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As an endemic Chinese genus, Sinopteris C. Chr.Ching was once considered an early diverged taxon of cheilanthoid ferns, and its taxonomic status has long been controversial. In this study, eight datasets spanning the complete chloroplast genomes and three nuclear genes were used to reconstruct the phylogeny of Sinopteris and its relatives. In addition, combining morphological analyses, divergence time estimation, and ancestral trait reconstruction, the origin and evolutionary history of Sinopteris were comprehensively discussed. Based on the complete chloroplast genome dataset, our analyses yielded a phylogram with all clades strongly supported (ML-BS = 100, BI-PP = 1.0), and the topology was almost identical to that based on the concatenated sequences of nrDNA, CRY2, and IBR3. Two species of Sinopteris were united and sister to Aleuritopteris niphobola (C. Chr.) Ching. They constituted a stable monophyletic group embedded in Aleuritopteris Fée. This was also consistent with the results of morphological analyses. Divergence time estimation indicated that the clade of Aleuritopteris and Sinopteris originated in the early Miocene (ca. 16.80 Ma) and experienced two rapid diversifications, which could coincide with environmental heterogeneity caused by the progressive uplift of the Himalayas and the intense uplift of the Hengduan Mountains. Sinopteris originated in the late Miocene (ca. 6.96 Ma), accompanied by the sharp intensifications of Asian Monsoon, and began to diversify at 2.34 Ma, following the intense uplift of the Hengduan Mountains. Ancestral character reconstruction showed that monangial sori and subsessile sporangia were clearly late derived states rather than early diverged states. Both the molecular phylogenetic and morphological analyses support the inclusion of Sinopteris in Aleuritopteris.

Coniogramme ser. Coniogramme is a perennial herbaceous fern group of the subfamily Cryptogrammoideae in Pteridaceae. The phylogenetic reconstruction of this series was performed using five chloroplast markers (matK/psbA-trnH/rbcL/rps4-trnS/trnL-F). The sporangium annulus cell numbers of eight species have also been counted and statistically assessed. The results showed that the present defined C. ser. Coniogramme is not a monophyletic group. Coniogramme merrillii, which has the highest number of annulus cells (median 18), is sister to the other species of this genus, and the number of sporangium annulus cells is stable at the species level and of great phylogenetic significance. It is not supported by both molecular and morphological evidence to synonymize C. merrillii, C. petelotii and C. gigantea in C. fraxinea. C. merrillii should be treated as a synonym of C. macrophylla, while C. lanceolata be accepted as a distinct species.

The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from associative learning. To test this hypothesis, we trained recurrent networks of excitatory and inhibitory neurons on memories composed of varying numbers of associations and compared the resulting network properties with those observed experimentally. We show that, when the network is robustly loaded with near-maximum amount of associations it can support, it develops properties that are consistent with the observed probabilities of excitatory and inhibitory connections, shapes of connection weight distributions, overexpression of specific 2- and 3-neuron motifs, distributions of connection numbers in clusters of 3–8 neurons, sustained, irregular, and asynchronous firing activity, and balance of excitation and inhibition. In addition, memories loaded into the network can be retrieved, even in the presence of noise that is comparable with the baseline variations in the postsynaptic potential. The confluence of these results suggests that many structural and dynamical properties of local cortical networks are simply a byproduct of associative learning. We predict that overexpression of excitatory–excitatory bidirectional connections observed in many cortical systems must be accompanied with underexpression of bidirectionally connected inhibitory–excitatory neuron pairs. SIGNIFICANCE STATEMENT Many structural and dynamical properties of local cortical networks are ubiquitously present across areas and species. Because synaptic connectivity is shaped by experience, we wondered whether continual learning, rather than genetic control, is responsible for producing such features. To answer this question, we developed a biologically constrained recurrent network of excitatory and inhibitory neurons capable of learning predefined sequences of network states. Embedding such associative memories into the network revealed that, when individual neurons are robustly loaded with a near-maximum amount of memories they can support, the network develops many properties that are consistent with experimental observations. Our findings suggest that basic structural and dynamical properties of local networks in the brain are simply a byproduct of learning and memory storage.

Coniogramme intermedia Hieron. is a morphologically distinctive species in the genus. It is identified by lanceolate pinnules with serrated margins, free veins, hydathodes extending into teeth, and...

Neural networks in the brain can function reliably despite various sources of errors and noise present at every step of signal transmission. These sources include errors in the presynaptic inputs to the neurons, noise in synaptic transmission, and fluctuations in the neurons’ postsynaptic potentials. Collectively they lead to errors in the neurons’ outputs which are, in turn, injected into the network. Does unreliable network activity hinder fundamental functions of the brain, such as learning and memory retrieval? To explore this question, this article examines the effects of errors and noise on the properties of model networks of inhibitory and excitatory neurons involved in associative sequence learning. The associative learning problem is solved analytically and numerically, and it is also shown how memory sequences can be loaded into the network with a biologically more plausible perceptron-type learning rule. Interestingly, the results reveal that errors and noise during learning increase the probability of memory recall. There is a tradeoff between the capacity and reliability of stored memories, and, noise during learning is required for optimal retrieval of stored information. What is more, networks loaded with associative memories to capacity display many structural and dynamical features observed in local cortical circuits in mammals. Based on the similarities between the associative and cortical networks, this article predicts that connections originating from more unreliable neurons or neuron classes in the cortex are more likely to be depressed or eliminated during learning, while connections onto noisier neurons or neuron classes have lower probabilities and higher weights. SIGNIFICANCE STATEMENT Signal transmission in the brain is accompanied by many sources of errors and noise, and yet, neural networks can reliably store memories. This article argues that noise should not be viewed as a nuisance, but that it is an essential component of the reliable learning mechanism implemented by the brain. The article describes a network model of associative sequence learning, showing that for optimal retrieval of stored information learning must be carried out in the presence of noise. To validate the model, it is shown that associative memories can be loaded into the network with an online perceptron-type learning rule and that networks loaded to capacity develop many structural and dynamical properties observed in the brain.

The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from associative learning. To test this hypothesis, we trained recurrent networks of excitatory and inhibitory neurons on memory sequences of varying lengths and compared network properties to those observed experimentally. We show that when the network is robustly loaded with near-maximum amount of associations it can support, it develops properties that are consistent with the observed probabilities of excitatory and inhibitory connections, shapes of connection weight distributions, overrepresentations of specific 3-neuron motifs, distributions of connection numbers in clusters of 3–8 neurons, sustained, irregular, and asynchronous firing activity, and balance of excitation and inhibition. What is more, memories loaded into the network can be retrieved even in the presence of noise comparable to the baseline variations in the postsynaptic potential. Confluence of these results suggests that many structural and dynamical properties of local cortical networks are simply a byproduct of associative learning.

Understanding why neural systems can process information extremely fast is a fundamental question in theoretical neuroscience. The present study investigates the effect of noise on accelerating neural computation. To evaluate the speed of network response, we consider a computational task in which the network tracks time-varying stimuli. Two noise structures are compared, namely, the stimulus-dependent and stimulus-independent noises. Based on a simple linear integrate-and-fire model, we theoretically analyze the network dynamics, and find that the stimulus-dependent noise, whose variance is proportional to the mean of external inputs, has better effect on speeding up network computation. This is due to two good properties in the transient network dynamics: (1) the instant firing rate of the network is proportional to the mean of external inputs, and (2) the stationary state of the network is robust to stimulus changes. We investigate two network models with varying recurrent interactions, and find that recurrent interactions tend to slow down the tracking speed of the network. When the biologically plausible Hodgkin–Huxley model is considered, we also observe that the stimulus-dependent noise accelerates neural computation, although the improvement is smaller than that in the case of linear integrate-and-fire model.

In natural signals, such as the luminance value across of a visual scene, abrupt changes in intensity value are often more relevant to an organism than intensity values at other positions and times. Thus to reduce redundancy, sensory systems are specialized to detect the times and amplitudes of informative abrupt changes in the input stream rather than coding the intensity values at all times. In theory, a system that responds transiently to fast changes is called a differentiator. In principle, several different neural circuit mechanisms exist that are capable of responding transiently to abrupt input changes. However, it is unclear which circuit would be best suited for early sensory systems, where the dynamic range of the natural input signals can be very wide. We here compare the properties of different simple neural circuit motifs for implementing signal differentiation. We found that a circuit motif based on presynaptic inhibition (PI) is unique in a sense that the vesicle resources in the presynaptic site can be stably maintained over a wide range of stimulus intensities, making PI a biophysically plausible mechanism to implement a differentiator with a very wide dynamical range. Moreover, by additionally considering short-term plasticity (STP), differentiation becomes contrast adaptive in the PI-circuit but not in other potential neural circuit motifs. Numerical simulations show that the behavior of the adaptive PI-circuit is consistent with experimental observations suggesting that adaptive presynaptic inhibition might be a good candidate neural mechanism to achieve differentiation in early sensory systems.

The present study investigates a potential computational role of dynamical electrical synapses in neural information process. Compared with chemical synapses, electrical synapses are more efficient in modulating the concerted activity of neurons. Based on the experimental data, we propose a phenomenological model for short-term facilitation of electrical synapses. The model satisfactorily reproduces the phenomenon that the neuronal correlation increases although the neuronal firing rates attenuate during the luminance adaptation. We explore how the stimulus information is encoded in parallel by firing rates and correlated activity of neurons, and find that dynamical electrical synapses mediate a transition from the firing rate code to the correlation one during the luminance adaptation. The latter encodes the stimulus information by using the concerted, but lower neuronal firing rate, and hence is economically more efficient.

Neurons receive inputs from thousands of synapses distributed across dendritic trees of complex morphology. It is known that dendritic integration of excitatory and inhibitory synapses can be highly nonlinear in reality and can heavily depend on the exact location and spatial arrangement of inhibitory and excitatory synapses on the dendrite. Despite this known fact, most neuron models used in artificial neural networks today still only describe the voltage potential of a single somatic compartment and assume a simple linear summation of all individual synaptic inputs. We here suggest a new biophysical motivated derivation of a single compartment model that integrates the nonlinear effects of shunting inhibition, where an inhibitory input on the route of an excitatory input to the soma cancels or ``shunts'' the excitatory potential. In particular, our integration of nonlinear dendritic processing into the neuron model follows a simple multiplicative rule, suggested recently by experiments, and allows for strict mathematical treatment of network effects. Using our new formulation, we further devised a spiking network model where inhibitory neurons act as global shunting gates, and show that the network exhibits persistent activity in a low firing regime.

Neural information processing is extremely complicated. A core challenge in theoretical neuroscience is to develop properly simplified models, which, on one hand, capture the fundamental features of the complex systems, and on the other hand, allow us to pursue analytic treatments. In the present study, we aim to develop simple models for synaptic information integration. We use simple current-based models to approximate the dynamics of conductance-based multi-compartment ones. The nonlinear shunting inhibition is expressed as a product between the contributions of excitatory and inhibitory currents, and its strength depends on the spatial configuration of excitatory and inhibitory inputs, agreeing with the experimental data. We expect that the current study will serve as a building brick for analyzing the dynamics of large-size networks.