selectivity ◽  
Extrastriate Cortex ◽  
Complex Stimulus ◽  
Specific Stimulus ◽  
Tuning Curves ◽  
Mt Neurons ◽  
Stimulus Features

Neurons in the primate extrastriate cortex are highly selective for complex stimulus features such as faces, objects, and motion patterns. One explanation for this selectivity is that neurons in these areas carry out sophisticated computations on the outputs of lower-level areas such as primary visual cortex (V1), where neuronal selectivity is often modeled in terms of linear spatiotemporal filters. However, it has long been known that such simple V1 models are incomplete because they fail to capture important nonlinearities that can substantially alter neuronal selectivity for specific stimulus features. Thus a key step in understanding the function of higher cortical areas is the development of realistic models of their V1 inputs. We have addressed this issue by constructing a computational model of the V1 neurons that provide the strongest input to extrastriate cortical middle temporal (MT) area. We find that a modest elaboration to the standard model of V1 direction selectivity generates model neurons with strong end-stopping, a property that is also found in the V1 layers that provide input to MT. With this computational feature in place, the seemingly complex properties of MT neurons can be simulated by assuming that they perform a simple nonlinear summation of their inputs. The resulting model, which has a very small number of free parameters, can simulate many of the diverse properties of MT neurons. In particular, we simulate the invariance of MT tuning curves to the orientation and length of tilted bar stimuli, as well as the accompanying temporal dynamics. We also show how this property relates to the continuum from component to pattern selectivity observed when MT neurons are tested with plaids. Finally, we confirm several key predictions of the model by recording from MT neurons in the alert macaque monkey. Overall our results demonstrate that many of the seemingly complex computations carried out by high-level cortical neurons can in principle be understood by examining the properties of their inputs.

scholarly journals Information diversity in individual auditory cortical neurons is associated with functionally distinct coordinated neuronal ensembles

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jermyn Z. See ◽  
Natsumi Y. Homma ◽  
Craig A. Atencio ◽  
Vikaas S. Sohal ◽  
Christoph E. Schreiner
Keyword(s):  
Cortical Neurons ◽  
Single Neuron ◽  
Receptive Fields ◽  
Acoustic Feature ◽  
Neuronal Ensembles ◽  
Neuron Spike ◽  
Information Diversity ◽  
Feature Selectivity ◽  
Stimulus Features ◽  
Time Specific

AbstractNeuronal activity in auditory cortex is often highly synchronous between neighboring neurons. Such coordinated activity is thought to be crucial for information processing. We determined the functional properties of coordinated neuronal ensembles (cNEs) within primary auditory cortical (AI) columns relative to the contributing neurons. Nearly half of AI cNEs showed robust spectro-temporal receptive fields whereas the remaining cNEs showed little or no acoustic feature selectivity. cNEs can therefore capture either specific, time-locked information of spectro-temporal stimulus features or reflect stimulus-unspecific, less-time specific processing aspects. By contrast, we show that individual neurons can represent both of those aspects through membership in multiple cNEs with either high or absent feature selectivity. These associations produce functionally heterogeneous spikes identifiable by instantaneous association with different cNEs. This demonstrates that single neuron spike trains can sequentially convey multiple aspects that contribute to cortical processing, including stimulus-specific and unspecific information.

scholarly journals Perinatal fentanyl exposure leads to long-lasting impairments in somatosensory circuit function and behavior

2020 ◽  
Author(s):  
Jason Alipio ◽  
Catherine Haga ◽  
Megan E Fox ◽  
Keiko Arakawa ◽  
Rakshita Balaji ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Morphological Changes ◽  
Anterior Cingulate ◽  
Preclinical Model ◽  
Sensory Stimuli ◽  
Synaptic Excitation ◽  
Molecular Abnormalities ◽  
Postsynaptic Currents ◽  
And Behavior

One consequence of the opioid epidemic are lasting neurodevelopmental sequelae afflicting adolescents exposed to opioids in the womb. A translationally relevant and developmentally accurate preclinical model is needed to understand the behavioral, circuit, network, and molecular abnormalities resulting from this exposure. By employing a novel preclinical model of perinatal fentanyl exposure, our data reveal that fentanyl has several dose-dependent, developmental consequences to somatosensory function and behavior. Newborn male and female mice exhibit signs of withdrawal and sensory-related deficits that extend at least to adolescence. As fentanyl exposure does not affect dams' health or maternal behavior, these effects result from the direct actions of perinatal fentanyl on the pups' developing brain. At adolescence, exposed mice exhibit reduced adaptation to sensory stimuli, and a corresponding impairment in primary somatosensory (S1) function. In vitro electrophysiology demonstrates a long-lasting reduction in S1 synaptic excitation, evidenced by decreases in release probability, NMDA receptor-mediated postsynaptic currents, and frequency of miniature excitatory postsynaptic currents, as well as increased frequency of miniature inhibitory postsynaptic currents. In contrast, anterior cingulate cortical neurons exhibit an opposite phenotype, with increased synaptic excitation. Consistent with these changes, electrocorticograms reveal suppressed ketamine-evoked γ oscillations. Morphological analysis of S1 pyramidal neurons indicate reduced dendritic complexity, dendritic length, and soma size. Further, exposed mice exhibited abnormal cortical mRNA expression of key receptors and neuronal growth and development, changes that were consistent with the electrophysiological and morphological changes. These findings demonstrate the lasting sequelae of perinatal fentanyl exposure on sensory processing and function.

scholarly journals Neural evidence supports a dual sensory-motor role for insect wings

2017 ◽  
Vol 284 (1862) ◽  
pp. 20170969 ◽  
Author(s):  
Brandon Pratt ◽  
Tanvi Deora ◽  
Thomas Mohren ◽  
Thomas Daniel
Keyword(s):  
Flight Control ◽  
Information Acquisition ◽  
Receptive Fields ◽  
Mechanical Stimulus ◽  
Sensory Function ◽  
Computational Techniques ◽  
Campaniform Sensilla ◽  
Specific Stimulus ◽  
Processing Times ◽  
Stimulus Features

Flying insects use feedback from various sensory modalities including vision and mechanosensation to navigate through their environment. The rapid speed of mechanosensory information acquisition and processing compensates for the slower processing times associated with vision, particularly under low light conditions. While halteres in dipteran species are well known to provide such information for flight control, less is understood about the mechanosensory roles of their evolutionary antecedent, wings. The features that wing mechanosensory neurons (campaniform sensilla) encode remains relatively unexplored. We hypothesized that the wing campaniform sensilla of the hawkmoth, Manduca sexta, rapidly and selectively extract mechanical stimulus features in a manner similar to halteres. We used electrophysiological and computational techniques to characterize the encoding properties of wing campaniform sensilla. To accomplish this, we developed a novel technique for localizing receptive fields using a focused IR laser that elicits changes in the neural activity of mechanoreceptors. We found that (i) most wing mechanosensors encoded mechanical stimulus features rapidly and precisely, (ii) they are selective for specific stimulus features, and (iii) there is diversity in the encoding properties of wing campaniform sensilla. We found that the encoding properties of wing campaniform sensilla are similar to those for haltere neurons. Therefore, it appears that the neural architecture that underlies the haltere sensory function is present in wings, which lends credence to the notion that wings themselves may serve a similar sensory function. Thus, wings may not only function as the primary actuator of the organism but also as sensors of the inertial dynamics of the animal.

Dense Circuit Reconstruction to Understand Neuronal Computation: Focus on Zebrafish

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rainer W. Friedrich ◽  
Adrian A. Wanner
Keyword(s):  
Neuronal Population ◽  
Higher Order ◽  
Order Structure ◽  
Annual Review ◽  
Publication Date ◽  
Population Activity ◽  
Stimulus Features ◽  
Processing And Storage ◽  
Low Dimensional ◽  
Neuronal Computation

The dense reconstruction of neuronal wiring diagrams from volumetric electron microscopy data has the potential to generate fundamentally new insights into mechanisms of information processing and storage in neuronal circuits. Zebrafish provide unique opportunities for dynamical connectomics approaches that combine reconstructions of wiring diagrams with measurements of neuronal population activity and behavior. Such approaches have the power to reveal higher-order structure in wiring diagrams that cannot be detected by sparse sampling of connectivity and that is essential for neuronal computations. In the brain stem, recurrently connected neuronal modules were identified that can account for slow, low-dimensional dynamics in an integrator circuit. In the spinal cord, connectivity specifies functional differences between premotor interneurons. In the olfactory bulb, tuning-dependent connectivity implements a whitening transformation that is based on the selective suppression of responses to overrepresented stimulus features. These findings illustrate the potential of dynamical connectomics in zebrafish to analyze the circuit mechanisms underlying higher-order neuronal computations. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Left-Hemispheric Asymmetry for Object-Based Attention: an ERP Study

2019 ◽  
Vol 9 (11) ◽  
pp. 315 ◽  
Author(s):  
Andrea Orlandi ◽  
Alice Mado Proverbio
Keyword(s):  
Visual Attention ◽  
Hemispheric Asymmetry ◽  
Time Course ◽  
Electrophysiological Study ◽  
Target Selection ◽  
Neural Correlates ◽  
Specific Stimulus ◽  
Object Categories ◽  
Object Based ◽  
Stimulus Features

It has been shown that selective attention enhances the activity in visual regions associated with stimulus processing. The left hemisphere seems to have a prominent role when non-spatial attention is directed towards specific stimulus features (e.g., color, spatial frequency). The present electrophysiological study investigated the time course and neural correlates of object-based attention, under the assumption of left-hemispheric asymmetry. Twenty-nine right-handed participants were presented with 3D graphic images representing the shapes of different object categories (wooden dummies, chairs, structures of cubes) which lacked detail. They were instructed to press a button in response to a target stimulus indicated at the beginning of each run. The perception of non-target stimuli elicited a larger anterior N2 component, which was likely associated with motor inhibition. Conversely, target selection resulted in an enhanced selection negativity (SN) response lateralized over the left occipito-temporal regions, followed by a larger centro-parietal P300 response. These potentials were interpreted as indexing attentional selection and categorization processes, respectively. The standardized weighted low-resolution electromagnetic tomography (swLORETA) source reconstruction showed the engagement of a fronto-temporo-limbic network underlying object-based visual attention. Overall, the SN scalp distribution and relative neural generators hinted at a left-hemispheric advantage for non-spatial object-based visual attention.

scholarly journals Serotonergic modulation across sensory modalities

2020 ◽  
Vol 123 (6) ◽  
pp. 2406-2425
Author(s):  
Tyler R. Sizemore ◽  
Laura M. Hurley ◽  
Andrew M. Dacks
Keyword(s):  
Serotonin Receptor ◽  
Physiological State ◽  
Sensory Information ◽  
Specific Stimulus ◽  
Sensory Organization ◽  
Sensory Modalities ◽  
Multiple Levels ◽  
Stimulus Features ◽  
Ubiquitous Presence ◽  
Serotonergic Modulation

The serotonergic system has been widely studied across animal taxa and different functional networks. This modulatory system is therefore well positioned to compare the consequences of neuromodulation for sensory processing across species and modalities at multiple levels of sensory organization. Serotonergic neurons that innervate sensory networks often bidirectionally exchange information with these networks but also receive input representative of motor events or motivational state. This convergence of information supports serotonin’s capacity for contextualizing sensory information according to the animal’s physiological state and external events. At the level of sensory circuitry, serotonin can have variable effects due to differential projections across specific sensory subregions, as well as differential serotonin receptor type expression within those subregions. Functionally, this infrastructure may gate or filter sensory inputs to emphasize specific stimulus features or select among different streams of information. The near-ubiquitous presence of serotonin and other neuromodulators within sensory regions, coupled with their strong effects on stimulus representation, suggests that these signaling pathways should be considered integral components of sensory systems.

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