Neural mechanisms of speed perception: transparent motion

2016

Cortex

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We investigated how neural activity in the middle temporal area of the macaque monkey changes after three seconds of exposure to a visual stimulus and used this to gain insight into the assumptions underlying the fMRI adaptation method (fMRIa). We studied both changes in tuning curves following weak and strong motion stimuli (adaptation) and the differences between a first and second exposure to the same stimulus (repetition suppression). Typically, tuning curves had smaller amplitudes and narrower tuning widths after strong adaptation; this was true for single neurons, multi-unit activity, the evoked local field potential (LFP), as well as gamma band activity. Repetition typically led to reduced responses. This reduction was correlated with direction selectivity and not explained by neural fatigue. Our data, however, warn against a simplistic view of the consequences of adaptation. First, a considerable fraction of neurons and sites showed response enhancements after adaptation, especially when probed with a stimulus that moved opposite to the direction of the adapting stimulus. Second, adaptation was stimulus selective only on a time scale of ~100 ms. Third, aggregate measures of neural activity (multi-unit activity, local field potentials) had substantially different adaptation effects. Fourth, there were qualitative differences between our findings in MT and earlier findings in IT cortex. We conclude that selective adaptation effects in fMRIa are relatively easy to miss even when they exist (for instance by presenting stimuli for too long, or because neurons that enhance after adaptation cancel out the effect of neurons that suppress). Moreover, we argue that adaptation should be understood in the context of the computations that a neural circuit perform. Using fMRIa as a tool to uncover neural selectivity requires a better understanding of this circuitry and its consequences for adaptation.

Section: Discussionsupporting

confidence: 63%

Testing the assumptions underlying fMRI adaptation using intracortical recordings in area MT

Kar

2016

Cortex

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“…We used the velocity tuning curves of 26 MT neurons recorded in two awake macaque monkeys. These recordings were subsets of the data in previously published studies focusing on the influence of contrast Krekelberg Contrast paper 2006, adaptation (Krekelberg et al, 2006 ), and transparency (Krekelberg and van Wezel, 2013 ) on speed tuning in MT. The selection criteria for inclusion in this modeling study are described in the Section Materials and Methods.…”

Section: Resultsmentioning

confidence: 99%

Motion detection based on recurrent network dynamics

Joukes

Hartmann

2014

Front. Syst. Neurosci.

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The detection of visual motion requires temporal delays to compare current with earlier visual input. Models of motion detection assume that these delays reside in separate classes of slow and fast thalamic cells, or slow and fast synaptic transmission. We used a data-driven modeling approach to generate a model that instead uses recurrent network dynamics with a single, fixed temporal integration window to implement the velocity computation. This model successfully reproduced the temporal response dynamics of a population of motion sensitive neurons in macaque middle temporal area (MT) and its constituent parts matched many of the properties found in the motion processing pathway (e.g., Gabor-like receptive fields (RFs), simple and complex cells, spatially asymmetric excitation and inhibition). Reverse correlation analysis revealed that a simplified network based on first and second order space-time correlations of the recurrent model behaved much like a feedforward motion energy (ME) model. The feedforward model, however, failed to capture the full speed tuning and direction selectivity properties based on higher than second order space-time correlations typically found in MT. These findings support the idea that recurrent network connectivity can create temporal delays to compute velocity. Moreover, the model explains why the motion detection system often behaves like a feedforward ME network, even though the anatomical evidence strongly suggests that this network should be dominated by recurrent feedback.

“…6A). According to models of decoding that we (Krekelberg et al 2006a(Krekelberg et al , 2006bKrekelberg and van Wezel 2013) and others (Churchland and Lisberger 2001;Priebe and Lisberger 2004) used to relate neural activity to perception, this predicts lower sensitivity to inverting-contrast motion, which seems at odds with the finding that human observers are equally sensitive to both types of motion (Bours et al 2009). However, we also found a widening of MT's TCIs (Fig.…”

Section: Primary Visual Versus Middle Temporal Cortexmentioning

confidence: 95%

Evidence and Counterevidence in Motion Perception

Duijnhouwer

2015

Cereb. Cortex

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Sensory neurons gather evidence in favor of the specific stimuli to which they are tuned, but they could improve their sensitivity by also taking counterevidence into account. The Bours-Lankheet model for motion detection uses counterevidence that relies on a specific combination of the ON and OFF channels in the early visual system. Specifically, the model detects pairs of flashes that occur separated in space and time. If the flashes have the same contrast polarity, they are interpreted as evidence in favor of the corresponding motion. But if they have opposite contrasts, they are interpreted as evidence against it. This mechanism provides an explanation for reverse-phi (the perceived reversal of an apparent motion stimulus due to periodic contrastinversions) that is a conceptual departure from the standard explanations of the effect. Here, we investigate this counterevidence mechanism by measuring directional tuning curves of neurons in the primary visual and middle temporal cortex areas of awake, behaving macaques using constant-contrast and inverting-contrast moving dot stimuli. Our electrophysiological data support the Bours-Lankheet model and suggest that the counterevidence computation occurs at an early stage of neural processing not captured by the standard models.