Transient Cortical Excitation at the Onset of Visual Fixation

Cognitive Neuroscience and Schizophrenia Program, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA.
Cerebral Cortex (Impact Factor: 8.67). 02/2008; 18(1):200-9. DOI: 10.1093/cercor/bhm046
Source: PubMed


Primates actively examine the visual world by rapidly shifting gaze (fixation) over the elements in a scene. Despite this fact, we typically study vision by presenting stimuli with gaze held constant. To better understand the dynamics of natural vision, we examined how the onset of visual fixation affects ongoing neuronal activity in the absence of visual stimulation. We used multiunit activity and current source density measurements to index neuronal firing patterns and underlying synaptic processes in macaque V1. Initial averaging of neural activity synchronized to the onset of fixation suggested that a brief period of cortical excitation follows each fixation. Subsequent single-trial analyses revealed that 1) neuronal oscillation phase transits from random to a highly organized state just after the fixation onset, 2) this phase concentration is accompanied by increased spectral power in several frequency bands, and 3) visual response amplitude is enhanced at the specific oscillatory phase associated with fixation. We hypothesize that nonvisual inputs are used by the brain to increase cortical excitability at fixation onset, thus "priming" the system for new visual inputs generated at fixation. Despite remaining mechanistic questions, it appears that analysis of fixation-related responses may be useful in studying natural vision.

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Available from: Peter Lakatos, May 23, 2014
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    • "The anatomical origins of the motor-related phase-resetting cues are uncertain, but plausible candidates are efference copies from the oculomotor system (pontine reticular formation and/or extraocular muscles, see Ito et al., 2011) or a corollary discharge route through the superior colliculus (SC), thalamus, and frontal eye fields (FEFs) (see Melloni et al., 2009). It is also possible that saccades and the corollary activity are both generated in parallel by attention (Melloni et al., 2009; Rajkai et al., 2008). (B) Selective attention orchestrates phase resetting of oscillations in auditory and visual cortices (e.g., Lakatos et al., 2008). "
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    ABSTRACT: Research into the anatomical substrates and "principles" for integrating inputs from separate sensory surfaces has yielded divergent findings. This suggests that multisensory integration is flexible and context dependent and underlines the need for dynamically adaptive neuronal integration mechanisms. We propose that flexible multisensory integration can be explained by a combination of canonical, population-level integrative operations, such as oscillatory phase resetting and divisive normalization. These canonical operations subsume multisensory integration into a fundamental set of principles as to how the brain integrates all sorts of information, and they are being used proactively and adaptively. We illustrate this proposition by unifying recent findings from different research themes such as timing, behavioral goal, and experience-related differences in integration.
    Neuron 03/2014; 81(6):1240-1253. DOI:10.1016/j.neuron.2014.02.044 · 15.05 Impact Factor
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    • "This synchronization in the local field potential signal is thought to be of non-retinal origin since it is observed in visual areas, even in the dark, during voluntary eye-movements. Similar processes could apply also to micro-fixation movements (Maldonado et al., 2008; Rajkai et al., 2008; Bosman et al., 2009). Recent work (Martinez-Conde et al., 2012) indicates that real micro-saccadic eye-movements during visual fixation trigger a phasic increase in firing rate, followed by a rebound suppression which is absent when simulating virtual saccades (reproduced—as in the present study—by manipulating the retinal flow alone). "
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    ABSTRACT: Synaptic Noise is thought to be a limiting factor for computational efficiency in the Brain. In visual cortex (V1), ongoing activity is present in vivo, and spiking responses to simple stimuli are highly unreliable across trials. Stimulus statistics used to plot receptive fields, however, are quite different from those experienced during natural visuomotor exploration. We recorded V1 neurons intracellularly in the anaesthetized and paralyzed cat and compared their spiking and synaptic responses to full field natural images animated by simulated eye-movements to those evoked by simpler (grating) or higher dimensionality statistics (dense noise). In most cells, natural scene animation was the only condition where high temporal precision (in the 10-20 ms range) was maintained during sparse and reliable activity. At the subthreshold level, irregular but highly reproducible membrane potential dynamics were observed, even during long (several 100 ms) “spike-less” periods. We showed that both the spatial structure of natural scenes and the temporal dynamics of eye-movements increase the signal-to-noise ratio by a non linear amplification of the signal combined with a reduction of the subthreshold contextual noise. These data support the view that the sparsening and the time precision of the neural code in V1 may depend primarily on three factors: 1) broadband input spectrum: the bandwidth must be rich enough for recruiting optimally the diversity of spatial and time constants during recurrent processing; 2) tight temporal interplay of excitation and inhibition: conductance measurements demonstrate that natural scene statistics narrow selectively the duration of the spiking opportunity window during which the balance between excitation and inhibition changes transiently and reversibly; 3) signal energy in the lower frequency band: a minimal level of power is needed below 10 Hz to reach consistently the spiking threshold, a situation rarely reached with visual dense noise.
    Frontiers in Neural Circuits 12/2013; 7:206. DOI:10.3389/fncir.2013.00206 · 3.60 Impact Factor
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    • "Given the existence of a strong P1 response after fixation and that the modulation of its peak amplitude is given by the amplitude of the preceding saccade; our results suggest that for fERPs the P1 is generated by a perceptual component related to the processing of a stimulus at fixation, and by a saccade component or lambda potential generated by brain activity associated with eye movements (Thickbroom et al., 1991; Yagi, 1981). This is consistent with previous neurophysiological reports in humans and monkeys that show the existence of saccadic related responses locked to the onset of eye movements (Fourment et al., 1976; Rajkai et al., 2008; Skrandies and Laschke, 1997; but see also Ossandón et al., 2010, for an experiment in which clear visual components were absent when subjects performed saccades on a homogeneously gray screen). For comparison, we implemented a simple fixed-gaze paradigm using faces taken from the visual scenes. "
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    ABSTRACT: Despite the compelling contribution of the study of event related potentials (ERPs) and eye movements to cognitive neuroscience, these two approaches have largely evolved independently. We designed an eye-movement visual search paradigm that allowed us to concurrently record EEG and eye movements while subjects were asked to find a hidden target face in a crowded scene with distractor faces. Fixation event-related potentials (fERPs) to target and distractor stimuli showed the emergence of robust sensory components associated with the perception of stimuli and cognitive components associated with the detection of target faces. We compared those components with the ones obtained in a control task at fixation: qualitative similarities as well as differences in terms of scalp topography and latency emerged between the two. By using single trial analyses, fixations to target and distractors could be decoded from the EEG signals above chance level in 11 out of 12 subjects. Our results show that EEG signatures related to cognitive behavior develop across spatially unconstrained exploration of natural scenes and provide a first step towards understanding the mechanisms of target detection during natural search.
    NeuroImage 12/2013; 89. DOI:10.1016/j.neuroimage.2013.12.006 · 6.36 Impact Factor
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