Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userSensory Substitution : Perception Dedicated to Action
251
0
0.0
(
0
)
Authors
Bianca Hardy
Ask ChatGPT about the research
No Arabic abstract
Paul Bach Y Rita [1] is the precursor of sensory substitutions. He started thirty years ago using visuo-tactile prostheses with the intent of satisfying blind people. These prostheses, called Tactile Vision Substitution Systems (TVSS), transform a sensory input from a given modality (vision) into another modality (touch). These new systems seemed to induce quasi-visual perceptions. One of the authors interests dealt with the understanding of the coupling between actions and sensations in perception mechanisms [4]. Throughout his search, he noticed that the subjects had to move the camera themselves in order to recognise a 3D target-object or a figure placed in front of them. Our work consists in understanding how sensory information provided by a visuo-tactile prosthesis can be used for motor behaviour. In this aim, we used the most simple substitution device (one photoreceptor coupled with one tactile stimulator) in order to control and enrich our knowledge of the ties between perception and action.
rate research
Read More
In this preliminary study we address the question of the influence of handedness on the localization of targets perceived through a visuo-auditory substitution device. Participants hold the device in one hand in order to explore the environment and to perceive the target. They point to the estimated location of the target with the other hand. This preliminary results support our hypothesis that pointing is more accurate when the device is held in the right dominant hand. Dexterity has to be attributed to the active part of the perceptive system. This study has obviously to be completed but it shows how the concept of enaction is important and how it can be experimentaly addressed in the field of sensory substitution.
We present a theoretical study aiming at model fitting for sensory neurons. Conventional neural network training approaches are not applicable to this problem due to lack of continuous data. Although the stimulus can be considered as a smooth time dependent variable, the associated response will be a set of neural spike timings (roughly the instants of successive action potential peaks) which have no amplitude information. A recurrent neural network model can be fitted to such a stimulus-response data pair by using maximum likelihood estimation method where the likelihood function is derived from Poisson statistics of neural spiking. The universal approximation feature of the recurrent dynamical neuron network models allow us to describe excitatory-inhibitory characteristics of an actual sensory neural network with any desired number of neurons. The stimulus data is generated by a Phased Cosine Fourier series having fixed amplitude and frequency but a randomly shot phase. Various values of amplitude, stimulus component size and sample size are applied in order to examine the effect of stimulus to the identification process. Results are presented in tabular form at the end of this text.
The cerebrospinal fluid (CSF) constitutes an interface through which chemical cues can reach and modulate the activity of neurons located at the epithelial boundary within the entire nervous system. Here, we investigate the role and functional connectivity of a class of GABAergic sensory neurons contacting the CSF in the vertebrate spinal cord and referred to as CSF-cNs. The remote activation of CSF-cNs was shown to trigger delayed slow locomotion in the zebrafish larva, suggesting that these cells modulate components of locomotor central pattern generators (CPGs). Combining anatomy, electrophysiology, and optogenetics in vivo, we show that CSF-cNs form active GABAergic synapses onto V0-v glutamatergic interneurons, an essential component of locomotor CPGs. We confirmed that activating CSF-cNs at rest induced delayed slow locomotion in the fictive preparation. In contrast, the activation of CSF-cNs promptly inhibited ongoing slow locomotion. Moreover, selective activation of rostral CSF-cNs during ongoing activity disrupted rostrocaudal propagation of descending excitation along the spinal cord, indicating that CSF-cNs primarily act at the premotor level. Altogether, our results demonstrate how a spinal GABAergic sensory neuron can tune the excitability of locomotor CPGs in a state-dependent manner by projecting onto essential components of the excitatory premotor pool.
Pain is a multidimensional process, which can be modulated by emotions, however, the mechanisms underlying this modulation are unknown. We used pictures with different emotional valence (negative, positive, neutral) as primes and applied electrical painful stimuli as targets to healthy participants. We assessed pain intensity and unpleasantness ratings and recorded electroencephalograms (EEG). We found that pain unpleasantness, and not pain intensity ratings were modulated by emotion, with increased ratings for negative and decreased for positive pictures. We also found two consecutive gamma band oscillations (GBOs) related to pain processing from time frequency analyses of the EEG signals. An early GBO had a cortical distribution contralateral to the painful stimulus, and its amplitude was positively correlated with intensity and unpleasantness ratings, but not with prime valence. The late GBO had a centroparietal distribution and its amplitude was larger for negative compared to neutral and positive pictures. The emotional modulation effect (negative versus positive) of the late GBO amplitude was positively correlated with pain unpleasantness. The early GBO might reflect the overall pain perception, possibly involving the thalamocortical circuit, while the late GBO might be related to the affective dimension of pain and top-down related processes.
Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes of the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer {population models of interacting neurons that collectively encode stimulus information}. The key to disentangling intrinsic from extrinsic correlations is to infer the {couplings between neurons} separately from the encoding model, and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach on retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
