Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity

Attentional selection and communication through coherence: Scope and limitations

Synchronous neural oscillations are strongly associated with a variety of perceptual, cognitive, and behavioural processes. It has been proposed that the role of the synchronous oscillations in these processes is to facilitate information transmission between brain areas, the 'communication through coherence,' or CTC hypothesis. The details of how this mechanism would work, however, and its causal status, are still unclear. Here we investigate computationally a proposed mechanism for selective attention that directly implicates the CTC as causal. The mechanism involves alpha band (about 10 Hz) oscillations, originating in the pulvinar nucleus of the thalamus, being sent to communicating cortical areas, organizing gamma (about 40 Hz) oscillations there, and thus facilitating phase coherence and communication between them. This is proposed to happen contingent on control signals sent from higher-level cortical areas to the thalamic reticular nucleus, which controls the alpha oscillations sent to cortex by the pulvinar. We studied the scope of this mechanism in parameter space, and limitations implied by this scope, using a computational implementation of our conceptual model. Our results indicate that, although the CTC-based mechanism can account for some effects of top-down and bottom-up attentional selection, its limitations indicate that an alternative mechanism, in which oscillatory coherence is caused by communication between brain areas rather than being a causal factor for it, might operate in addition to, or even instead of, the CTC mechanism.

A Computational Model of Direction Selectivity in Macaque V1 Cortex Based on Dynamic Differences between On and Off Pathways

This paper is about neural mechanisms of direction selectivity (DS) in macaque primary visual cortex, V1. We present data (on male macaque) showing strong DS in a majority of simple cells in V1 layer 4Cα, the cortical layer that receives direct afferent input from the magnocellular division of the lateral geniculate nucleus (LGN). Magnocellular LGN cells are not direction-selective. To understand the mechanisms of DS, we built a large-scale, recurrent model of spiking neurons called DSV1. Like its predecessors, DSV1 reproduces many visual response properties of V1 cells including orientation selectivity. Two important new features of DSV1 are (1) DS is initiated by small, consistent dynamic differences in the visual responses of OFF and ON Magnocellular LGN cells, and (2) DS in the responses of most model simple cells is increased over those of their feedforward inputs; this increase is achieved through dynamic interaction of feedforward and intracortical synaptic currents without the use of intracortical direction-specific connections. The DSV1 model emulates experimental data in the following ways: (1) most 4Cα Simple cells were highly direction-selective but 4Cα Complex cells were not; (2) the preferred directions of the model's direction-selective Simple cells were invariant with spatial and temporal frequency (TF); (3) the distribution of the preferred/opposite ratio across the model's population of cells was very close to that found in experiments. The strong quantitative agreement between DS in data and in model simulations suggests that the neural mechanisms of DS in DSV1 may be similar to those in the real visual cortex.SIGNIFICANCE STATEMENT Motion perception is a vital part of our visual experience of the world. In monkeys, whose vision resembles that of humans, the neural computation of the direction of a moving target starts in the primary visual cortex, V1, in layer 4Cα that receives input from the eye through the lateral geniculate nucleus (LGN). How direction selectivity (DS) is generated in layer 4Cα is an outstanding unsolved problem in theoretical neuroscience. In this paper, we offer a solution based on plausible biological mechanisms. We present a new large-scale circuit model in which DS originates from slightly different LGN ON/OFF response time-courses and is enhanced in cortex without the need for direction-specific intracortical connections. The model's DS is in quantitative agreement with experiments.

Altered Temporal Dynamic Intrinsic Brain Activity in Late Blindness

Previous neuroimaging studies demonstrated that visual deprivation triggers significant crossmodal plasticity in the functional and structural architecture of the brain. However, prior neuroimaging studies focused on the static brain activity in blindness. It remains unknown whether alterations of dynamic intrinsic brain activity occur in late blindness (LB). This study investigated dynamic intrinsic brain activity changes in individuals with late blindness by assessing the dynamic amplitude of low-frequency fluctuations (dALFFs) using sliding-window analyses. Forty-one cases of late blindness (LB) (29 males and 12 females, mean age: 39.70 ± 12.66 years) and 48 sighted controls (SCs) (17 males and 31 females, mean age: 43.23 ± 13.40 years) closely matched in age, sex, and education level were enrolled in this study. The dALFF with sliding-window analyses was used to compare the difference in dynamic intrinsic brain activity between the two groups. Compared with SCs, individuals with LB exhibited significantly lower dALFF values in the bilateral lingual gyrus (LING)/calcarine (CAL) and left thalamus (THA). LB cases also showed considerably decreased dFC values between the bilateral LING/CAL and the left middle frontal gyrus (MFG) and between the left THA and the right LING/cerebelum_6 (CER) (two-tailed, voxel-level P < 0.01, Gaussian random field (GRF) correction, cluster-level P < 0.05). Our study demonstrated that LB individuals showed lower-temporal variability of dALFF in the visual cortices and thalamus, suggesting lower flexibility of visual thalamocortical activity, which might reflect impaired visual processing in LB individuals. These findings indicate that abnormal dynamic intrinsic brain activity might be involved in the neurophysiological mechanisms of LB.

Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex

A new computational model of the primary visual cortex (V1) of the macaque monkey was constructed to reconcile the visual functions of V1 with anatomical data on its LGN input, the extreme sparseness of which presented serious challenges to theoretically sound explanations of cortical function. We demonstrate that, even with such sparse input, it is possible to produce robust orientation selectivity, as well as continuity in the orientation map. We went beyond that to find plausible dynamic regimes of our new model that emulate simultaneously experimental data for a wide range of V1 phenomena, beginning with orientation selectivity but also including diversity in neuronal responses, bimodal distributions of the modulation ratio (the simple/complex classification), and dynamic signatures, such as gamma-band oscillations. Intracortical interactions play a major role in all aspects of the visual functions of the model.

SIGNIFICANCE STATEMENT

We present the first realistic model that has captured the sparseness of magnocellular LGN inputs to the macaque primary visual cortex and successfully derived orientation selectivity from them. Three implications are (1) even in input layers to the visual cortex, the system is less feedforward and more dominated by intracortical signals than previously thought, (2) interactions among cortical neurons in local populations produce dynamics not explained by single neurons, and (3) such dynamics are important for function. Our model also shows that a comprehensive picture is necessary to explain function, because different visual properties are related. This study points to the need for paradigm shifts in neuroscience modeling: greater emphasis on population dynamics and, where possible, a move toward data-driven, comprehensive models.

The Influence of Mexican Hat Recurrent Connectivity on Noise Correlations and Stimulus Encoding

Noise correlations are a common feature of neural responses and have been observed in many cortical areas across different species. These correlations can influence information processing by enhancing or diminishing the quality of the neural code, but the origin of these correlations is still a matter of controversy. In this computational study we explore the hypothesis that noise correlations are the result of local recurrent excitatory and inhibitory connections. We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels. Noise correlations decay with distance between neurons but are only observed if the range of excitatory connections is smaller than the range of inhibitory connections (“Mexican hat” connectivity) and if the connection strengths are sufficiently strong. These correlations arise from a moving blob-like structure of evoked activity, which is absent if inhibitory interactions have a smaller range (“inverse Mexican hat” connectivity). Spatially structured external inputs fixate these blobs to certain locations and thus effectively reduce noise correlations. We further investigated the influence of these network configurations on stimulus encoding. On the one hand, the observed correlations diminish information about a stimulus encoded by a network. On the other hand, correlated activity allows for more precise encoding of stimulus information if the decoder has only access to a limited amount of neurons.

Orientation selectivity in cat primary visual cortex: local and global measurement

In this study, we investigated orientation selectivity in cat primary visual cortex (V1) and its relationship with various parameters. We found a strong correlation between circular variance (CV) and orthogonal-topreferred response ratio (O/P ratio), and a moderate correlation between tuning width and O/P ratio. Moreover, the suppression far from the peak that accounted for the lower CV in cat V1 cells also contributed to the narrowing of the tuning width of cells. We also studied the dependence of orientation selectivity on the modulation ratio for each cell, which is consistent with robust entrainment of the neuronal response to the phase of the drifting grating stimulus. In conclusion, the CV (global measure) and tuning width (local measure) are signifi cantly correlated with the modulation ratio.

Whole-field visual motion drives swimming in larval zebrafish via a stochastic process

Caudo-rostral whole-field visual motion elicits forward locomotion in many organisms, including larval zebrafish. Here, we investigate the dependence on the latency to initiate this forward swimming as a function of the speed of the visual motion. We show that latency is highly dependent on speed for slow speeds (<10 mm s(-1)) and then plateaus for higher values. Typical latencies are >1.5 s, which is much longer than neuronal transduction processes. What mechanisms underlie these long latencies? We propose two alternative, biologically inspired models that could account for this latency to initiate swimming: an integrate and fire model, which is history dependent, and a stochastic Poisson model, which has no history dependence. We use these models to predict the behavior of larvae when presented with whole-field motion of varying speed and find that the stochastic process shows better agreement with the experimental data. Finally, we discuss possible neuronal implementations of these models.