This paper provides a global picture of the bifurcation scenario of the Hindmarsh–Rose model. A combination between simulations and numerical continuations is used to unfold the complex bifurcation structure. The bifurcation analysis is carried out by varying two bifurcation parameters and evidence is given that the structure that is found is universal and appears for all combinations of bifurcation parameters. The information about the organizing principles and bifurcation diagrams are then used to compare the dynamics of the model with that of a piecewise-linear approximation, customized for circuit implementation. A good match between the dynamical behaviors of the models is found. These results can be used both to design a circuit implementation of the Hindmarsh–Rose model mimicking the diversity of neural response and as guidelines to predict the behavior of the model as well as its circuit implementation as a function of parameters.

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. 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.

To support cognitive function, the CA3 region of the hippocampus performs computations involving attractor dynamics. Understanding how cellular and ensemble activities of CA3 neurons enable computation is critical for elucidating the neural correlates of cognition. Here we show that CA3 comprises not only classically described pyramid cells with thorny excrescences, but also includes previously unidentified ‘athorny’ pyramid cells that lack mossy-fiber input. Moreover, the two neuron types have distinct morphological and physiological phenotypes and are differentially modulated by acetylcholine. To understand the contribution of these athorny pyramid neurons to circuit function, we measured cell-type-specific firing patterns during sharp-wave synchronization events in vivo and recapitulated these dynamics with an attractor network model comprising two principal cell types. Our data and simulations reveal a key role for athorny cell bursting in the initiation of sharp waves: transient network attractor states that signify the execution of pattern completion computations vital to cognitive function.

The transplantation of pluripotent stem-cell-derived neurons constitutes a promising avenue for the treatment of several brain diseases. However, their potential for the repair of the cerebral cortex remains unclear, given its complexity and neuronal diversity. Here, we show that human visual cortical cells differentiated from embryonic stem cells can be transplanted and can integrate successfully into the lesioned mouse adult visual cortex. The transplanted human neurons expressed the appropriate repertoire of markers of six cortical layers, projected axons to specific visual cortical targets, and were synaptically active within the adult brain. Moreover, transplant maturation and integration were much less efficient following transplantation into the lesioned motor cortex, as previously observed for transplanted mouse cortical neurons. These data constitute an important milestone for the potential use of human PSC-derived cortical cells for the reassembly of cortical circuits and emphasize the importance of cortical areal identity for successful transplantation.

Inertia is a measure of a power system’s capability to counteract frequency disturbances: in conventional power networks, inertia is approximately constant over time, which contributes to network stability. However, as the share of renewable energy sources increases, the inertia associated to synchronous generators declines, which may pose a threat to the overall stability. Reliably estimating the inertia of power systems dominated by inverted-connected sources has therefore become of paramount importance. We develop a framework for the continuous estimation of the inertia in an electric power system, exploiting state-of-the-art artificial intelligence techniques. We perform an in-depth investigation based on power spectra analysis and input-output correlations to explain how the artificial neural network operates in this specific realm, thus shedding light on the input features necessary for proper neural-network training. We validate our approach on a heterogeneous power network comprising synchronous generators, static compensators and converter-interfaced generation: our results highlight how different devices are characterized by distinct spectral footprints - a feature that must be taken into account by transmission system operators when performing online network stability analyses.