Temporal filters in response to presynaptic spike trains: Interplay of cellular, synaptic and short-term plasticity time scales

Temporal filters, the ability of postsynaptic neurons to preferentially select certain presynaptic input patterns, have been shown to be associated with the notion of information filtering and coding of sensory inputs. Their properties can be dynamically characterized as the transient responses to periodic presynaptic inputs. Short-term plasticity (STP) has been proposed to be an important player in the generation of temporal filters, but the response of postsynaptic neurons to presynaptic inputs depends on a collection of time scales in addition to STP's, which conspire to create temporal filters: the postsynaptic time scales generated by the cellular intrinsic currents and the presynaptic time scales captured by the ISI distribution patterns. The mechanisms by which these time scales and the processes giving rise to them interact to produce temporal filters in response to presynaptic input spike trains are not well understood. We carry out a systematic modeling and computational analysis to understand how the postsynaptic low-, high- and band-pass temporal filters are generated in response to periodic presynaptic spike trains in the presence STP, and how the dynamic properties of these filters depend on the interplay of a hierarchy of processes: arrival of the presynaptic spikes, the activation of STP and its effect on the synaptic connection efficacy, and the response of the postsynaptic cell. The time scales associated with each of these processes operate at the short-term, single-event level (they are activated at the arrival of each presynaptic spike) and collectively produce the long-term time scales that determine the shape and properties of the filters. We develop a series of mathematical tools to address these issues for a relatively simple model where depression and facilitation interact only at the level of the synaptic efficacy change as time progresses and we extend our results and tools to account for more complex models that involve interactions at the STP level and multiple STP time scales. We use these tools to understand the mechanisms of generation of temporal filters in the postsynaptic cells in terms of the properties and dynamics of the interacting building blocks.

Connectomic features underlying diverse synaptic connection strengths and subcellular computation

Connectomes generated from electron microscopy images of neural tissue unveil the complex morphology of every neuron and the locations of every synapse interconnecting them. These wiring diagrams may also enable inference of synaptic and neuronal biophysics, such as the functional weights of synaptic connections, but this requires integration with physiological data to properly parameterize. Working with a stereotyped olfactory network in the Drosophila brain, we make direct comparisons of the anatomy and physiology of diverse neurons and synapses with subcellular and subthreshold resolution. We find that synapse density and location jointly predict the amplitude of the somatic postsynaptic potential evoked by a single presynaptic spike. Biophysical models fit to data predict that electrical compartmentalization allows axon and dendrite arbors to balance independent and interacting computations. These findings begin to fill the gap between connectivity maps and activity maps, which should enable new hypotheses about how network structure constrains network function.

Tracking Fast and Slow Changes in Synaptic Weights from Simultaneously Observed Pre- and Postsynaptic Spiking

Synapses change on multiple timescales, ranging from milliseconds to minutes, due to a combination of both short- and long-term plasticity. Here we develop an extension of the common generalized linear model to infer both short- and long-term changes in the coupling between a pre- and postsynaptic neuron based on observed spiking activity. We model short-term synaptic plasticity using additive effects that depend on the presynaptic spike timing, and we model long-term changes in both synaptic weight and baseline firing rate using point process adaptive smoothing. Using simulations, we first show that this model can accurately recover time-varying synaptic weights (1) for both depressing and facilitating synapses, (2) with a variety of long-term changes (including realistic changes, such as due to STDP), (3) with a range of pre and postsynaptic firing rates, and (4) for both excitatory and inhibitory synapses. We then apply our model to two experimentally recorded putative synaptic connections. We find that simultaneously tracking fast changes in synaptic weights, slow changes in synaptic weights, and unexplained variations in baseline firing is essential. Omitting any one of these factors can lead to spurious inferences for the others. Altogether, this model provides a flexible framework for tracking short- and long-term variation in spike transmission.

Large, stable spikes exhibit differential broadening in excitatory and inhibitory neocortical boutons

SUMMARYPresynaptic action potential spikes control neurotransmitter release and thus interneuronal communication. However, the properties and the dynamics of presynaptic spikes in the neocortex remain enigmatic because boutons in the neocortex are small and direct patch-clamp recordings have not been performed. Here we report direct recordings from boutons of neocortical pyramidal neurons and interneurons. Our data reveal rapid and large presynaptic action potentials in layer 5 neurons and fast-spiking interneurons reliably propagating into axon collaterals. For in-depth analyses we validate boutons of mature cultured neurons as models for excitatory neocortical boutons, demonstrating that the presynaptic spike amplitude was unaffected by potassium channels, homeostatic long-term plasticity, and high-frequency firing. In contrast to the stable amplitude, presynaptic spikes profoundly broadened for example during high-frequency firing in layer 5 pyramidal neurons but not in fast-spiking interneurons. Thus, our data demonstrate large presynaptic spikes and fundamental differences between excitatory and inhibitory boutons in the neocortex.

Axonal Na+ channels detect and transmit levels of input synchrony in local brain circuits

Sensory processing requires mechanisms of fast coincidence detection to discriminate synchronous from asynchronous inputs. Spike threshold adaptation enables such a discrimination but is ineffective in transmitting this information to the network. We show here that presynaptic axonal sodium channels read and transmit precise levels of input synchrony to the postsynaptic cell by modulating the presynaptic action potential (AP) amplitude. As a consequence, synaptic transmission is facilitated at cortical synapses when the presynaptic spike is produced by synchronous inputs. Using dual soma-axon recordings, imaging, and modeling, we show that this facilitation results from enhanced AP amplitude in the axon due to minimized inactivation of axonal sodium channels. Quantifying local circuit activity and using network modeling, we found that spikes induced by synchronous inputs produced a larger effect on network activity than spikes induced by asynchronous inputs. Therefore, this input synchrony–dependent facilitation may constitute a powerful mechanism, regulating synaptic transmission at proximal synapses.

Axonal Na+ channels detect and transmit levels of input synchrony in local brain circuits

Sensory processing requires mechanisms of fast coincidence-detection to discriminate synchronous from asynchronous inputs. Spike-threshold adaptation enables such a discrimination but is ineffective in transmitting this information to the network. We show here that presynaptic axonal sodium channels read and transmit precise levels of input synchrony to the postsynaptic cell by modulating the presynaptic action potential (AP) amplitude. As a consequence, synaptic transmission is facilitated at cortical synapses when the presynaptic spike is produced by synchronous inputs. Using dual soma-axon recordings, imaging, and modeling, we show that this facilitation results from enhanced AP amplitude in the axon due to minimized inactivation of axonal sodium-channels. Quantifying local circuit activity and using network modeling, we found that spikes induced by synchronous inputs produced a larger effect on network activity than spikes induced by asynchronous inputs. Therefore, this input-synchrony dependent facilitation (ISF) may constitute a powerful mechanism regulating spike transmission.

Kv1.1 channelopathy abolishes presynaptic spike width modulation by subthreshold somatic depolarization

Although action potentials propagate along axons in an all-or-none manner, subthreshold membrane potential fluctuations at the soma affect neurotransmitter release from synaptic boutons. An important mechanism underlying analog–digital modulation is depolarization-mediated inactivation of presynaptic Kv1-family potassium channels, leading to action potential broadening and increased calcium influx. Previous studies have relied heavily on recordings from blebs formed after axon transection, which may exaggerate the passive propagation of somatic depolarization. We recorded instead from small boutons supplied by intact axons identified with scanning ion conductance microscopy in primary hippocampal cultures and asked how distinct potassium channels interact in determining the basal spike width and its modulation by subthreshold somatic depolarization. Pharmacological or genetic deletion of Kv1.1 broadened presynaptic spikes without preventing further prolongation by brief depolarizing somatic prepulses. A heterozygous mouse model of episodic ataxia type 1 harboring a dominant Kv1.1 mutation had a similar broadening effect on basal spike shape as deletion of Kv1.1; however, spike modulation by somatic prepulses was abolished. These results argue that the Kv1.1 subunit is not necessary for subthreshold modulation of spike width. However, a disease-associated mutant subunit prevents the interplay of analog and digital transmission, possibly by disrupting the normal stoichiometry of presynaptic potassium channels.

Mechanisms That Modulate the Transfer of Spiking Correlations

Correlations between neuronal spike trains affect network dynamics and population coding. Overlapping afferent populations and correlations between presynaptic spike trains introduce correlations between the inputs to downstream cells. To understand network activity and population coding, it is therefore important to understand how these input correlations are transferred to output correlations.Recent studies have addressed this question in the limit of many inputs with infinitesimal postsynaptic response amplitudes, where the total input can be approximated by gaussian noise. In contrast, we address the problem of correlation transfer by representing input spike trains as point processes, with each input spike eliciting a finite postsynaptic response. This approach allows us to naturally model synaptic noise and recurrent coupling and to treat excitatory and inhibitory inputs separately.We derive several new results that provide intuitive insights into the fundamental mechanisms that modulate the transfer of spiking correlations.