Distinct dendritic Ca2+ spike forms produce opposing input-output transformations in rat CA3 pyramidal cells

Morpho-electric diversity of human hippocampal CA1 pyramidal neurons

Hippocampal pyramidal neuron activity underlies episodic memory and spatial navigation. Although extensively studied in rodents, extremely little is known about human hippocampal pyramidal neurons, even though the human hippocampus underwent strong evolutionary reorganization and shows lower theta rhythm frequencies. To test whether biophysical properties of human Cornu Amonis subfield 1 (CA1) pyramidal neurons can explain observed rhythms, we map the morpho-electric properties of individual CA1 pyramidal neurons in human, non-pathological hippocampal slices from neurosurgery. Human CA1 pyramidal neurons have much larger dendritic trees than mouse CA1 pyramidal neurons, have a large number of oblique dendrites, and resonate at 2.9 Hz, optimally tuned to human theta frequencies. Morphological and biophysical properties suggest cellular diversity along a multidimensional gradient rather than discrete clustering. Across the population, dendritic architecture and a large number of oblique dendrites consistently boost memory capacity in human CA1 pyramidal neurons by an order of magnitude compared to mouse CA1 pyramidal neurons.

Dendritic excitability controls overdispersion

The brain is an intricate assembly of intercommunicating neurons whose input-output function is only partially understood. The role of active dendrites in shaping spiking responses, in particular, is unclear. Although existing models account for active dendrites and spiking responses, they are too complex to analyze analytically and demand long stochastic simulations. Here we combine cable and renewal theory to describe how input fluctuations shape the response of neuronal ensembles with active dendrites. We found that dendritic input readily and potently controls interspike interval dispersion. This phenomenon can be understood by considering that neurons display three fundamental operating regimes: one mean-driven regime and two fluctuation-driven regimes. We show that these results are expected to appear for a wide range of dendritic properties and verify predictions of the model in experimental data. These findings have implications for the role of interspike interval dispersion in learning and for theories of attractor states.

Silences, spikes and bursts: Three-part knot of the neural code

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.

Dendrites and efficiency: Optimizing performance and resource utilization

The brain is a highly efficient system that has evolved to optimize performance under limited resources. In this review, we highlight recent theoretical and experimental studies that support the view that dendrites make information processing and storage in the brain more efficient. This is achieved through the dynamic modulation of integration versus segregation of inputs and activity within a neuron. We argue that under conditions of limited energy and space, dendrites help biological networks to implement complex functions such as processing natural stimuli on behavioral timescales, performing the inference process on those stimuli in a context-specific manner, and storing the information in overlapping populations of neurons. A global picture starts to emerge, in which dendrites help the brain achieve efficiency through a combination of optimization strategies that balance the tradeoff between performance and resource utilization.

BTSP, not STDP, Drives Shifts in Hippocampal Representations During Familiarization

Synaptic plasticity is widely thought to support memory storage in the brain, but the rules determining impactful synaptic changes in-vivo are not known. We considered the trial-by-trial shifting dynamics of hippocampal place fields (PFs) as an indicator of ongoing plasticity during memory formation. By implementing different plasticity rules in computational models of spiking place cells and comparing to experimentally measured PFs from mice navigating familiar and novel environments, we found that Behavioral-Timescale-Synaptic-Plasticity (BTSP), rather than Hebbian Spike-Timing-Dependent-Plasticity, is the principal mechanism governing PF shifting dynamics. BTSP-triggering events are rare, but more frequent during novel experiences. During exploration, their probability is dynamic: it decays after PF onset, but continually drives a population-level representational drift. Finally, our results show that BTSP occurs in CA3 but is less frequent and phenomenologically different than in CA1. Overall, our study provides a new framework to understand how synaptic plasticity shapes neuronal representations during learning.

The impact of Hodgkin-Huxley models on dendritic research

For the past seven decades, the Hodgkin-Huxley (HH) formalism has been an invaluable tool in the arsenal of neuroscientists, allowing for robust and reproducible modelling of ionic conductances and the electrophysiological phenomena they underlie. Despite its apparent age, its role as a cornerstone of computational neuroscience has not waned. The discovery of dendritic regenerative events mediated by ionic and synaptic conductances has solidified the importance of HH-based models further, yielding new predictions concerning dendritic integration, synaptic plasticity and neuronal computation. These predictions are often validated through in vivo and in vitro experiments, advancing our understanding of the neuron as a biological system and emphasizing the importance of HH-based detailed computational models as an instrument of dendritic research. In this article, we discuss recent studies in which the HH formalism is used to shed new light on dendritic function and its role in neuronal phenomena.

Hypothalamic Supramammillary Nucleus Selectively Excites Hippocampal CA3 Interneurons to Suppress CA3 Pyramidal Neuron Activity

A key mode of neuronal communication between distant brain regions is through excitatory synaptic transmission mediated by long-range glutamatergic projections emitted from principal neurons. The long-range glutamatergic projection normally forms numerous en passant excitatory synapses onto both principal neurons and interneurons along its path. Under physiological conditions, the monosynaptic excitatory drive onto postsynaptic principal neurons outweighs disynaptic feedforward inhibition, with the net effect of depolarizing principal neurons. In contrast with this conventional doctrine, here we report that a glutamatergic projection from the hypothalamic supramammillary nucleus (SuM) largely evades postsynaptic pyramidal neurons (PNs), but preferentially target interneurons in the hippocampal CA3 region to predominantly provide feedforward inhibition. Using viral-based retrograde and anterograde tracing and ChannelRhodopsin2 (ChR2)-assisted patch-clamp recording in mice of either sex, we show that SuM projects sparsely to CA3 and provides minimal excitation onto CA3 PNs. Surprisingly, despite its sparse innervation, the SuM input inhibits all CA3 PNs along the transverse axis. Further, we find that SuM provides strong monosynaptic excitation onto CA3 parvalbumin-expressing interneurons evenly along the transverse axis, which likely mediates the SuM-driven feedforward inhibition. Together, our results demonstrate that a novel long-range glutamatergic pathway largely evades principal neurons, but rather preferentially innervates interneurons in a distant brain region to suppress principal neuron activity. Moreover, our findings reveal a new means by which SuM regulates hippocampal activity through SuM-to-CA3 circuit, independent of the previously focused projections from SuM to CA2 or dentate gyrus.SIGNIFICANCE STATEMENT The dominant mode of neuronal communication between brain regions is the excitatory synaptic transmission mediated by long-range glutamatergic projections, which form en passant excitatory synapses onto both pyramidal neurons and interneurons along its path. Under normal conditions, the excitation onto postsynaptic neurons outweighs feedforward inhibition, with the net effect of depolarization. In contrast with this conventional doctrine, here we report that a glutamatergic input from hypothalamic supramammillary nucleus (SuM) largely evades PNs but selectively targets interneurons to almost exclusively provide disynaptic feedforward inhibition onto hippocampal CA3 PNs. Thus, our findings reveal a novel subcortical-hippocampal circuit that enables SuM to regulate hippocampal activity via SuM-CA3 circuit, independent of its projections to CA2 or dentate gyrus.

The guide to dendritic spikes of the mammalian cortex in vitro and in vivo

Half a century since their discovery by Llinás and colleagues, dendritic spikes have been observed in various neurons in different brain regions, from the neocortex and cerebellum to the basal ganglia. Dendrites exhibit a terrifically diverse but stereotypical repertoire of spikes, sometimes specific to subregions of the dendrite. Despite their prevalence, we only have a glimpse into their role in the behaving animal. This article aims to survey the full range of dendritic spikes found in excitatory and inhibitory neurons, compare them in vivo versus in vitro, and discuss new studies describing dendritic spikes in the human cortex. We focus on dendritic spikes in neocortical and hippocampal neurons and present a roadmap to identify and understand the broader role of dendritic spikes in single-cell computation.