Studies on conjugation of Spirogyra using monoclonal culture

We succeeded in inducing conjugation of Spirogyracastanacea by incubating algal filaments on agar plate. Conjugation could be induced using clone culture. The scalariform conjugation was generally observed, while lateral conjugation was rarely. When two filaments formed scalariform conjugation, all cells of one filament behaved as male and those of other filament did as female. Very rarely, however, zygospores were formed in both of pair filaments. The surface of conjugation tube was stained with fluorescently labeled-lectins, such as Bandeiraea (Griffonia) simplicifolia lectin (BSL-I) and jacalin. BSL-I strongly stained the conjugation tubes, while weakly did the cell surface of female gamete first and then that of male gamete. Jacalin stained mainly the conjugation tubes. Addition of jacalin inhibited the formation of papilla, suggesting some important role of jacalin-binding material at the initial step of formation of the conjugation tubes

Design for a Darwinian Brain: Part 1. Philosophy and Neuroscience

Physical symbol systems are needed for open-ended cognition. A good way to understand physical symbol systems is by comparison of thought to chemistry. Both have systematicity, productivity and compositionality. The state of the art in cognitive architectures for open-ended cognition is critically assessed. I conclude that a cognitive architecture that evolves symbol structures in the brain is a promising candidate to explain open-ended cognition. Part 2 of the paper presents such a cognitive architecture.Comment: Darwinian Neurodynamics. Submitted as a two part paper to Living Machines 2013 Natural History Museum, Londo

Theory of Interaction of Memory Patterns in Layered Associative Networks

A synfire chain is a network that can generate repeated spike patterns with millisecond precision. Although synfire chains with only one activity propagation mode have been intensively analyzed with several neuron models, those with several stable propagation modes have not been thoroughly investigated. By using the leaky integrate-and-fire neuron model, we constructed a layered associative network embedded with memory patterns. We analyzed the network dynamics with the Fokker-Planck equation. First, we addressed the stability of one memory pattern as a propagating spike volley. We showed that memory patterns propagate as pulse packets. Second, we investigated the activity when we activated two different memory patterns. Simultaneous activation of two memory patterns with the same strength led the propagating pattern to a mixed state. In contrast, when the activations had different strengths, the pulse packet converged to a two-peak state. Finally, we studied the effect of the preceding pulse packet on the following pulse packet. The following pulse packet was modified from its original activated memory pattern, and it converged to a two-peak state, mixed state or non-spike state depending on the time interval

Sparse and Dense Encoding in Layered Associative Network of Spiking Neurons

A synfire chain is a simple neural network model which can propagate stable synchronous spikes called a pulse packet and widely researched. However how synfire chains coexist in one network remains to be elucidated. We have studied the activity of a layered associative network of Leaky Integrate-and-Fire neurons in which connection we embed memory patterns by the Hebbian Learning. We analyzed their activity by the Fokker-Planck method. In our previous report, when a half of neurons belongs to each memory pattern (memory pattern rate $F=0.5$), the temporal profiles of the network activity is split into temporally clustered groups called sublattices under certain input conditions. In this study, we show that when the network is sparsely connected ($F<0.5$), synchronous firings of the memory pattern are promoted. On the contrary, the densely connected network ($F>0.5$) inhibit synchronous firings. The sparseness and denseness also effect the basin of attraction and the storage capacity of the embedded memory patterns. We show that the sparsely(densely) connected networks enlarge(shrink) the basion of attraction and increase(decrease) the storage capacity

Universal features of correlated bursty behaviour

Inhomogeneous temporal processes, like those appearing in human communications, neuron spike trains, and seismic signals, consist of high-activity bursty intervals alternating with long low-activity periods. In recent studies such bursty behavior has been characterized by a fat-tailed inter-event time distribution, while temporal correlations were measured by the autocorrelation function. However, these characteristic functions are not capable to fully characterize temporally correlated heterogenous behavior. Here we show that the distribution of the number of events in a bursty period serves as a good indicator of the dependencies, leading to the universal observation of power-law distribution in a broad class of phenomena. We find that the correlations in these quite different systems can be commonly interpreted by memory effects and described by a simple phenomenological model, which displays temporal behavior qualitatively similar to that in real systems

Statistical Significance of Precisely Repeated Intracellular Synaptic Patterns

Can neuronal networks produce patterns of activity with millisecond accuracy? It may seem unlikely, considering the probabilistic nature of synaptic transmission. However, some theories of brain function predict that such precision is feasible and can emerge from the non-linearity of the action potential generation in circuits of connected neurons. Several studies have presented evidence for and against this hypothesis. Our earlier work supported the precision hypothesis, based on results demonstrating that precise patterns of synaptic inputs could be found in intracellular recordings from neurons in brain slices and in vivo. To test this hypothesis, we devised a method for finding precise repeats of activity and compared repeats found in the data to those found in surrogate datasets made by shuffling the original data. Because more repeats were found in the original data than in the surrogate data sets, we argued that repeats were not due to chance occurrence. Mokeichev et al. (2007) challenged these conclusions, arguing that the generation of surrogate data was insufficiently rigorous. We have now reanalyzed our previous data with the methods introduced from Mokeichev et al. (2007). Our reanalysis reveals that repeats are statistically significant, thus supporting our earlier conclusions, while also supporting many conclusions that Mokeichev et al. (2007) drew from their recent in vivo recordings. Moreover, we also show that the conditions under which the membrane potential is recorded contributes significantly to the ability to detect repeats and may explain conflicting results. In conclusion, our reevaluation resolves the methodological contradictions between Ikegaya et al. (2004) and Mokeichev et al. (2007), but demonstrates the validity of our previous conclusion that spontaneous network activity is non-randomly organized

β-Amyloid 1-42 Oligomers Impair Function of Human Embryonic Stem Cell-Derived Forebrain Cholinergic Neurons

Cognitive impairment in Alzheimer's disease (AD) patients is associated with a decline in the levels of growth factors, impairment of axonal transport and marked degeneration of basal forebrain cholinergic neurons (BFCNs). Neurogenesis persists in the adult human brain, and the stimulation of regenerative processes in the CNS is an attractive prospect for neuroreplacement therapy in neurodegenerative diseases such as AD. Currently, it is still not clear how the pathophysiological environment in the AD brain affects stem cell biology. Previous studies investigating the effects of the β-amyloid (Aβ) peptide on neurogenesis have been inconclusive, since both neurogenic and neurotoxic effects on progenitor cell populations have been reported. In this study, we treated pluripotent human embryonic stem (hES) cells with nerve growth factor (NGF) as well as with fibrillar and oligomeric Aβ1-40 and Aβ1-42 (nM-µM concentrations) and thereafter studied the differentiation in vitro during 28-35 days. The process applied real time quantitative PCR, immunocytochemistry as well as functional studies of intracellular calcium signaling. Treatment with NGF promoted the differentiation into functionally mature BFCNs. In comparison to untreated cells, oligomeric Aβ1–40 increased the number of functional neurons, whereas oligomeric Aβ1–42 suppressed the number of functional neurons. Interestingly, oligomeric Aβ exposure did not influence the number of hES cell-derived neurons compared with untreated cells, while in contrast fibrillar Aβ1–40 and Aβ1–42 induced gliogenesis. These findings indicate that Aβ1–42 oligomers may impair the function of stem cell-derived neurons. We propose that it may be possible for future AD therapies to promote the maturation of functional stem cell-derived neurons by altering the brain microenvironment with trophic support and by targeting different aggregation forms of Aβ

Principal component analysis of ensemble recordings reveals cell assemblies at high temporal resolution

Simultaneous recordings of many single neurons reveals unique insights into network processing spanning the timescale from single spikes to global oscillations. Neurons dynamically self-organize in subgroups of coactivated elements referred to as cell assemblies. Furthermore, these cell assemblies are reactivated, or replayed, preferentially during subsequent rest or sleep episodes, a proposed mechanism for memory trace consolidation. Here we employ Principal Component Analysis to isolate such patterns of neural activity. In addition, a measure is developed to quantify the similarity of instantaneous activity with a template pattern, and we derive theoretical distributions for the null hypothesis of no correlation between spike trains, allowing one to evaluate the statistical significance of instantaneous coactivations. Hence, when applied in an epoch different from the one where the patterns were identified, (e.g. subsequent sleep) this measure allows to identify times and intensities of reactivation. The distribution of this measure provides information on the dynamics of reactivation events: in sleep these occur as transients rather than as a continuous process