Neuroscience
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Fear of falling increases conscious control of balance and postural threat warrants accurate anticipatory motor commands for keeping a safe body posture. This study examines the anticipatory (APAs) and compensatory (CPAs) postural adjustments generated in response to an external perturbation while individuals are positioned at two different altitudes (2 cm and 80 cm) from the floor level. The main result indicates that due to the perceived emotional threat, different agonist and antagonist muscles synergies (R and C-Indexes) are manifested, particularly during the anticipatory phase. ⋯ Interestingly, the APAs strategies were modified under different postural threats by controlling the agonist-antagonist muscles at different joints of lower extremity. For CPAs the reciprocal activation was less applied compared to muscles co-activation to unsure larger margin for compensatory adjustments as needed and re-establish the postural stability. The results indicate that when facing to a postural threat, the CNS modulates the anticipatory and compensatory phases of postural adjustments to minimize the risk of falling.
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Proteinase-activated receptor-1 (PAR1) antagonist plays a protective effect in brain injury. We investigated the potential function and mechanisms of PAR1 antagonist in ICH-induced brain injury. Results showed that PAR1 antagonist protected against neurobehavior deficits, brain edema and blood-brain barrier integrity in ICH mice via the JNK/ERK/p38 MAPK signaling pathway at 24 h after ICH. ⋯ Moreover, the protective effect of PAR1 antagonist on ICH-induced brain injury was blocked by FGL2 or TLR4 overexpression, and the levels of p-JNK, p-ERK and p-p38 MAPK were increased. Furthermore, PAR1 antagonist combined with TLR4 antagonist markedly alleviated brain injury after ICH at 72 h. Overall, PAR1 antagonist protected against short-term brain injury, and the effect of PAR1 antagonist on ICH-induced brain injury was mediated by FGL2 or TLR4.
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The nociceptive withdrawal reflex (NWR) is a behavioral response to protect the body from noxious stimuli. The spatial characteristics of the stimulus modulate the reflex response to prevent damage to the affected tissue. Interneurons in the deep dorsal horn in the spinal cord encode the relationship between stimulus characteristics and the magnitude of the NWR and are also likely integrating spatial information of the nociceptive stimulus. ⋯ In contrast, the NWR recorded during the attention task did not differ from baseline. These results further support that the spinal NWR pathway is under descending control which can be modulated by cognitive processes. The NWRs recorded over both proximal and distal muscles were similarly affected by the tasks, suggesting that the descending control affects the lower leg spinal system, with no discrimination between spinal segments.
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Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option for capturing the complexities of real neural computation is to use cascade models, which treat individual neurons as a cascade of linear and non-linear operations, akin to a multi-layer artificial neural network. ⋯ Given their tractable mathematical structure, we show that neuron models expressed in terms of parallel recurrent cascades can themselves be integrated into multi-layered artificial neural networks and trained to perform complex tasks. We go on to discuss potential implications and uses of these models for artificial intelligence. Overall, we argue that parallel, recurrent cascade models provide an important, unifying tool for capturing single-cell computation and exploring the algorithmic implications of physiological phenomena.
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The dendritic membrane potential was recently measured for the first time in drug-free, naturally behaving rats over several days. These showed that neuronal dendrites generate a lot of sodium spikes, up to ten times as many as the somatic spikes. These key experimental findings are reviewed here, along with a discussion of computational models, and computational consequences of such intense spike traffic in dendrites. ⋯ One remarkable aspect is that in the model, with fast dendritic spikes, the efficacy of synaptic strength in terms of driving the somatic activity is much less dependent on the position of the synapse in dendrites. This property suggests that fast dendritic spikes is a way to confer to neurons the possibility to grow complex dendritic trees with little computational loss for the distal most synapses, and thus form very complex networks with high density of connections, such as typically in the human brain. Another important consequence is that dendritically localized spikes can allow simultaneous but different computations on different dendritic branches, thereby greatly increasing the computational capacity and complexity of neuronal networks.