NMDA Currents Modulate the Synaptic Input–Output Functions of Neurons in the Dorsal Nucleus of the Lateral Lemniscus in Mongolian Gerbils

Neurons in the dorsal nucleus of the lateral lemniscus (DNLL) receive excitatory and inhibitory inputs from the superior olivary complex (SOC) and convey GABAergic inhibition to the contralateral DNLL and the inferior colliculi. Unlike the fast glycinergic inhibition in the SOC, this GABAergic inhibition outlasts auditory stimulation by tens of milliseconds. Two mechanisms have been postulated to explain this persistent inhibition. One, an “integration-based” mechanism, suggests that postsynaptic excitatory integration in DNLL neurons generates prolonged activity, and the other favors the synaptic time course of the DNLL output itself. The feasibility of the integration-based mechanism was tested in vitro in DNLL neurons of Mongolian gerbils by quantifying the cellular excitability and synaptic input–output functions (IO-Fs). All neurons were sustained firing and generated a near monotonic IO-F on current injections. From synaptic stimulations, we estimate that activation of approximately five fibers, each on average liberating ∼18 vesicles, is sufficient to trigger a single postsynaptic action potential. A strong single pulse of afferent fiber stimulation triggered multiple postsynaptic action potentials. The steepness of the synaptic IO-F was dependent on the synaptic NMDA component. The synaptic NMDA receptor current defines the slope of the synaptic IO-F by enhancing the temporal and spatial EPSP summation. Blocking this NMDA-dependent amplification during postsynaptic integration of train stimulations resulted into a ∼20% reduction of the decay time course of the GABAergic inhibition. Thus, our data show that the NMDA-dependent amplification of the postsynaptic activity contributes to the GABAergic persistent inhibition generated by DNLL neurons

Medial Ganglionic Eminence Progenitors Transplanted into Hippocampus Integrate in a Functional and Subtype-Appropriate Manner.

Medial ganglionic eminence (MGE) transplantation rescues disease phenotypes in various preclinical models with interneuron deficiency or dysfunction, including epilepsy. While underlying mechanism(s) remains unclear to date, a simple explanation is that appropriate synaptic integration of MGE-derived interneurons elevates GABA-mediated inhibition and modifies the firing activity of excitatory neurons in the host brain. However, given the complexity of interneurons and potential for transplant-derived interneurons to integrate or alter the host network in unexpected ways, it remains unexplored whether synaptic connections formed by transplant-derived interneurons safely mirror those associated with endogenous interneurons. Here, we combined optogenetics, interneuron-specific Cre driver mouse lines, and electrophysiology to study synaptic integration of MGE progenitors. We demonstrated that MGE-derived interneurons, when transplanted into the hippocampus of neonatal mice, migrate in the host brain, differentiate to mature inhibitory interneurons, and form appropriate synaptic connections with native pyramidal neurons. Endogenous and transplant-derived MGE progenitors preferentially formed inhibitory synaptic connections onto pyramidal neurons but not endogenous interneurons. These findings demonstrate that transplanted MGE progenitors functionally integrate into the postnatal hippocampal network

Effects of Synaptic and Myelin Plasticity on Learning in a Network of Kuramoto Phase Oscillators

Models of learning typically focus on synaptic plasticity. However, learning is the result of both synaptic and myelin plasticity. Specifically, synaptic changes often co-occur and interact with myelin changes, leading to complex dynamic interactions between these processes. Here, we investigate the implications of these interactions for the coupling behavior of a system of Kuramoto oscillators. To that end, we construct a fully connected, one-dimensional ring network of phase oscillators whose coupling strength (reflecting synaptic strength) as well as conduction velocity (reflecting myelination) are each regulated by a Hebbian learning rule. We evaluate the behavior of the system in terms of structural (pairwise connection strength and conduction velocity) and functional connectivity (local and global synchronization behavior). We find that for conditions in which a system limited to synaptic plasticity develops two distinct clusters both structurally and functionally, additional adaptive myelination allows for functional communication across these structural clusters. Hence, dynamic conduction velocity permits the functional integration of structurally segregated clusters. Our results confirm that network states following learning may be different when myelin plasticity is considered in addition to synaptic plasticity, pointing towards the relevance of integrating both factors in computational models of learning.Comment: 39 pages, 15 figures This work is submitted in Chaos: An Interdisciplinary Journal of Nonlinear Scienc

A Neuron as a Signal Processing Device

A neuron is a basic physiological and computational unit of the brain. While much is known about the physiological properties of a neuron, its computational role is poorly understood. Here we propose to view a neuron as a signal processing device that represents the incoming streaming data matrix as a sparse vector of synaptic weights scaled by an outgoing sparse activity vector. Formally, a neuron minimizes a cost function comprising a cumulative squared representation error and regularization terms. We derive an online algorithm that minimizes such cost function by alternating between the minimization with respect to activity and with respect to synaptic weights. The steps of this algorithm reproduce well-known physiological properties of a neuron, such as weighted summation and leaky integration of synaptic inputs, as well as an Oja-like, but parameter-free, synaptic learning rule. Our theoretical framework makes several predictions, some of which can be verified by the existing data, others require further experiments. Such framework should allow modeling the function of neuronal circuits without necessarily measuring all the microscopic biophysical parameters, as well as facilitate the design of neuromorphic electronics.Comment: 2013 Asilomar Conference on Signals, Systems and Computers, see http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=681029

Reading out a spatiotemporal population code by imaging neighbouring parallel fibre axons in vivo.

The spatiotemporal pattern of synaptic inputs to the dendritic tree is crucial for synaptic integration and plasticity. However, it is not known if input patterns driven by sensory stimuli are structured or random. Here we investigate the spatial patterning of synaptic inputs by directly monitoring presynaptic activity in the intact mouse brain on the micron scale. Using in vivo calcium imaging of multiple neighbouring cerebellar parallel fibre axons, we find evidence for clustered patterns of axonal activity during sensory processing. The clustered parallel fibre input we observe is ideally suited for driving dendritic spikes, postsynaptic calcium signalling, and synaptic plasticity in downstream Purkinje cells, and is thus likely to be a major feature of cerebellar function during sensory processing

Comprehensive Monosynaptic Rabies Virus Mapping of Host Connectivity with Neural Progenitor Grafts after Spinal Cord Injury.

Neural progenitor cells grafted to sites of spinal cord injury have supported electrophysiological and functional recovery in several studies. Mechanisms associated with graft-related improvements in outcome appear dependent on functional synaptic integration of graft and host systems, although the extent and diversity of synaptic integration of grafts with hosts are unknown. Using transgenic mouse spinal neural progenitor cell grafts expressing the TVA and G-protein components of the modified rabies virus system, we initiated monosynaptic tracing strictly from graft neurons placed in sites of cervical spinal cord injury. We find that graft neurons receive synaptic inputs from virtually every known host system that normally innervates the spinal cord, including numerous cortical, brainstem, spinal cord, and dorsal root ganglia inputs. Thus, implanted neural progenitor cells receive an extensive range of host neural inputs to the injury site, potentially enabling functional restoration across multiple systems

Evolving Dendritic Morphologies Highlight the Impact of Structured Synaptic Inputs on Neuronal Performance

Acknowledgements I would like to express my sincere gratitude to Dr. Rene te Boekhorst for his valued support and guidance extended to me.Postprin

Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition

A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in-situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.Comment: This is a preprint of an article accepted for publication in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no. 2, June 201