Feedforward and feedback pathways interact in specific dendritic domains to enable cognitive functions such as predictive processing and learning. Based on axonal projections, hierarchically lower ...areas are thought to form synapses primarily on dendrites in middle cortical layers, whereas higher-order areas are thought to target dendrites in layer 1 and in deep layers. However, the extent to which functional synapses form in regions of axodendritic overlap has not been extensively studied. Here, we use viral tracing in the secondary visual cortex of male mice to map brain-wide inputs to thick-tufted layer 5 pyramidal neurons. Furthermore, we provide a comprehensive map of input locations through subcellular optogenetic circuit mapping. We show that input pathways target distinct dendritic domains with far greater specificity than appears from their axonal branching, often deviating substantially from the canonical patterns. Common assumptions regarding the dendrite-level interaction of feedforward and feedback inputs may thus need revisiting.
Perception and learning depend on the ability of the brain to shape neuronal representations across all processing stages. Long-range connections across different hierarchical levels enable diverse sources of contextual information, such as predictions or motivational state, to modify feedforward signals. Assumptions regarding the organization of this hierarchical connectivity have not been extensively verified. Here, we assess the synaptic connectivity of brain-wide projections onto pyramidal neurons in the visual cortex of mice. Using trans-synaptic viral tracing and subcellular optogenetic circuit mapping, we show that functional synapses do not follow the consistent connectivity rule predicted by their axonal branching patterns. These findings highlight the diversity of computational strategies operating throughout cortical networks and may aid in building better artificial networks.
Anatomical similarity across the neocortex has led to the common assumption that the circuitry is modular and performs stereotyped computations. Layer 5 pyramidal neurons (L5PNs) in particular are ...thought to be central to cortical computation because of their extensive arborisation and nonlinear dendritic operations. Here, we demonstrate that computations associated with dendritic Ca
plateaus in mouse L5PNs vary substantially between the primary and secondary visual cortices. L5PNs in the secondary visual cortex show reduced dendritic excitability and smaller propensity for burst firing. This reduced excitability is correlated with shorter apical dendrites. Using numerical modelling, we uncover a universal principle underlying the influence of apical length on dendritic backpropagation and excitability, based on a Na
channel-dependent broadening of backpropagating action potentials. In summary, we provide new insights into the modulation of dendritic excitability by apical dendrite length and show that the operational repertoire of L5PNs is not universal throughout the brain.
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient AI and learning machines. An ...important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing material phase transitions. Here, we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO
), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO
devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to fourfold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning in neuromorphic hardware.
It is generally appreciated that storing memories of specific events in the mammalian brain, and associating features of the environment with behavioral outcomes requires fine-tuning of the strengths ...of connections between neurons through synaptic plasticity. It is less understood whether the organization of neuronal circuits comprised of multiple distinct neuronal cell types provides an architectural prior that facilitates learning and memory by generating unique patterns of neuronal activity in response to different stimuli in the environment, even before plasticity and learning occur. Here we simulated a neuronal network responding to sensory stimuli, and systematically determined the effects of specific neuronal cell types and connections on three key metrics of neuronal sensory representations: sparsity, selectivity, and discriminability. We found that when the total amount of input varied considerably across stimuli, standard feedforward and feedback inhibitory circuit motifs failed to discriminate all stimuli without sacrificing sparsity or selectivity. Interestingly, networks that included dedicated excitatory feedback interneurons based on the mossy cells of the hippocampal dentate gyrus exhibited improved pattern separation, a result that depended on the indirect recruitment of feedback inhibition. These results elucidate the roles of cellular diversity and neural circuit architecture on generating neuronal representations with properties advantageous for memory storage and recall.
Design of hardware based on biological principles of neuronal computation and plasticity in the brain is a leading approach to realizing energy- and sample-efficient artificial intelligence and ...learning machines. An important factor in selection of the hardware building blocks is the identification of candidate materials with physical properties suitable to emulate the large dynamic ranges and varied timescales of neuronal signaling. Previous work has shown that the all-or-none spiking behavior of neurons can be mimicked by threshold switches utilizing phase transitions. Here we demonstrate that devices based on a prototypical metal-insulator-transition material, vanadium dioxide (VO2), can be dynamically controlled to access a continuum of intermediate resistance states. Furthermore, the timescale of their intrinsic relaxation can be configured to match a range of biologically-relevant timescales from milliseconds to seconds. We exploit these device properties to emulate three aspects of neuronal analog computation: fast (~1 ms) spiking in a neuronal soma compartment, slow (~100 ms) spiking in a dendritic compartment, and ultraslow (~1 s) biochemical signaling involved in temporal credit assignment for a recently discovered biological mechanism of one-shot learning. Simulations show that an artificial neural network using properties of VO2 devices to control an agent navigating a spatial environment can learn an efficient path to a reward in up to 4 fold fewer trials than standard methods. The phase relaxations described in our study may be engineered in a variety of materials, and can be controlled by thermal, electrical, or optical stimuli, suggesting further opportunities to emulate biological learning.
The vast majority of stent thrombosis occurs in the acute and sub-acute phases and is more common in patients with acute coronary syndromes, due to the thrombotic milieu where stent struts are ...positioned. Stent thrombosis is likely due to incomplete tissue coverage of metallic stents as the contact between metallic stents and blood elements may lead to platelet adhesion and trigger vessel thrombosis. If a stent is covered after 7 days, the risk that it will be found uncovered at later stages is very low (<1 %). In this article, we demonstrate that diamond-like carbon (DLC) coatings, deposited by physical vapour deposition, promote rapid endothelisation of coronary stent devices, with very low platelets activation, reducing thrombotic clots. We relate these behaviours to the surface and bulk material properties of the DLC films, subjected to a comprehensive chemico-physical characterisation using several techniques (X-ray photoelectron spectroscopy, atomic force microscopy, field-emission scanning electron microscope, transmission electron microscopy combined with electron energy loss spectroscopy, Raman and dispersive X-ray spectroscopy). In vivo studies, conducted on 24 pigs, have shown complete endothelisation after 7 days, with no fibrin mesh and with only rare monocytes scattered on the endothelial layer while 30 and 180 days tests have shown reduced inflammatory activation and a complete stabilisation of the vessel healing, with a minimal neointimal proliferation. The integral and permanent DLC film coating improves haemo- and bio-compatibility and leads to an excellent early vessel healing of the stent whilst the extremely thin strut thickness reduces the amount of late neointima and consequently the risk of late restenosis. These data should translate into a reduced acute and sub-acute stent thrombosis.
Fig
Carbon film-coated stent (SEM-×500 magnification). Detail of the endothelial layer.
The prognostic role of GB virus type C (GBV-C) viraemia in HIV-infected subjects treated with highly active antiretroviral therapy (HAART) is still undefined. The aim of this analysis is to assess ...the relationship between GBV-C infection and response to antiretroviral therapy among HIV-infected subjects initiating HAART when antiretroviral-naive. A prospective, observational study of 400 HIV-infected patients with measurements of GBV-C RNA, hepatitis C virus (HCV) antibodies and HCV RNA determined from plasma stored prior to HAART initiation. Time to virological (achieving HIV RNA < or =500 copies/ml) and immunological success (a CD4+ count increase of > or =200 cells/microl), and the time to virological relapse (confirmed HIV RNA >500 copies/ml) were assessed by Kaplan-Meier methods and Cox proportional hazard regression model. Of the subjects, 117 (29.3%) were GBV-C positive and, overall, 351 (87.8%) patients achieved virological success. After controlling for a number of confounders including HCV RNA, GBV-C viraemic patients experienced a significantly lower risk of HIV rebound than those who were GBV-C negative relative hazard (RH)=0.56, 95% CI: 0.34-0.93, P=0.03. Conversely, the probability of achieving initial virological success or CD4+ count response after HAART did not differ between GBV-C-negative and -positive subjects. These results suggest that GBV-C coinfection may play a role in determining the rate of HIV rebound possibly by competing with HIV replication after HIV load has been successfully suppressed by HAART.
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