Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach

Abstract

Interactions between the body and the environment occur within the peripersonal space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored on the body and restricted in depth. The extension in depth of PPS neurons’ RFs has been documented to change dynamically as a function of the velocity of incoming stimuli, but the underlying neural mechanisms are still unknown. Here, by integrating a psychophysical approach with neural network modeling, we propose a mechanistic explanation behind this inherent dynamic property of PPS. We psychophysically mapped the size of participant’s peri-face and peri-trunk space as a function of the velocity of task-irrelevant approaching auditory stimuli. Findings indicated that the peri-trunk space was larger than the peri-face space, and, importantly, as for the neurophysiological delineation of RFs, both of these representations enlarged as the velocity of incoming sound increased. We propose a neural network model to mechanistically interpret these findings: the network includes reciprocal connections between unisensory areas and higher order multisensory neurons, and it implements neural adaptation to persistent stimulation as a mechanism sensitive to stimulus velocity. The network was capable of replicating the behavioral observations of PPS size remapping and relates behavioral proxies of PPS size to neurophysiological measures of multisensory neurons’ RF size. We propose that a biologically plausible neural adaptation mechanism embedded within the network encoding for PPS can be responsible for the dynamic alterations in PPS size as a function of the velocity of incoming stimuli.NEW & NOTEWORTHY Interactions between body and environment occur within the peripersonal space (PPS). PPS neurons are highly dynamic, adapting online as a function of body-object interactions. The mechanistic underpinning PPS dynamic properties are unexplained. We demonstrate with a psychophysical approach that PPS enlarges as incoming stimulus velocity increases, efficiently preventing contacts with faster approaching objects. We present a neurocomputational model of multisensory PPS implementing neural adaptation to persistent stimulation to propose a neurophysiological mechanism underlying this effect.

From multisensory integration in peripersonal space to bodily self‐consciousness: from statistical regularities to statistical inference

“…Lastly related to the role of PPS in prediction, a seminal observation by Fogassi and colleagues suggested that the receptive field of PPS neurons expands when the velocity of incoming stimuli increases; as if to anticipate the moment of contact. Our group has recently demonstrated an analogous effect in humans, and by incorporating discharge adaptation in a neural network model of PPS we were able to mechanistically account for the observation . Namely, we have recently expanded on a neural network model of PPS capable of replicating the plastic nature of PPS (e.g., the fact that it expands after tool use in the far space) to now equally replicate the dynamic nature of PPS (e.g., the fact that it reshapes as a function of the velocity of the incoming stimuli) .…”

“…Our group has recently demonstrated an analogous effect in humans, and by incorporating discharge adaptation in a neural network model of PPS we were able to mechanistically account for the observation . Namely, we have recently expanded on a neural network model of PPS capable of replicating the plastic nature of PPS (e.g., the fact that it expands after tool use in the far space) to now equally replicate the dynamic nature of PPS (e.g., the fact that it reshapes as a function of the velocity of the incoming stimuli) . That is, while the model was previously capable of mimicking behavior that resulted from manipulations in the order minutes to hours, with the inclusion of a neural adaptation mechanism, it may now account for behavioral and neurophysiological findings that occur on a trial‐per‐trial basis (e.g., whether the particular stimulus is approaching quickly or slowly).…”

Rapid Recalibration of Peri-Personal Space; Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

“…To suggest a putative mechanistic underpinning the observed rapid recalibration of PPS we employed a nonspiking biologically inspired neural network model that has previously been demonstrated to account for a number of PPS phenomena (Magosso et al, 2010a, b;Serino et al, 2015a;Noel et al, 2018b). Importantly, we did not attempt to build a new model from scratch to explain the rapid recalibration of PPS; contrarily, we simply took the most recent version of the model (Noel et al, 2018b) and imbued this model with Hebbian learning (as in Magosso et al, 2010a;Serino et al, 2015a) given the conceptual hypothesis that this form of learning ought in principle to account for rapid recalibration. This approach was taken as we considered it more powerful (conceptually) to demonstrate that a model already shown to account for a number of PPS phenomena can also incorporate the newly described rapid recalibration effect, than it is important to exactly fit behavioral results.…”

“…This approach was taken as we considered it more powerful (conceptually) to demonstrate that a model already shown to account for a number of PPS phenomena can also incorporate the newly described rapid recalibration effect, than it is important to exactly fit behavioral results. Previous iterations of this model can account for sigmoidal facilitation functions , the fact that PPS has different sizes for different body parts (Noel et al, 2018b), as well as its enlargement after tool-use (Magosso et al, 2010) and as a function of increasing exteroceptive signal velocities (Noel et al, 2018b). Thus, the model simply inherited previous parameters (see Table 1 in Noel et al, 2018a), with exception of those ruling Hebbian learning (for more detail regarding the model parameters and the robustness of the its PPS encoding to parameter selection see Noel et al, 2018b.…”

“…This space is encoded by a dedicated frontoparietal neural network, in which neurons possess visuo-tactile receptive fields anchored on particular body parts -most notably the face and hand, but also the trunk (Graziano et al, 1994(Graziano et al, , 1997(Graziano et al, , 1999Duhamel et al, 1998;Schlack et al, 2005;see Serino et al, 2015b, for an analogous demonstration in humans). These multisensory neurons are most responsive to dynamic looming stimuli (Fogassi et al, 1996;Noel et al, 2018b), and are part of a network which can evoke complex and stereotyped defensive movements upon microstimulation (Cooke et al, 2003;Cooke & Graziano, 2004). In turn, PPS is conceived as an adaptive multisensory space mediating both bodily protection and goal-directed action (Cooke & Graziano, 2004;Brozzoli et al, 2014;Serino, 2019).…”