• European Marine Science
• Open Access close
• Aurora Universities Network close

eLife digest Our brains are made up of almost 100 billion brain cells. Each of them acts like a small chip: they collect, process and pass on information in the form of electrical signals. In brain areas that integrate different types of information, such as frontal and temporal lobes, brain cells have larger dendrites – long projections specialized to collect signals. Theoretical studies predict that larger dendrites help cells to initiate electrical signals faster. Because of difficulty in accessing human neurons, it has been unknown whether any of these features also relate to human intelligence. Previous studies have revealed that people with a higher IQ have a thicker outer layer (the cortex) in areas such as the frontal and temporal lobes. But does a thicker cortex also contain cells with larger dendrites and is their role different? To test whether smarter brains are equipped with faster and larger cells, Goriounova et al. studied 46 people who needed surgery for brain tumors or epilepsy. Each took an IQ test before the operation. To access the diseased tissue deep in the brain, the surgeon also removed small, undamaged samples of temporal lobe. These samples still contained living cells and their electrical signals were measured in the lab. The experiments showed that cells from people with a higher IQ had larger dendrites that transported information more quickly, especially when they are very active. Computer models were then used to understand how these findings can lead to more efficient information transfer in human neurons. Traditionally, research on human intelligence has focused on three main strategies: to study brain structure and function, to find genes associated with intelligence and to study the connection between our mind and behavior. Goriounova et al. are the first to take the single-cell perspective and link cell properties to human intelligence. The findings could help connect these separate approaches, and explain how genes for intelligence lead to thicker cortices and faster reaction times in people with higher IQ. It is generally assumed that human intelligence relies on efficient processing by neurons in our brain. Although grey matter thickness and activity of temporal and frontal cortical areas correlate with IQ scores, no direct evidence exists that links structural and physiological properties of neurons to human intelligence. Here, we find that high IQ scores and large temporal cortical thickness associate with larger, more complex dendrites of human pyramidal neurons. We show in silico that larger dendritic trees enable pyramidal neurons to track activity of synaptic inputs with higher temporal precision, due to fast action potential kinetics. Indeed, we find that human pyramidal neurons of individuals with higher IQ scores sustain fast action potential kinetics during repeated firing. These findings provide the first evidence that human intelligence is associated with neuronal complexity, action potential kinetics and efficient information transfer from inputs to output within cortical neurons.

Higher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs—referred to as the rich club—has been hypothesized to support task‐specific functional integration. In the present paper, we use a whole‐cortex model to estimate directed interactions between 68 cortical regions from functional magnetic resonance imaging activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state‐dependent input and output effective connectivity (EC) of the structural rich club and relate these to whole‐cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing EC during task performance as compared with rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task‐specific. Furthermore, we observe that whole‐cortex dynamics are faster during task as compared with rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing EC. Together our findings speak to a gating mechanism of the rich club that supports fast‐paced information exchange among relevant peripheral regions in a task‐specific and goal‐directed fashion, while constantly listening to the whole network. International audience

The propagation of an electron beam in the presence of transverse magnetic fields possessing integer topological charges is presented. The spin--magnetic interaction introduces a nonuniform spin precession of the electrons that gains a space-variant geometrical phase in the transverse plane proportional to the field's topological charge, whose handedness depends on the input electron's spin state. A combination of our proposed device with an electron orbital angular momentum sorter can be utilized as a spin-filter of electron beams in a mid-energy range. We examine these two different configurations of a partial spin-filter generator numerically. The results of these analysis could prove useful in the design of improved electron microscope. Comment: 7 pages, 7 figures

Los datos de las redes sociales brindan oportunidades sin precedentes para investigar los procesos que rigen la dinámica de los fenómenos sociales colectivos. Consideramos un enfoque teórico de la información para definir y medir las firmas temporales y estructurales típicas de los eventos sociales colectivos a medida que surgen y ganan importancia. Utilizamos el análisis de entropía de transferencia simbólica de series temporales de microblogging para extraer redes de influencia dirigidas entre subunidades geolocalizadas en los sistemas sociales. Esta metodología captura el surgimiento de dinámicas a nivel de sistema cercanas al inicio de fenómenos colectivos socialmente relevantes. El marco se valida con un análisis empírico detallado de cinco estudios de casos. En particular, identificamos un cambio en la característica escala de tiempo de la transferencia de información que marca el inicio de los fenómenos colectivos impulsados por la información. Además, nuestro enfoque identifica una transición orden-desorden en la red de influencia dirigida entre subunidades sociales. En ausencia de una conducción exógena clara, los fenómenos sociales colectivos pueden representarse como transiciones estructurales impulsadas endógenamente de la red de transferencia de información. Este estudio proporciona resultados que pueden ayudar a definir modelos y algoritmos predictivos para el análisis de eventos sociales basados en datos de fuente abierta. Les dades de les xarxes socials ofereixen oportunitats sense precedents per investigar els processos que regeixen la dinàmica dels fenòmens socials col·lectius. Considerem un enfocament teòric de la informació per definir i mesurar les firmes temporals i estructurals típiques dels esdeveniments socials col·lectius a mesura que sorgeixen i guanyen importància. Utilitzem l'anàlisi d'entropia de transferència simbòlica de sèries temporals de microblogging per extreure xarxes d'influència dirigides entre subunitats geolocalitzades en els sistemes socials. Aquesta metodologia captura el sorgiment de dinàmiques a nivell de sistema properes a l'inici de fenòmens col·lectius socialment rellevants. El marc es valida amb una anàlisi empírica detallada de cinc estudis de casos. En particular, identifiquem un canvi en la característica escala de temps de la transferència d'informació que marca l'inici dels fenòmens col·lectius impulsats per la informació. A més, el nostre enfocament identifica una transició ordre-desordre a la xarxa d'influència dirigida entre subunitats socials. En absència d'una conducció exògena clara, els fenòmens socials col·lectius poden representar-se com transicions estructurals impulsades endògenament de la xarxa de transferència d'informació. Aquest estudi proporciona resultats que poden ajudar a definir models i algoritmes predictius per a l'anàlisi d'esdeveniments socials basats en dades de font oberta. Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

Neuronal firing, synaptic transmission, and its plasticity form the building blocks for processing and storage of information in the brain. It is unknown whether adult human synapses are more efficient in transferring information between neurons than rodent synapses. To test this, we recorded from connected pairs of pyramidal neurons in acute brain slices of adult human and mouse temporal cortex and probed the dynamical properties of use-dependent plasticity. We found that human synaptic connections were purely depressing and that they recovered three to four times more swiftly from depression than synapses in rodent neocortex. Thereby, during realistic spike trains, the temporal resolution of synaptic information exchange in human synapses substantially surpasses that in mice. Using information theory, we calculate that information transfer between human pyramidal neurons exceeds that of mouse pyramidal neurons by four to nine times, well into the beta and gamma frequency range. In addition, we found that human principal cells tracked fine temporal features, conveyed in received synaptic inputs, at a wider bandwidth than for rodents. Action potential firing probability was reliably phase-locked to input transients up to 1,000 cycles/s because of a steep onset of action potentials in human pyramidal neurons during spike trains, unlike in rodent neurons. Our data show that, in contrast to the widely held views of limited information transfer in rodent depressing synapses, fast recovering synapses of human neurons can actually transfer substantial amounts of information during spike trains. In addition, human pyramidal neurons are equipped to encode high synaptic information content. Thus, adult human cortical microcircuits relay information at a wider bandwidth than rodent microcircuits. Author Summary Our ability to think, memorize information, and act appropriately depends on circuits of connected neurons in the brain. In these circuits, neurons pass information to each other using electric pulses (action potentials) that cause the release of chemical neurotransmitters, which alter the membrane electric potential of receiving neurons. Based on the inputs neurons receive, they decide whether to transmit action potentials to other neurons in the circuit to pass on information. During sequences of repeated information transfer, synaptic connections between two neurons temporarily become weaker by synaptic depression. Our knowledge of neuronal information transfer is based on rodent neurons. The properties of synaptic information transfer and synaptic depression in humans are not known. Here, we show that adult human neurons can transfer information with up to ten times higher rates than mouse neurons, because of a three to four times faster recovery from depression. Furthermore, we found that human neurons can respond faster to synaptic inputs, owing to faster initiation of action potentials. Human neurons can thereby reliably encode high input frequencies in their output. Thus, neuronal information transfer can have a substantially higher bandwidth in human neocortical circuits than in rodent brains. Because of fast recovery from synaptic depression and fast-initiated action potentials, neuronal information transfer can have a substantially higher bandwidth in human neocortical circuits than in those of rodents.