Logarithmic distributions prove that intrinsic learning is Hebbian

Principles of visual cortex excitatory microcircuit organization

Synapse-specific connectivity and dynamics determine microcircuit function but are challenging to explore with classic paired recordings due to their low throughput. We therefore implemented optomapping, a ∼100-fold faster two-photon optogenetic method. In mouse primary visual cortex (V1), we optomapped 30,454 candidate inputs to reveal 1,790 excitatory inputs to pyramidal, basket, and Martinotti cells. Across these cell types, log-normal distribution of synaptic efficacies emerged as a principle. For pyramidal cells, optomapping reproduced the canonical circuit but unexpectedly uncovered that the excitation of basket cells concentrated to layer 5 and that of Martinotti cells dominated in layer 2/3. The excitation of basket cells was stronger and reached farther than the excitation of pyramidal cells, which may promote stability. Short-term plasticity surprisingly depended on cortical layer in addition to target cell. Finally, optomapping revealed an overrepresentation of shared inputs for interconnected layer-6 pyramidal cells. Thus, by resolving the throughput problem, optomapping uncovered hitherto unappreciated principles of V1 structure.

Hierarchical organization of human physical activity

Human physical activity (HPA), a fundamental physiological signal characteristic of bodily motion is of rapidly growing interest in multidisciplinary research. Here we report the existence of hitherto unidentified hierarchical levels in the temporal organization of HPA on the ultradian scale: on the minute's scale, passive periods are followed by activity bursts of similar intensity ('quanta') that are organized into superstructures on the hours- and on the daily scale. The time course of HPA can be considered a stochastic, quasi-binary process, where quanta, assigned to task-oriented actions are organized into work packages on higher levels of hierarchy. In order to grasp the essence of this complex dynamic behaviour, we established a stochastic mathematical model which could reproduce the main statistical features of real activity time series. The results are expected to provide important data for developing novel behavioural models and advancing the diagnostics of neurological or psychiatric diseases.

Linking spontaneous and stimulated spine dynamics

Our brains continuously acquire and store memories through synaptic plasticity. However, spontaneous synaptic changes can also occur and pose a challenge for maintaining stable memories. Despite fluctuations in synapse size, recent studies have shown that key population-level synaptic properties remain stable over time. This raises the question of how local synaptic plasticity affects the global population-level synaptic size distribution and whether individual synapses undergoing plasticity escape the stable distribution to encode specific memories. To address this question, we (i) studied spontaneously evolving spines and (ii) induced synaptic potentiation at selected sites while observing the spine distribution pre- and post-stimulation. We designed a stochastic model to describe how the current size of a synapse affects its future size under baseline and stimulation conditions and how these local effects give rise to population-level synaptic shifts. Our study offers insights into how seemingly spontaneous synaptic fluctuations and local plasticity both contribute to population-level synaptic dynamics.

Computational Modeling of Structural Synaptic Plasticity in Echo State Networks

Most existing studies on computational modeling of neural plasticity have focused on synaptic plasticity. However, regulation of the internal weights in the reservoir based on synaptic plasticity often results in unstable learning dynamics. In this article, a structural synaptic plasticity learning rule is proposed to train the weights and add or remove neurons within the reservoir, which is shown to be able to alleviate the instability of the synaptic plasticity, and to contribute to increase the memory capacity of the network as well. Our experimental results also reveal that a few stronger connections may last for a longer period of time in a constantly changing network structure, and are relatively resistant to decay or disruptions in the learning process. These results are consistent with the evidence observed in biological systems. Finally, we show that an echo state network (ESN) using the proposed structural plasticity rule outperforms an ESN using synaptic plasticity and three state-of-the-art ESNs on four benchmark tasks.

Long-Term Changes in Axon Calibers after Injury: Observations on the Mouse Corticospinal Tract

White matter pathology is common across a wide spectrum of neurological diseases. Characterizing this pathology is important for both a mechanistic understanding of neurological diseases as well as for the development of neuroimaging biomarkers. Although axonal calibers can vary by orders of magnitude, they are tightly regulated and related to neuronal function, and changes in axon calibers have been reported in several diseases and their models. In this study, we utilize the impact acceleration model of traumatic brain injury (IA-TBI) to assess early and late changes in the axon diameter distribution (ADD) of the mouse corticospinal tract using Airyscan and electron microscopy. We find that axon calibers follow a lognormal distribution whose parameters significantly change after injury. While IA-TBI leads to 30% loss of corticospinal axons by day 7 with a bias for larger axons, at 21 days after injury we find a significant redistribution of axon frequencies that is driven by a reduction in large-caliber axons in the absence of detectable degeneration. We postulate that changes in ADD features may reflect a functional adaptation of injured neural systems. Moreover, we find that ADD features offer an accurate way to discriminate between injured and non-injured mice. Exploring injury-related ADD signatures by histology or new emerging neuroimaging modalities may offer a more nuanced and comprehensive way to characterize white matter pathology and may also have the potential to generate novel biomarkers of injury.

Conceptual Method of Temperature Sensation in Bionic Hand by Extraordinary Perceptual Phenomenon

Lack of temperature sensation of myoelectric prosthetic hand limits the daily activities of amputees. To this end, a non-invasive temperature sensation method is proposed to train amputees to sense temperature with psychophysical sensory substitution. In this study, 22 healthy participants took part besides 5 amputee participants. The duration time of the study was 31 days with five test steps according to the Leitner technique. An adjustable temperature mug and a Peltier were used to change the temperature of the water/phantom digits to induce temperature to participants. Also, to isolate the surroundings and show colors, a Virtual Reality (VR) glass was employed. The statistical results conducted are based on the response of participants with questionnaire method. Using Chi-square tests, it is concluded that participants answer the experiment significantly correctly using the Leitner technique (P value < 0.05). Also, by applying the "Repeated Measures ANOVA", it is noticed that the time of numbness felt by participants had significant (P value < 0.001) difference. Participants could remember lowest and highest temperatures significantly better than other temperatures (P value < 0.001); furthermore, the well-trained amputee participant practically using the prosthesis with 72.58% could identify object's temperature with only once time experimenting the color temperature.

Dynamical Mechanism Underlying Scale-Free Network Reorganization in Low Acetylcholine States Corresponding to Slow Wave Sleep

Sleep is indispensable for most animals' cognitive functions, and is hypothesized to be a major factor in memory consolidation. Although we do not fully understand the mechanisms of network reorganisation driving memory consolidation, available data suggests that sleep-associated neurochemical changes may be important for such processes. In particular, global acetylcholine levels change across the sleep/wake cycle, with high cholinergic tone during wake and REM sleep and low cholinergic tone during slow wave sleep. Furthermore, experimental perturbation of cholinergic tone has been shown to impact memory storage. Through in silico modeling of neuronal networks, we show how spiking dynamics change in highly heterogenous networks under varying levels of cholinergic tone, with neuronal networks under high cholinergic modulation firing asynchronously and at high frequencies, while those under low cholinergic modulation exhibit synchronous patterns of activity. We further examined the network's dynamics and its reorganization mediated via changing levels of acetylcholine within the context of different scale-free topologies, comparing network activity within the hub cells, a small group of neurons having high degree connectivity, and with the rest of the network. We show a dramatic, state-dependent change in information flow throughout the network, with highly active hub cells integrating information in a high-acetylcholine state, and transferring it to rest of the network in a low-acetylcholine state. This result is experimentally corroborated by frequency-dependent frequency changes observed in vivo experiments. Together, these findings provide insight into how new neurons are recruited into memory traces during sleep, a mechanism which may underlie system memory consolidation.

Computational roles of intrinsic synaptic dynamics

Conventional theories assume that long-term information storage in the brain is implemented by modifying synaptic efficacy. Recent experimental findings challenge this view by demonstrating that dendritic spine sizes, or their corresponding synaptic weights, are highly volatile even in the absence of neural activity. Here, we review previous computational works on the roles of these intrinsic synaptic dynamics. We first present the possibility for neuronal networks to sustain stable performance in their presence, and we then hypothesize that intrinsic dynamics could be more than mere noise to withstand, but they may improve information processing in the brain.