scholarly journals Mapping of Visual Receptive Fields by Tomographic Reconstruction

2012 ◽  
Vol 24 (10) ◽  
pp. 2543-2578 ◽  
Author(s):  
Gordon Pipa ◽  
Zhe Chen ◽  
Sergio Neuenschwander ◽  
Bruss Lima ◽  
Emery N. Brown
Keyword(s):  
Point Process ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Tomographic Reconstruction ◽  
Receptive Fields ◽  
Stimulus Presentation ◽  
Orientation Tuning ◽  
V1 Neurons ◽  
Baseline Activity ◽  
Spiking Activity

The moving bar experiment is a classic paradigm for characterizing the receptive field (RF) properties of neurons in primary visual cortex (V1). Current approaches for analyzing neural spiking activity recorded from these experiments do not take into account the point-process nature of these data and the circular geometry of the stimulus presentation. We present a novel analysis approach to mapping V1 receptive fields that combines point-process generalized linear models (PPGLM) with tomographic reconstruction computed by filtered-back projection. We use the method to map the RF sizes and orientations of 251 V1 neurons recorded from two macaque monkeys during a moving bar experiment. Our cross-validated goodness-of-fit analyses show that the PPGLM provides a more accurate characterization of spike train data than analyses based on rate functions computed by the methods of spike-triggered averages or first-order Wiener-Volterra kernel. Our analysis leads to a new definition of RF size as the spatial area over which the spiking activity is significantly greater than baseline activity. Our approach yields larger RF sizes and sharper orientation tuning estimates. The tomographic reconstruction paradigm further suggests an efficient approach to choosing the number of directions and the number of trials per direction in designing moving bar experiments. Our results demonstrate that standard tomographic principles for image reconstruction can be adapted to characterize V1 RFs and that two fundamental properties, size and orientation, may be substantially different from what is currently reported.

Cone Inputs to Simple and Complex Cells in V1 of Awake Macaque

2007 ◽  
Vol 97 (4) ◽  
pp. 3070-3081 ◽  
Author(s):  
Gregory D. Horwitz ◽  
E. J. Chichilnisky ◽  
Thomas D. Albright
Keyword(s):  
Linear Models ◽  
Noise Analysis ◽  
Receptive Fields ◽  
Light Responses ◽  
Simple Cells ◽  
Complex Cells ◽  
V1 Neurons ◽  
Spike Triggered Averaging ◽  
Simple And Complex Cells ◽  
Simple And Complex Cell

Rules by which V1 neurons combine signals originating in the cone photoreceptors are poorly understood. We measured cone inputs to V1 neurons in awake, fixating monkeys with white-noise analysis techniques that reveal properties of light responses not revealed by purely linear models used in previous studies. Simple cells were studied by spike-triggered averaging that is robust to static nonlinearities in spike generation. This analysis revealed, among heterogeneously tuned neurons, two relatively discrete categories: one with opponent L- and M-cone weights and another with nonopponent cone weights. Complex cells were studied by spike-triggered covariance, which identifies features in the stimulus sequence that trigger spikes in neurons with receptive fields containing multiple linear subunits that combine nonlinearly. All complex cells responded to nonopponent stimulus modulations. Although some complex cells responded to cone-opponent stimulus modulations too, none exhibited the pure opponent sensitivity observed in many simple cells. These results extend the findings on distinctions between simple and complex cell chromatic tuning observed in previous studies in anesthetized monkeys.

Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework

2021 ◽  
pp. 1-31
Author(s):  
Yalda Amidi ◽  
Behzad Nazari ◽  
Saeid Sadri ◽  
Ali Yousefi
Keyword(s):  
State Space ◽  
Point Process ◽  
Goodness Of Fit ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Synaptic Connections ◽  
Synaptic Coupling ◽  
Modeling Framework ◽  
Space Modeling ◽  
Spiking Activity

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

scholarly journals Feedforward mechanisms of masking in mouse V1

2021 ◽  
Author(s):  
Dylan Barbera ◽  
Nicholas J. Priebe ◽  
Lindsey L. Glickfeld
Keyword(s):  
Visual Cortex ◽  
Spatial Structure ◽  
Primary Visual Cortex ◽  
Sensory Neurons ◽  
Spatial Configuration ◽  
Receptive Fields ◽  
Cortical Activity ◽  
Afferent Input ◽  
Orientation Tuning ◽  
V1 Neurons

AbstractSensory neurons not only encode stimuli that align with their receptive fields but are also modulated by context. For example, the responses of neurons in mouse primary visual cortex (V1) to gratings of their preferred orientation are modulated by the presence of superimposed orthogonal gratings (“plaids”). The effects of this modulation can be diverse: some neurons exhibit cross-orientation suppression while other neurons have larger responses to a plaid than its components. We investigated whether these diverse forms of masking could be explained by a unified circuit mechanism. We report that the suppression of cortical activity does not alter the effects of masking, ruling out cortical mechanisms. Instead, we demonstrate that the heterogeneity of plaid responses is explained by an interaction between stimulus geometry and orientation tuning. Highly selective neurons uniformly exhibit cross-orientation suppression, whereas in weakly-selective neurons masking depends on the spatial configuration of the stimulus, with effects transitioning systematically between suppression and facilitation. Thus, the diverse responses of mouse V1 neurons emerge as a consequence of the spatial structure of the afferent input to V1, with no need to invoke cortical interactions.

scholarly journals Anticipatory responses along motion trajectories in awake monkey area V1

2020 ◽  
Author(s):  
Giacomo Benvenuti ◽  
Sandrine Chemla ◽  
Arjan Boonman ◽  
Laurent Perrinet ◽  
Guillaume S Masson ◽  
...  
Keyword(s):  
Visual Motion ◽  
Receptive Fields ◽  
Contextual Effects ◽  
List Type ◽  
Motion Trajectories ◽  
V1 Neurons ◽  
Motion Integration ◽  
Feedback Networks ◽  
Lateral Propagation ◽  
Spiking Activity

ABSTRACTWhat are the neural mechanisms underlying motion integration of translating objects? Visual motion integration is generally conceived of as a feedforward, hierarchical, information processing. However, feedforward models fail to account for many contextual effects revealed using natural moving stimuli. In particular, a translating object evokes a sequence of transient feedforward responses in the primary visual cortex but also propagations of activity through horizontal and feedback pathways. We investigated how these pathways shape the representation of a translating bar in monkey V1. We show that, for long trajectories, spiking activity builds-up hundreds of milliseconds before the bar enters the neurons’ receptive fields. Using VSDI and LFP recordings guided by a phenomenological model of propagation dynamics, we demonstrate that this anticipatory response arises from the interplay between horizontal and feedback networks driving V1 neurons well ahead of their feedforward inputs. This mechanism could subtend several perceptual contextual effects observed with translating objects.HighlightsOur hypothesis is that lateral propagation of activity in V1 contributes to the integration of translating stimuliConsistent with this hypothesis, we find that a translating bar induces anticipatory spiking activity in V1 neurons.A V1 model describes how this anticipation can arise from inter and intra-cortical lateral propagation of activity.The dynamic of VSDi and LFP signals in V1 is consistent with the predictions made by the model.The intra-cortical origin is further confirmed by the fact that a bar moving from the ipsilateral hemifield does not evoke anticipation.Horizontal and feedback input are not only modulatory but can also drive spiking responses in specific contexts.

A point-process model of human heartbeat intervals: new definitions of heart rate and heart rate variability

2005 ◽  
Vol 288 (1) ◽  
pp. H424-H435 ◽  
Author(s):  
Riccardo Barbieri ◽  
Eric C. Matten ◽  
AbdulRasheed A. Alabi ◽  
Emery N. Brown
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Point Process ◽  
Process Model ◽  
Goodness Of Fit ◽  
Gaussian Model ◽  
Quantitative Measure ◽  
Local Maximum ◽  
Inverse Gaussian ◽  
Design Of Algorithms

Heart rate is a vital sign, whereas heart rate variability is an important quantitative measure of cardiovascular regulation by the autonomic nervous system. Although the design of algorithms to compute heart rate and assess heart rate variability is an active area of research, none of the approaches considers the natural point-process structure of human heartbeats, and none gives instantaneous estimates of heart rate variability. We model the stochastic structure of heartbeat intervals as a history-dependent inverse Gaussian process and derive from it an explicit probability density that gives new definitions of heart rate and heart rate variability: instantaneous R-R interval and heart rate standard deviations. We estimate the time-varying parameters of the inverse Gaussian model by local maximum likelihood and assess model goodness-of-fit by Kolmogorov-Smirnov tests based on the time-rescaling theorem. We illustrate our new definitions in an analysis of human heartbeat intervals from 10 healthy subjects undergoing a tilt-table experiment. Although several studies have identified deterministic, nonlinear dynamical features in human heartbeat intervals, our analysis shows that a highly accurate description of these series at rest and in extreme physiological conditions may be given by an elementary, physiologically based, stochastic model.

scholarly journals Neural Decoding Using a Parallel Sequential Monte Carlo Method on Point Processes with Ensemble Effect

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Xu ◽  
Yiwen Wang ◽  
Fang Wang ◽  
Yuxi Liao ◽  
Qiaosheng Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Point Process ◽  
Goodness Of Fit ◽  
Point Processes ◽  
Sequential Monte Carlo ◽  
Neuronal Response ◽  
Position Estimation ◽  
Monte Carlo Algorithm ◽  
Sequential Monte Carlo Method ◽  
Proposed Model

Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.

scholarly journals Emergent orientation selectivity from random networks in mouse visual cortex

2018 ◽  
Author(s):  
J.J. Pattadkal ◽  
G. Mato ◽  
C. van Vreeswijk ◽  
N. J. Priebe ◽  
D. Hansel
Keyword(s):  
Visual Cortex ◽  
Calcium Imaging ◽  
Receptive Fields ◽  
Orientation Selectivity ◽  
Random Networks ◽  
Two Dimensional ◽  
V1 Neurons ◽  
Mouse Visual Cortex ◽  
Whole Cell Recordings ◽  
Random Connectivity

SummaryWe study the connectivity principles underlying the emergence of orientation selectivity in primary visual cortex (V1) of mammals lacking an orientation map. We present a computational model in which random connectivity gives rise to orientation selectivity that matches experimental observations. It predicts that mouse V1 neurons should exhibit intricate receptive fields in the two-dimensional frequency domain, causing shift in orientation preferences with spatial frequency. We find evidence for these features in mouse V1 using calcium imaging and intracellular whole cell recordings.

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