Neuromorphic excitable maps for visual processing

The ripple pond: enabling spiking networks to see

We present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns (TP) suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilizing the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable TP and the use of asynchronous frames for information binding.

Multi-target neurostimulation for adequate long-term relief of neuropathic and nociceptive chronic pain components

Successful treatment of chronic pain for patients with failed back surgery syndrome can be extremely complicated. These patients require careful and individualized clinical assessment, as they often present with mixed pain syndromes that involve both neuropathic and nociceptive components. The distinct types of pain involved in such cases may require combined treatments from individual interventions that are analgesically independent and specific for each type of pain involved. Neuromodulation by electric stimulation at appropriately chosen targets and combinations may be an important option to consider for such patients. We present a case of combined debilitating axial nociceptive spinal pain and bilateral neuropathic leg pain in a patient after 14 failed back operations. A combination of spinal cord stimulation (SCS) and deep brain stimulation in the periventricular gray (PVG) have successfully provided the patient with complete relief of both components of his chronic pain condition, after all other pain management options had been exhausted. By alternating activation of each implanted stimulator separately and in conjunction, we were able to demonstrate a clinically independent analgesic character for each stimulation system, each specific to a particular type of pain. The SCS provided complete relief of the neuropathic pain component, without affecting the nociceptive component at all. The PVG stimulation provided complete relief of the nociceptive component, without affecting the neuropathic component at all. In combination, there was complete relief of the total chronic pain condition. There appeared to be no overlapping or synergistic effect between the two neuromodulation systems in the patient. The patient has had prolonged complete relief from his chronic pain condition with the combined neuromodulation intervention over 22 years of follow-up.

Spatio-temporal adaptation in the unsupervised development of networked visual neurons

There have been many computational models mimicking the visual cortex that are based on spatial adaptations of unsupervised neural networks. In this paper, we present a new model called neuronal cluster which includes spatial as well as temporal weights in its unified adaptation scheme. The "in-place" nature of the model is based on two biologically plausible learning rules, Hebbian rule and lateral inhibition. We present the mathematical demonstration that the temporal weights are derived from the delay in lateral inhibition. By training with the natural videos, this model can develop spatio-temporal features such as orientation selective cells, motion sensitive cells, and spatio-temporal complex cells. The unified nature of the adaptation scheme allows us to construct a multilayered and task-independent attention selection network which uses the same learning rule for edge, motion, and color detection, and we can use this network to engage in attention selection in both static and dynamic scenes.