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Extremely interesting work on artificial tactile response for robotics and the importance of feedforward spiking neural networks to advance this research:
www.nature.com
One of the key features of the human fingertip is its ability to recognize edge orientation. In this way, it was illustrated that the random innervation of the mechanoreceptors by the primary afferents allows the encoding of orientation information through the spatiotemporal spiking pattern. This structure organizes a peripheral neural mechanism for extraction and then transmission of geometric features of the touched objects. The proposed hierarchical spiking neural network successfully discriminated edge orientation stimuli irrespective of edge location. It was shown that using the first spikes of cortical neurons; the orientation of stimuli (scanned or indented edge) was recognizable. The effect of afferent receptive field size was compared in two different experiments (scanned and indented edge). Orientation detection of the scanned edge stimuli in the first spikes of cortical neurons was improved when the afferents’ receptive field size was increased. Nevertheless, for the indented edge experiment, the situation was reversed and increasing the size of the afferents receptive field resulted in the reduction of correct detection. The findings showed that the importance of receptive field size depends on the specific tasks and experiments. Recent studies have shown that the main connections in neuronal pathways are formed during the developmental process38,39,40. However, the exact cortical dynamics and function have not been studied yet. Here, we investigated edge orientation detection through the cortical neurons as a biomimetic classifier. We showed that the intensity of a neuron’s response would signal edge orientation because its firing rate would increase with the degree of spatial coincidence between the neuron’s highly sensitive zones (excitatory region of receptive field) and the local skin deformations formed by edge indentation. That is, for a given neuron, some edge orientations exhibit more spatial coincidence than others and thus stronger responses are produced.
Also, the role of the inhibitory current which forms the lateral inhibition within the cuneate nucleus was studied. Indeed, the simulation results suggest that when lateral inhibition has increased the process of spike filtering is amplified. This leads to the reduction in “noise” within the system and hence the third-order neurons are activated by a strong and consistent signal. This also increases the spatial resolution of the receptive fields and gives them a more distinct border which improves discrimination between two separate points of simultaneous stimulation. Although other forms of lateral inhibition are also observed, the “feedforward” type of lateral inhibition is likely the most significant41. Various aspects of tactile sensitivity have been related to different forms of neuronal inhibitory function. Impaired reactions to tactile stimuli in children with autism spectrum disorder (ASD) are frequently reported symptoms. Indeed, impairments in filtering of or adaptation to tactile inputs have been described in ASD42. Under the assumption that the inhibitory mechanism is altered in ASD43,44, it can be suggested that dysfunction in lateral inhibition of the second layer of tactile processing or malfunction in the formation of the inhibitory sub-regions of the cortical neurons may also have a role. Understanding the specific mechanisms underlying sensory symptoms in ASD is still under investigation which may allow for more specific therapeutic approaches in the future.
The main limitation of the proposed spiking model is the lack of neural recordings for all network layers. Nevertheless, the model is based on the significant literature and published data for model building and validation. The proposed spiking network for a tactile system can be employed in the design and implementation of sensory neuroprostheses applications45,46,47,48. Additionally, the broad significance of this work is that the biomimetic tactile sensing and edge encoding are useful in robotic applications for shape recognition and object grasping and palpation49,50,51.”
A functional spiking neuronal network for tactile sensing pathway to process edge orientation - Scientific Reports
To obtain deeper insights into the tactile processing pathway from a population-level point of view, we have modeled three stages of the tactile pathway from the periphery to the cortex in response to indentation and scanned edge stimuli at different orientations. Three stages in the tactile...
“Conclusion
The functional modeling of the tactile pathway from the cutaneous mechanoreceptors (first layer), to the cuneate nucleus (second layer) up to the somatosensory area 3b (third layer), provides a mechanistic tool for understanding the role of different neuronal networks in tactile information processing. The current research highlights the importance of each stage in neuronal population coding in the detection of edge orientation. It also provides a deeper understanding of how the response of cortical neurons to edge stimuli changes as the mechanoreceptor innervation mechanisms and receptive fields are changed. The simulated spiking neural networks are functionally compatible with physiological observations across a wide range of conditions sampled from literature. Indeed, many recent neurophysiological findings have been embedded in the proposed model and its performance—based on spiking responses of cortical neurons—has been demonstrated for decoding of edge orientations.One of the key features of the human fingertip is its ability to recognize edge orientation. In this way, it was illustrated that the random innervation of the mechanoreceptors by the primary afferents allows the encoding of orientation information through the spatiotemporal spiking pattern. This structure organizes a peripheral neural mechanism for extraction and then transmission of geometric features of the touched objects. The proposed hierarchical spiking neural network successfully discriminated edge orientation stimuli irrespective of edge location. It was shown that using the first spikes of cortical neurons; the orientation of stimuli (scanned or indented edge) was recognizable. The effect of afferent receptive field size was compared in two different experiments (scanned and indented edge). Orientation detection of the scanned edge stimuli in the first spikes of cortical neurons was improved when the afferents’ receptive field size was increased. Nevertheless, for the indented edge experiment, the situation was reversed and increasing the size of the afferents receptive field resulted in the reduction of correct detection. The findings showed that the importance of receptive field size depends on the specific tasks and experiments. Recent studies have shown that the main connections in neuronal pathways are formed during the developmental process38,39,40. However, the exact cortical dynamics and function have not been studied yet. Here, we investigated edge orientation detection through the cortical neurons as a biomimetic classifier. We showed that the intensity of a neuron’s response would signal edge orientation because its firing rate would increase with the degree of spatial coincidence between the neuron’s highly sensitive zones (excitatory region of receptive field) and the local skin deformations formed by edge indentation. That is, for a given neuron, some edge orientations exhibit more spatial coincidence than others and thus stronger responses are produced.
Also, the role of the inhibitory current which forms the lateral inhibition within the cuneate nucleus was studied. Indeed, the simulation results suggest that when lateral inhibition has increased the process of spike filtering is amplified. This leads to the reduction in “noise” within the system and hence the third-order neurons are activated by a strong and consistent signal. This also increases the spatial resolution of the receptive fields and gives them a more distinct border which improves discrimination between two separate points of simultaneous stimulation. Although other forms of lateral inhibition are also observed, the “feedforward” type of lateral inhibition is likely the most significant41. Various aspects of tactile sensitivity have been related to different forms of neuronal inhibitory function. Impaired reactions to tactile stimuli in children with autism spectrum disorder (ASD) are frequently reported symptoms. Indeed, impairments in filtering of or adaptation to tactile inputs have been described in ASD42. Under the assumption that the inhibitory mechanism is altered in ASD43,44, it can be suggested that dysfunction in lateral inhibition of the second layer of tactile processing or malfunction in the formation of the inhibitory sub-regions of the cortical neurons may also have a role. Understanding the specific mechanisms underlying sensory symptoms in ASD is still under investigation which may allow for more specific therapeutic approaches in the future.
The main limitation of the proposed spiking model is the lack of neural recordings for all network layers. Nevertheless, the model is based on the significant literature and published data for model building and validation. The proposed spiking network for a tactile system can be employed in the design and implementation of sensory neuroprostheses applications45,46,47,48. Additionally, the broad significance of this work is that the biomimetic tactile sensing and edge encoding are useful in robotic applications for shape recognition and object grasping and palpation49,50,51.”
. Merry Christmas and happy Boxing day all, even 
