Why spikes?
On a fast timescale, neurons mostly interact by short, stereotypical electrical impulses or spikes. Why? A common answer is that spikes are useful for long-distance communication, to avoid alterations while traveling along axons. But as it turns out, spikes are seen in many places outside neurons: in the heart, in muscles, in plants and even in protists. From these examples, it appears that action potentials mediate some form of coordinated action, a timed event. From this perspective, spikes should not be seen simply as noisy implementations of underlying continuous signals (a sort of analog-to-digital conversion), but rather as events or actions. I will give a number of examples of functional spike-based interactions in living systems.
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, labeling 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. In this talk, I will discuss the implications of seeing the neural code as having three syllables: silences, spikes and bursts. In particular, I will describe recent theoretical and experimental results that implicate bursting in the implementation of top-down attention and the coordination of learning.
Reflex Regulation of Innate Immunity
Reflex circuits in the nervous system integrate changes in the environment with physiology. Compact clusters of brain neuron cell bodies, termed nuclei, are essential for receiving sensory input and for transmitting motor outputs to the body. These nucelii are critical relay stations which process incoming information and convert these signals to outgoing action potentials which regulate immune system functions. Thus, reflex neural circuits maintain parameters of immunological physiology within a narrow range optimal for health. Advances in neuroscience and immunology using optogenetics, pharmacogenetics, and functional mapping offer a new understanding of the importance of neural circuitry underlying immunity, and offer direct paths to new therapies.
Data-driven reduction of dendritic morphologies with preserved dendro-somatic responses
There is little consensus on the level of spatial complexity at which dendrites operate. On the one hand, emergent evidence indicates that synapses cluster at micrometer spatial scales. On the other hand, most modelling and network studies ignore dendrites altogether. This dichotomy raises an urgent question: what is the smallest relevant spatial scale for understanding dendritic computation? We have developed a method to construct compartmental models at any level of spatial complexity. Through carefully chosen parameter fits, solvable in the least-squares sense, we obtain accurate reduced compartmental models. Thus, we are able to systematically construct passive as well as active dendrite models at varying degrees of spatial complexity. We evaluate which elements of the dendritic computational repertoire are captured by these models. We show that many canonical elements of the dendritic computational repertoire can be reproduced with few compartments. For instance, for a model to behave as a two-layer network, it is sufficient to fit a reduced model at the soma and at locations at the dendritic tips. In the basal dendrites of an L2/3 pyramidal model, we reproduce the backpropagation of somatic action potentials (APs) with a single dendritic compartment at the tip. Further, we obtain the well-known Ca-spike coincidence detection mechanism in L5 Pyramidal cells with as few as eleven compartments, the requirement being that their spacing along the apical trunk supports AP backpropagation. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input resistance between the ablated branches and the next proximal dendrite. Consequently, when the average conductance load on distal synapses is constant, the dendritic tree can be simplified while appropriately decreasing synaptic weights. When the conductance level fluctuates strongly, for instance through a-priori unpredictable fluctuations in NMDA activation, a constant weight rescale factor cannot be found, and the dendrite cannot be simplified. We have created an open source Python toolbox (NEAT - https://neatdend.readthedocs.io/en/latest/) that automatises the simplification process. A NEST implementation of the reduced models, currently under construction, will enable the simulation of few-compartment models in large-scale networks, thus bridging the gap between cellular and network level neuroscience.
Capacitance clamp - artificial capacitance in biological neurons via dynamic clamp
A basic time scale in neural dynamics from single cells to the network level is the membrane time constant - set by a neuron’s input resistance and its capacitance. Interestingly, the membrane capacitance appears to be more dynamic than previously assumed with implications for neural function and pathology. Indeed, altered membrane capacitance has been observed in reaction to physiological changes like neural swelling, but also in ageing and Alzheimer's disease. Importantly, according to theory, even small changes of the capacitance can affect neuronal signal processing, e.g. increase network synchronization or facilitate transmission of high frequencies. In experiment, robust methods to modify the capacitance of a neuron have been missing. Here, we present the capacitance clamp - an electrophysiological method for capacitance control based on an unconventional application of the dynamic clamp. In its original form, dynamic clamp mimics additional synaptic or ionic conductances by injecting their respective currents. Whereas a conductance directly governs a current, the membrane capacitance determines how fast the voltage responds to a current. Accordingly, capacitance clamp mimics an altered capacitance by injecting a dynamic current that slows down or speeds up the voltage response (Fig 1 A). For the required dynamic current, the experimenter only has to specify the original cell and the desired target capacitance. In particular, capacitance clamp requires no detailed model of present conductances and thus can be applied in every excitable cell. To validate the capacitance clamp, we performed numerical simulations of the protocol and applied it to modify the capacitance of cultured neurons. First, we simulated capacitance clamp in conductance based neuron models and analysed impedance and firing frequency to verify the altered capacitance. Second, in dentate gyrus granule cells from rats, we could reliably control the capacitance in a range of 75 to 200% of the original capacitance and observed pronounced changes in the shape of the action potentials: increasing the capacitance reduced after-hyperpolarization amplitudes and slowed down repolarization. To conclude, we present a novel tool for electrophysiology: the capacitance clamp provides reliable control over the capacitance of a neuron and thereby opens a new way to study the temporal dynamics of excitable cells.
Workshop: Spatial Brain Dynamics
Traditionally, the term dynamics means changes in a system evolving over time. However, in the brain action potentials propagate along axons to induce postsynaptic currents with different delays at many sites simultaneously. This fundamental computational mechanism evolves spatially to engage the neuron populations involved in brain functions. To identify and understand the spatial processing in brains, this workshop will focus on the spatial principles of brain dynamics that determine how action potentials and membrane currents propagate in the networks of neurons that brains are made of. We will focus on non-artificial dynamics, which excludes in vitro dynamics, interference, electrical and optogenetic stimulations of brains in vivo. Recent non-artificial studies of spatial brain dynamics can actually explain how sensory, motor and internal brain functions evolve. The purpose of this workshop is to discuss these recent results and identify common principles of spatial brain dynamics.
Workshop: Spatial Brain Dynamics
Traditionally, the term dynamics means changes in a system evolving over time. However, in the brain action potentials propagate along axons to induce postsynaptic currents with different delays at many sites simultaneously. This fundamental computational mechanism evolves spatially to engage the neuron populations involved in brain functions. To identify and understand the spatial processing in brains, this workshop will focus on the spatial principles of brain dynamics that determine how action potentials and membrane currents propagate in the networks of neurons that brains are made of. We will focus on non-artificial dynamics, which excludes in vitro dynamics, interference, electrical and optogenetic stimulations of brains in vivo. Recent non-artificial studies of spatial brain dynamics can actually explain how sensory, motor and internal brain functions evolve. The purpose of this workshop is to discuss these recent results and identify common principles of spatial brain dynamics.
Workshop: Spatial Brain Dynamics
Traditionally, the term dynamics means changes in a system evolving over time. However, in the brain action potentials propagate along axons to induce postsynaptic currents with different delays at many sites simultaneously. This fundamental computational mechanism evolves spatially to engage the neuron populations involved in brain functions. To identify and understand the spatial processing in brains, this workshop will focus on the spatial principles of brain dynamics that determine how action potentials and membrane currents propagate in the networks of neurons that brains are made of. We will focus on non-artificial dynamics, which excludes in vitro dynamics, interference, electrical and optogenetic stimulations of brains in vivo. Recent non-artificial studies of spatial brain dynamics can actually explain how sensory, motor and internal brain functions evolve. The purpose of this workshop is to discuss these recent results and identify common principles of spatial brain dynamics.