On a fast timescale, neurons mostly interact by short, stereotypical electrical impulses or spikes. Why? A common answer is that spikes are useful for long-distance communication, to avoid alterations while traveling along axons. But as it turns out, spikes are seen in many places outside neurons: in the heart, in muscles, in plants and even in protists. From these examples, it appears that action potentials mediate some form of coordinated action, a timed event. From this perspective, spikes should not be seen simply as noisy implementations of underlying continuous signals (a sort of analog-to-digital conversion), but rather as events or actions. I will give a number of examples of functional spike-based interactions in living systems.

It is widely known that motor learning leads to reorganization changes in the motor cortex. Recently, we have shown that using navigated transcranial magnetic stimulation (TMS) allows us to reliably trace interactions among motor cortical representations (MCRs) of different upper limb muscles. Using this approach, we investigate changes in the MCRs after fine finger movement training. Our preliminary results demonstrated that areas of the APB and ADM and their overlaps tended to increase after finger independence training. Considering the behavioral data, hand dexterity increased for both hands, but the amplitudes of voluntary contraction of the muscles for the APB and ADM did not change significantly. The behavioral results correspond with a previously described suggestion that hand strength and hand dexterity are not directly related as well as an increase in overlaps between MCRs of the trained muscles supports the idea that voluntary muscle relaxation is an active physiological process.

My group pioneered the development of a novel intracortical brain computer interface (iBCI) that decodes muscle activity (EMG) from signals recorded in the motor cortex of animals. We use these synthetic EMG signals to control Functional Electrical Stimulation (FES), which causes the muscles to contract and thereby restores rudimentary voluntary control of the paralyzed limb. In the past few years, there has been much interest in the fact that information from the millions of neurons active during movement can be reduced to a small number of “latent” signals in a low-dimensional manifold computed from the multiple neuron recordings. These signals can be used to provide a stable prediction of the animal’s behavior over many months-long periods, and they may also provide the means to implement methods of transfer learning across individuals, an application that could be of particular importance for paralyzed human users. We have begun to examine the representation within this latent space, of a broad range of behaviors, including well-learned, stereotyped movements in the lab, and more natural movements in the animal’s home cage, meant to better represent a person’s daily activities. We intend to develop an FES-based iBCI that will restore voluntary movement across a broad range of motor tasks without need for intermittent recalibration. However, the nonlinearities and context dependence within this low-dimensional manifold present significant challenges.

We have been developing Brain-Computer Interface (BCI) using electrocorticography (ECoG) [1] , which is recorded by electrodes implanted on brain surface, and magnetoencephalography (MEG) [2] , which records the cortical activities non-invasively, for the clinical applications. The invasive BCI using ECoG has been applied for severely paralyzed patient to restore the communication and motor function. The non-invasive BCI using MEG has been applied as a neurofeedback tool to modulate some pathological neural activities to treat some neuropsychiatric disorders. Although these techniques have been developed for clinical application, BCI is also an important tool to investigate neural function. For example, motor BCI records some neural activities in a part of the motor cortex to generate some movements of external devices. Although our motor system consists of complex system including motor cortex, basal ganglia, cerebellum, spinal cord and muscles, the BCI affords us to simplify the motor system with exactly known inputs, outputs and the relation of them. We can investigate the motor system by manipulating the parameters in BCI system. Recently, we are developing some BCIs to visualize and manipulate our perception and mental imagery. Although these BCI has been developed for clinical application, the BCI will be useful to understand our neural system to generate the perception and imagery. In this talk, I will introduce our study of phantom limb pain [3] , that is controlled by MEG-BCI, and the development of a communication BCI using ECoG [4] , that enable the subject to visualize the contents of their mental imagery. And I would like to discuss how much we can control our cortical activities that represent our perception and mental imagery. These examples demonstrate that BCI is a promising tool to visualize and manipulate the perception and imagery and to understand our consciousness. References 1. Yanagisawa, T., Hirata, M., Saitoh, Y., Kishima, H., Matsushita, K., Goto, T., Fukuma, R., Yokoi, H., Kamitani, Y., and Yoshimine, T. (2012). Electrocorticographic control of a prosthetic arm in paralyzed patients. AnnNeurol 71, 353-361. 2. Yanagisawa, T., Fukuma, R., Seymour, B., Hosomi, K., Kishima, H., Shimizu, T., Yokoi, H., Hirata, M., Yoshimine, T., Kamitani, Y., et al. (2016). Induced sensorimotor brain plasticity controls pain in phantom limb patients. Nature communications 7, 13209. 3. Yanagisawa, T., Fukuma, R., Seymour, B., Tanaka, M., Hosomi, K., Yamashita, O., Kishima, H., Kamitani, Y., and Saitoh, Y. (2020). BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial. Neurology 95, e417-e426. 4. Ryohei Fukuma, Takufumi Yanagisawa, Shinji Nishimoto, Hidenori Sugano, Kentaro Tamura, Shota Yamamoto, Yasushi Iimura, Yuya Fujita, Satoru Oshino, Naoki Tani, Naoko Koide-Majima, Yukiyasu Kamitani, Haruhiko Kishima (2022). Voluntary control of semantic neural representations by imagery with conflicting visual stimulation. arXiv arXiv:2112.01223.

In this talk I present the perspectives of the "neuropunk revolution'' technologies. One could understand the "neuropunk revolution'' as the integration of real-time neurosimulations into biological nervous/motor systems via neurostimulation or artificial robotic systems via integration with actuators. I see the added value of the real-time neurosimulations as bridge technology for the set of developed technologies: BCI, neuroprosthetics, AI, robotics to provide bio-compatible integration into biological or artificial limbs. Here I present the three types of integration of the "neuropunk revolution'' technologies as inbound, outbound and closed-loop in-outbound systems. I see the shift of the perspective of how we see now the set of technologies including AI, BCI, neuroprosthetics and robotics due to the proposed concept for example the integration of external to a body simulated part of the nervous system back into the biological nervous system or muscles.

Hydra is an extraordinary creature. Continuously replacing itself, it can live indefinitely, performing a stable repertoire of reasonably sophisticated behaviors. This remarkable stability under plasticity may be due to the uniform nature of its nervous system, which consists of two apparently noncommunicating nerve net layers. We use modeling to understand the role of active muscles and biomechanics interact with neural activity to shape Hydra behaviour. We will discuss our findings and thoughts on how this simple nervous system may self-organize to produce purposeful behavior.

The vIRt nucleus in the medulla, composed of mainly inhibitory neurons, is necessary for whisking rhythm generation. It innervates motoneurons in the facial nucleus (FN) that project to intrinsic vibrissa muscles. The nearby pre-Bötzinger complex (pBötC), which generates inhalation, sends inhibitory inputs to the vIRt nucleus which contribute to the synchronization of vIRt neurons. Lower-amplitude periodic whisking, however, can occur after decay of the pBötC signal. To explain how vIRt network generates these “intervening” whisks by bursting in synchrony, and how pBötC input induces strong whisks, we construct and analyze a conductance-based (CB) model of the vIRt circuit composed of hypothetical two groups, vIRtr and vIRtp, of bursting inhibitory neurons with spike-frequency adaptation currents and constant external inputs. The CB model is reduced to a rate model to enable analytical treatment. We find, analytically and computationally, that without pBötC input, periodic bursting states occur within a certain ranges of network connectivities. Whisk amplitudes increase with the level constant external input to the vIRT. With pBötC inhibition intact, the amplitude of the first whisk in a breathing cycle is larger than the intervening whisks for large pBötC input and small inhibitory coupling between the vIRT sub-populations. The pBötC input advances the next whisk and shortens its amplitude if it arrives at the beginning of the whisking cycle generated by the vIRT, and delays the next whisks if it arrives at the end of that cycle. Our theory provides a mechanism for whisking generation and reveals how whisking frequency and amplitude are controlled.

Each day we experience myriad somatosensory stimuli: hugs from loved ones, warm showers, a mosquito bite, and sore muscles after a workout. These tactile, thermal, itch, and nociceptive signals are detected by peripheral sensory neuron terminals distributed throughout our body, propagated into the spinal cord, and then transmitted to the brain through ascending spinal pathways. Primary sensory neurons that detect a wide range of somatosensory stimuli have been identified and characterized. In contrast, very little is known about how peripheral signals are integrated and processed within the spinal cord and conveyed to the brain to generate somatosensory perception and behavioral responses. We tackled this question by developing new mouse genetic tools to define projection neuron (PN) subsets of the anterolateral pathway, a major ascending spinal cord pathway, and combining these new tools with advanced anatomical, physiological, and behavioral approaches. We found that Gpr83+ PNs, a newly identified subset of spinal cord output neurons, and Tacr1+ PNs are largely non-overlapping populations that innervate distinct sets of subnuclei within the lateral parabrachial nucleus (PBNL) of the pons in a zonally segregated manner. In addition, Gpr83+ PNs are highly sensitive to cutaneous mechanical stimuli, receive strong synaptic inputs from primary mechanosensory neurons, and convey tactile information bilaterally to the PBNL in a non-topographically organized manner. Remarkably, Gpr83+ mechanosensory limb of the anterolateral pathway controls behaviors associated with different hedonic values (appetitive or aversive) in a scalable manner. This is the first study to identify a dedicated spinal cord output pathway that conveys affective touch signals to the brain and to define parallel ascending circuit modules that cooperate to convey tactile, thermal and noxious cutaneous signals from the spinal cord to the brain. This study has also revealed exciting new therapeutic opportunities for developing treatments for neurological disorders associated with pain and affective touch.

Carnosine is a natural dipeptide widely distributed in mammalian tissues and exists at particularly high concentrations in skeletal and cardiac muscles and brain. A growing body of evidence shows that carnosine is involved in many cellular defense mechanisms against oxidative stress, including inhibition of amyloid-β (Aβ) aggregation, modulation of nitric oxide (NO) metabolism, and scavenging both reactive nitrogen and oxygen species. Different types of cells are involved in the innate immune response, with macrophage cells representing those primarily activated, especially under different diseases characterized by oxidative stress and systemic inflammation such as depression and cardiovascular disorders. Microglia, the tissue-resident macrophages of the brain, are emerging as a central player in regulating key pathways in central nervous system inflammation; with specific regard to Alzheimer’s disease (AD) these cells exert a dual role: on one hand promoting the clearance of Aβ via phagocytosis, on the other hand increasing neuroinflammation through the secretion of inflammatory mediators and free radicals. The activity of carnosine was tested in an in vitro model of macrophage activation (M1) (RAW 264.7 cells stimulated with LPS + IFN-γ) and in a well-validated model of Aβ-induced neuroinflammation (BV-2 microglia treated with Aβ oligomers). An ample set of techniques/assays including MTT assay, trypan blue exclusion test, high performance liquid chromatography, high-throughput real-time PCR, western blot, atomic force microscopy, microchip electrophoresis coupled to laser-induced fluorescence, and ELISA aimed to evaluate the antioxidant and anti-inflammatory activities of carnosine was employed. In our experimental model of macrophage activation (M1), therapeutic concentrations of carnosine exerted the following effects: 1) an increased degradation rate of NO into its non-toxic end-products nitrite and nitrate; 2) the amelioration of the macrophage energy state, by restoring nucleoside triphosphates and counterbalancing the changes in ATP/ADP, NAD+/NADH and NADP+/NADPH ratio obtained by LPS + IFN-γ induction; 3) a reduced expression of pro-oxidant enzymes (NADPH oxidase, Cyclooxygenase-2) and of the lipid peroxidation product malondialdehyde; 4) the rescue of antioxidant enzymes expression (Glutathione peroxidase 1, Superoxide dismutase 2, Catalase); 5) an increased synthesis of transforming growth factor-β1 (TGF-β1) combined with the negative modulation of interleukines 1β and 6 (IL-1β and IL-6), and 6) the induction of nuclear factor erythroid-derived 2-like 2 (Nrf2) and heme oxygenase-1 (HO-1). In our experimental model of Aβ-induced neuroinflammation, carnosine: 1) prevented cell death in BV-2 cells challenged with Aβ oligomers; 2) lowered oxidative stress by decreasing the expression of inducible nitric oxide synthase and NADPH oxidase, and the concentrations of nitric oxide and superoxide anion; 3) decreased the secretion of pro-inflammatory cytokines such as IL-1β simultaneously rescuing IL-10 levels and increasing the expression and the release of TGF-β1; 4) prevented Aβ-induced neurodegeneration in primary mixed neuronal cultures challenged with Aβ oligomers and these neuroprotective effects was completely abolished by SB431542, a selective inhibitor of type-1 TGF-β receptor. Overall, our data suggest a novel multimodal mechanism of action of carnosine underlying its protective effects in macrophages and microglia and the therapeutic potential of this dipeptide in counteracting pro-oxidant and pro-inflammatory phenomena observed in different disorders characterized by elevated levels of oxidative stress and inflammation such as depression, cardiovascular disorders, and Alzheimer’s disease.

Animals rely on an internal sense of body position and movement to effectively control motor behaviour. This sense of proprioception is mediated by diverse populations of internal mechanosensory neurons distributed throughout the body. My lab is trying to understand how proprioceptive stimuli are detected by sensory neurons, integrated and transformed in central circuits, and used to guide motor output. We approach these questions using genetic tools, in vivo two-photon imaging, and patch-clamp electrophysiology in Drosophila. We recently found that the axons of fly leg proprioceptors are organized into distinct functional projections that contain topographic representations of specific kinematic features: one group of axons encodes tibia position, another encodes movement direction, and a third encodes bidirectional movement and vibration frequency. Whole-cell recordings from downstream neurons reveal that position, movement, and directional information remain segregated in central circuits. These feedback signals then converge upon motor neurons that control leg muscles during walking. Overall, our findings reveal how a low-dimensional stimulus – the angle of a single leg joint – is encoded by a diverse population of mechanosensory neurons. Specific proprioceptive parameters are initially processed by parallel pathways, but are ultimately integrated to influence motor output. This architecture may help to maximize information transmission, processing speed, and robustness, which are critical for feedback control of the limbs during adaptive locomotion.