Automated whole-cell patch-clamp electrophysiology of neurons in vivo

Whole-cell patch-clamp electrophysiology of neurons is a gold-standard technique for high-fidelity analysis of the biophysical mechanisms of neural computation and pathology, but it requires great skill to perform. We have developed a robot that automatically performs patch clamping in vivo, algorithmically detecting cells by analyzing the temporal sequence of electrode impedance changes. We demonstrate good yield, throughput and quality of automated intracellular recording in mouse cortex and hippocampus.National Institutes of Health (U.S.) (NIH EUREKA Award program (1R01NS075421))National Institutes of Health (U.S.) ((NIH) Director′s New Innovator Award (DP2OD002002)National Science Foundation (U.S.) ((NSF) CAREER award (CBET 1053233))New York Stem Cell Foundation (Robertson Neuroscience Award)Dr. Gerald Burnett and Marjorie BurnettNational Science Foundation (U.S.) (grant CISE 1110947)National Science Foundation (U.S.) (grant EHR 0965945)American Heart Association (10GRNT4430029

Computers from plants we never made. Speculations

We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analog electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots' network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, $\alpha$-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing inspired by physics, chemistry and biology. Essays presented to Julian Miller on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew Adamatzky (Springer, 2017

Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

A Fokker-Planck formalism for diffusion with finite increments and absorbing boundaries

Gaussian white noise is frequently used to model fluctuations in physical systems. In Fokker-Planck theory, this leads to a vanishing probability density near the absorbing boundary of threshold models. Here we derive the boundary condition for the stationary density of a first-order stochastic differential equation for additive finite-grained Poisson noise and show that the response properties of threshold units are qualitatively altered. Applied to the integrate-and-fire neuron model, the response turns out to be instantaneous rather than exhibiting low-pass characteristics, highly non-linear, and asymmetric for excitation and inhibition. The novel mechanism is exhibited on the network level and is a generic property of pulse-coupled systems of threshold units.Comment: Consists of two parts: main article (3 figures) plus supplementary text (3 extra figures

A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences

Stimulus-specific adaptation (SSA) occurs when the spike rate of a neuron decreases with repetitions of the same stimulus, but recovers when a different stimulus is presented. It has been suggested that SSA in single auditory neurons may provide information to change detection mechanisms evident at other scales (e.g., mismatch negativity in the event related potential), and participate in the control of attention and the formation of auditory streams. This article presents a spiking-neuron model that accounts for SSA in terms of the convergence of depressing synapses that convey feature-specific inputs. The model is anatomically plausible, comprising just a few homogeneously connected populations, and does not require organised feature maps. The model is calibrated to match the SSA measured in the cortex of the awake rat, as reported in one study. The effect of frequency separation, deviant probability, repetition rate and duration upon SSA are investigated. With the same parameter set, the model generates responses consistent with a wide range of published data obtained in other auditory regions using other stimulus configurations, such as block, sequential and random stimuli. A new stimulus paradigm is introduced, which generalises the oddball concept to Markov chains, allowing the experimenter to vary the tone probabilities and the rate of switching independently. The model predicts greater SSA for higher rates of switching. Finally, the issue of whether rarity or novelty elicits SSA is addressed by comparing the responses of the model to deviants in the context of a sequence of a single standard or many standards. The results support the view that synaptic adaptation alone can explain almost all aspects of SSA reported to date, including its purported novelty component, and that non-trivial networks of depressing synapses can intensify this novelty response

Topoisomerase II-Mediated DNA Damage Is Differently Repaired during the Cell Cycle by Non-Homologous End Joining and Homologous Recombination

Topoisomerase II (Top2) is a nuclear enzyme involved in several metabolic processes of DNA. Chemotherapy agents that poison Top2 are known to induce persistent protein-mediated DNA double strand breaks (DSB). In this report, by using knock down experiments, we demonstrated that Top2α was largely responsible for the induction of γH2AX and cytotoxicity by the Top2 poisons idarubicin and etoposide in normal human cells. As DSB resulting from Top2 poisons-mediated damage may be repaired by non-homologous end joining (NHEJ) or homologous recombination (HR), we aimed to analyze both DNA repair pathways. We found that DNA-PKcs was rapidly activated in human cells, as evidenced by autophosphorylation at serine 2056, following Top2-mediated DNA damage. The chemical inhibition of DNA-PKcs by wortmannin and vanillin resulted in an increased accumulation of DNA DSB, as evaluated by the comet assay. This was supported by a hypersensitive phenotype to Top2 poisons of Ku80- and DNA-PKcs- defective Chinese hamster cell lines. We also showed that Rad51 protein levels, Rad51 foci formation and sister chromatid exchanges were increased in human cells following Top2-mediated DNA damage. In support, BRCA2- and Rad51C- defective Chinese hamster cells displayed hypersensitivity to Top2 poisons. The analysis by immunofluorescence of the DNA DSB repair response in synchronized human cell cultures revealed activation of DNA-PKcs throughout the cell cycle and Rad51 foci formation in S and late S/G2 cells. Additionally, we found an increase of DNA-PKcs-mediated residual repair events, but not Rad51 residual foci, into micronucleated and apoptotic cells. Therefore, we conclude that in human cells both NHEJ and HR are required, with cell cycle stage specificity, for the repair of Top2-mediated reversible DNA damage. Moreover, NHEJ-mediated residual repair events are more frequently associated to irreversibly damaged cells

Long-term modification of cortical synapses improves sensory perception

Synapses and receptive fields of the cerebral cortex are plastic. However, changes to specific inputs must be coordinated within neural networks to ensure that excitability and feature selectivity are appropriately configured for perception of the sensory environment. Long-lasting enhancements and decrements to rat primary auditory cortical excitatory synaptic strength were induced by pairing acoustic stimuli with activation of the nucleus basalis neuromodulatory system. Here we report that these synaptic modifications were approximately balanced across individual receptive fields, conserving mean excitation while reducing overall response variability. Decreased response variability should increase detection and recognition of near-threshold or previously imperceptible stimuli, as we found in behaving animals. Thus, modification of cortical inputs leads to wide-scale synaptic changes, which are related to improved sensory perception and enhanced behavioral performance

Noise Suppression and Surplus Synchrony by Coincidence Detection

The functional significance of correlations between action potentials of neurons is still a matter of vivid debates. In particular it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex. The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony, preventing a thorough analysis. Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to closely time-locked spiking activity of pairs of neurons. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling, allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents. We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons, so that the same input covariance can be realized by common inputs or by spiking synchrony. We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient, which is typically smaller than unity, but increases sensitively even for weakly synchronous inputs. In the limit of high afferent correlation, in the presence of synchrony a qualitatively new picture arises. As the non-linear neuronal response becomes dominant, the output correlation becomes higher than the total correlation in the input. This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise, elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks

Forward Masking Estimated by Signal Detection Theory Analysis of Neuronal Responses in Primary Auditory Cortex

Psychophysical forward masking is an increase in threshold of detection of a sound (probe) when it is preceded by another sound (masker). This is reminiscent of the reduction in neuronal responses to a sound following prior stimulation. Studies in the auditory nerve and cochlear nucleus using signal detection theory techniques to derive neuronal thresholds showed that in centrally projecting neurons, increases in masked thresholds were significantly smaller than the changes measured psychophysically. Larger threshold shifts have been reported in the inferior colliculus of awake marmoset. The present study investigated the magnitude of forward masking in primary auditory cortical neurons of anaesthetised guinea-pigs. Responses of cortical neurons to unmasked and forward masked tones were measured and probe detection thresholds estimated using signal detection theory methods. Threshold shifts were larger than in the auditory nerve, cochlear nucleus and inferior colliculus. The larger threshold shifts suggest that central, and probably cortical, processes contribute to forward masking. However, although methodological differences make comparisons difficult, the threshold shifts in cortical neurons were, in contrast to subcortical nuclei, actually larger than those observed psychophysically. Masking was largely attributable to a reduction in the responses to the probe, rather than either a persistence of the masker responses or an increase in the variability of probe responses

Memory in Microbes: Quantifying History-Dependent Behavior in a Bacterium

Memory is usually associated with higher organisms rather than bacteria. However, evidence is mounting that many regulatory networks within bacteria are capable of complex dynamics and multi-stable behaviors that have been linked to memory in other systems. Moreover, it is recognized that bacteria that have experienced different environmental histories may respond differently to current conditions. These “memory” effects may be more than incidental to the regulatory mechanisms controlling acclimation or to the status of the metabolic stores. Rather, they may be regulated by the cell and confer fitness to the organism in the evolutionary game it participates in. Here, we propose that history-dependent behavior is a potentially important manifestation of memory, worth classifying and quantifying. To this end, we develop an information-theory based conceptual framework for measuring both the persistence of memory in microbes and the amount of information about the past encoded in history-dependent dynamics. This method produces a phenomenological measure of cellular memory without regard to the specific cellular mechanisms encoding it. We then apply this framework to a strain of Bacillus subtilis engineered to report on commitment to sporulation and degradative enzyme (AprE) synthesis and estimate the capacity of these systems and growth dynamics to ‘remember’ 10 distinct cell histories prior to application of a common stressor. The analysis suggests that B. subtilis remembers, both in short and long term, aspects of its cell history, and that this memory is distributed differently among the observables. While this study does not examine the mechanistic bases for memory, it presents a framework for quantifying memory in cellular behaviors and is thus a starting point for studying new questions about cellular regulation and evolutionary strategy