Multi-region calcium imaging in freely behaving mice with ultra-compact head-mounted fluorescence microscopes.

To investigate the circuit-level neural mechanisms of behavior, simultaneous imaging of neuronal activity in multiple cortical and subcortical regions is highly desired. Miniature head-mounted microscopes offer the capability of calcium imaging in freely behaving animals. However, implanting multiple microscopes on a mouse brain remains challenging due to space constraints and the cumbersome weight of the equipment. Here, we present TINIscope, a Tightly Integrated Neuronal Imaging microscope optimized for electronic and opto-mechanical design. With its compact and lightweight design of 0.43 g, TINIscope enables unprecedented simultaneous imaging of behavior-relevant activity in up to four brain regions in mice. Proof-of-concept experiments with TINIscope recorded over 1000 neurons in four hippocampal subregions and revealed concurrent activity patterns spanning across these regions. Moreover, we explored potential multi-modal experimental designs by integrating additional modules for optogenetics, electrical stimulation or local field potential recordings. Overall, TINIscope represents a timely and indispensable tool for studying the brain-wide interregional coordination that underlies unrestrained behaviors.

Insights into the evolutionary history and taxonomic status of Sinopteris (Pteridaceae).

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.

The complete chloroplast genome sequence of Ranunculus pekinensis (L. Liou) Luferov (Ranunculaceae), a species endemic to China.

Ranunculus pekinensis (L. Liou) Luferov 1997, a perennial aquatic herb, is endemic to Beijing, China and has high water quality requirements. Because its habitat is under great threat and its population is declining, it is now listed as a national protected plant in China. To provide genomic resources for future research of this endangered species, the complete chloroplast genome sequence of R. pekinensis was assembled and annotated for the first time. The complete chloroplast genome sequence was 156,139 bp in length, containing a large single copy region (LSC) of 85,430 bp and a small single copy region (SSC) of 19,970 bp, which were separated by a pair of 25,367 bp inverted repeat regions (IRs). The complete chloroplast sequence contained 112 unique genes, including 30 tRNA, 4 rRNA, and 78 protein-coding genes. The overall guanine-cytosine (GC) content of the chloroplast genome was 37.8%, and the GC contents of the LSC, SSC, and IR regions were 36.0%, 31.3%, and 43.5%, respectively. Phylogenetic analysis with the reported chloroplast sequences showed that R. pekinensis was closely related to R. bungei Steud. 1841, both of which belonged to Ranunculus Sect. Batrachium DC. 1817. These data will provide essential resources regarding the evolution and conservation of R. pekinensis.

The complete chloroplast genome of a distinctive fern, Coniogramme intermedia Hieron. (Pteridaceae).

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 laminae abaxially hairy. It is mainly distributed in the tropical and subtropical regions of Asia. Herein, we report the first complete chloroplast genome sequence of C. intermedia. Also, it is the opening one of the genus Coniogramme Fée. The chloroplast genome sequence is 153,561 bp in length. The genome has a typical quadripartite structure, including a large single-copy (LSC) region of 82,817 bp, a small single-copy (SSC) region of 21,236 bp, and two inverted repeat (IR) regions of 24,754bp each. The total GC content is 45.0%. The complete plastome sequence contains 114 genes, including, 81 protein-coding, 29 tRNA, and four rRNA genes. The phylogenetic analysis of Pteridaceae based on the complete chloroplast genomes was also presented in this study.

Noise in Neurons and Synapses Enables Reliable Associative Memory Storage in Local Cortical Circuits.

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 (PSPs). 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 trade-off 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.

Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.

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.

Two new records of the fern genus Coniogramme (Pteridaceae) from Vietnam.

Two new records of the fern genus Coniogramme Fée from Vietnam, C.japonica and C.procera, are presented. In addition, a key to recognising the species of Coniogramme in Vietnam is given in this paper.

Circuit motifs for contrast-adaptive differentiation in early sensory systems: the role of presynaptic inhibition and short-term plasticity.

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.

Mapping brain injury with symmetrical-channels' EEG signal analysis--a pilot study.

A technique for detecting brain injury at the bedside has great clinical value, but conventional imaging techniques (such as computed tomography [CT] and magnetic resonance imaging) are impractical. In this study, a novel method-the symmetrical channel electroencephalogram (EEG) signal analysis-was developed for this purpose. The study population consisted of 45 traumatic brain injury patients and 10 healthy controls. EEG signals in resting and stimulus states were acquired, and approximate entropy (ApEn) and slow-wave coefficient were extracted to calculate the ratio values of ApEn and SWC for injured and uninjured areas. Statistical analyses showed that the ratio values for both ApEn and SWC between injured and uninjured brain areas differed significantly (P<0.05) for both resting and name call stimulus states. A set of criteria (range of ratio values) to determine whether a brain area is injured or uninjured was proposed and its reliability was verified by statistical analyses and CT images.

Design principles of the sparse coding network and the role of "sister cells" in the olfactory system of Drosophila.

Sensory systems face the challenge to represent sensory inputs in a way to allow easy readout of sensory information by higher brain areas. In the olfactory system of the fly drosopohila melanogaster, projection neurons (PNs) of the antennal lobe (AL) convert a dense activation of glomeruli into a sparse, high-dimensional firing pattern of Kenyon cells (KCs) in the mushroom body (MB). Here we investigate the design principles of the olfactory system of drosophila in regard to the capabilities to discriminate odor quality from the MB representation and its robustness to different types of noise. We focus on understanding the role of highly correlated homotypic projection neurons ("sister cells") found in the glomeruli of flies. These cells are coupled by gap-junctions and receive almost identical sensory inputs, but target randomly different KCs in MB. We show that sister cells might play a crucial role in increasing the robustness of the MB odor representation to noise. Computationally, sister cells thus might help the system to improve the generalization capabilities in face of noise without impairing the discriminability of odor quality at the same time.

Nonlinear multiplicative dendritic integration in neuron and network models.

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 non-linear 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 non-linear 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 non-linear 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.

Adaptive neural information processing with dynamical electrical synapses.

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.

Concentration-invariant odor representation in the olfactory system by presynaptic inhibition.

The present study investigates a network model for implementing concentration-invariant representation for odors in the olfactory system. The network consists of olfactory receptor neurons, projection neurons, and inhibitory local neurons. Receptor neurons send excitatory inputs to projection neurons, which are modulated by the inhibitory inputs from local neurons. The modulation occurs at the presynaptic site from a receptor neuron to a projection one, leading to the operation of divisive normalization. The responses of local interneurons are determined by the total activities of olfactory receptor neurons. We find that with a proper parameter condition, the responses of projection neurons become effectively independent of the odor concentration. Simulation results confirm our theoretical analysis.

Impact of noise structure and network topology on tracking speed of neural networks.

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.