scholarly journals Correction: Error-Robust Modes of the Retinal Population Code

2017 ◽  
Vol 13 (11) ◽  
pp. e1005855
Author(s):  
Neuron ◽  
2013 ◽  
Vol 78 (2) ◽  
pp. 209-210 ◽  
Author(s):  
Frédéric E. Theunissen ◽  
Julie E. Elie
Keyword(s):  

1977 ◽  
Vol 87 (2) ◽  
pp. 253
Author(s):  
VINCENT M. RICCARDI
Keyword(s):  

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Rava Azeredo da Silveira ◽  
Fred Rieke

Neurons in the brain represent information in their collective activity. The fidelity of this neural population code depends on whether and how variability in the response of one neuron is shared with other neurons. Two decades of studies have investigated the influence of these noise correlations on the properties of neural coding. We provide an overview of the theoretical developments on the topic. Using simple, qualitative, and general arguments, we discuss, categorize, and relate the various published results. We emphasize the relevance of the fine structure of noise correlation, and we present a new approach to the issue. Throughout this review, we emphasize a geometrical picture of how noise correlations impact the neural code. 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.


Author(s):  
Emmanouil Froudarakis ◽  
Uri Cohen ◽  
Maria Diamantaki ◽  
Edgar Y. Walker ◽  
Jacob Reimer ◽  
...  

AbstractDespite variations in appearance we robustly recognize objects. Neuronal populations responding to objects presented under varying conditions form object manifolds and hierarchically organized visual areas are thought to untangle pixel intensities into linearly decodable object representations. However, the associated changes in the geometry of object manifolds along the cortex remain unknown. Using home cage training we showed that mice are capable of invariant object recognition. We simultaneously recorded the responses of thousands of neurons to measure the information about object identity available across the visual cortex and found that lateral visual areas LM, LI and AL carry more linearly decodable object identity information compared to other visual areas. We applied the theory of linear separability of manifolds, and found that the increase in classification capacity is associated with a decrease in the dimension and radius of the object manifold, identifying features of the population code that enable invariant object coding.


2018 ◽  
Vol 115 (31) ◽  
pp. 8015-8018 ◽  
Author(s):  
Dun Mao ◽  
Adam R. Neumann ◽  
Jianjun Sun ◽  
Vincent Bonin ◽  
Majid H. Mohajerani ◽  
...  

Retrosplenial cortex (RSC) is involved in visuospatial integration and spatial learning, and RSC neurons exhibit discrete, place cell-like sequential activity that resembles the population code of space in hippocampus. To investigate the origins and population dynamics of this activity, we combined longitudinal cellular calcium imaging of dysgranular RSC neurons in mice with excitotoxic hippocampal lesions. We tracked the emergence and stability of RSC spatial activity over consecutive imaging sessions. Overall, spatial activity in RSC was experience-dependent, emerging gradually over time, but, as seen in the hippocampus, the spatial code changed dynamically across days. Bilateral but not unilateral hippocampal lesions impeded the development of spatial activity in RSC. Thus, the emergence of spatial activity in RSC, a major recipient of hippocampal information, depends critically on an intact hippocampus; the indirect connections between the dysgranular RSC and the hippocampus further indicate that hippocampus may exert such influences polysynaptically within neocortex.


2005 ◽  
Vol 15 (6) ◽  
pp. 738-746 ◽  
Author(s):  
Stefan Leutgeb ◽  
Jill K Leutgeb ◽  
May-Britt Moser ◽  
Edvard I Moser

2020 ◽  
Vol 117 (28) ◽  
pp. 16596-16605 ◽  
Author(s):  
Marco Lanzilotto ◽  
Monica Maranesi ◽  
Alessandro Livi ◽  
Carolina Giulia Ferroni ◽  
Guy A. Orban ◽  
...  

Humans accurately identify observed actions despite large dynamic changes in their retinal images and a variety of visual presentation formats. A large network of brain regions in primates participates in the processing of others’ actions, with the anterior intraparietal area (AIP) playing a major role in routing information about observed manipulative actions (OMAs) to the other nodes of the network. This study investigated whether the AIP also contributes to invariant coding of OMAs across different visual formats. We recorded AIP neuronal activity from two macaques while they observed videos portraying seven manipulative actions (drag, drop, grasp, push, roll, rotate, squeeze) in four visual formats. Each format resulted from the combination of two actor’s body postures (standing, sitting) and two viewpoints (lateral, frontal). Out of 297 recorded units, 38% were OMA-selective in at least one format. Robust population code for viewpoint and actor’s body posture emerged shortly after stimulus presentation, followed by OMA selectivity. Although we found no fully invariant OMA-selective neuron, we discovered a population code that allowed us to classify action exemplars irrespective of the visual format. This code depends on a multiplicative mixing of signals about OMA identity and visual format, particularly evidenced by a set of units maintaining a relatively stable OMA selectivity across formats despite considerable rescaling of their firing rate depending on the visual specificities of each format. These findings suggest that the AIP integrates format-dependent information and the visual features of others’ actions, leading to a stable readout of observed manipulative action identity.


Sign in / Sign up

Export Citation Format

Share Document