Number Of Presynaptic Neurons Research Articles

Proposal and detailed theoretical analysis on a photonic neural network with optically pumped Spin-VCSEL spiking neurons

A dual-layer photonic spiking neural network (PSNN) was constructed, where multiple optically pumped spin vertical-cavity surface-emitting lasers (Spin-VCSELs) were proposed as spiking neurons. Based on a detailed theoretical analysis of leaky integrate-and-fire (LIF) and refractory period characteristics of Spin-VCSEL neurons, the training and testing performance for the studied PSNN was evaluated using two standard pattern classification tasks (Iris dataset, simple digit recognition). The results showed that, by selecting appropriate parameters such as frequency detuning and number of pre-synaptic neurons, etc., higher training/testing accuracies beyond 90% can be obtained. When compared with traditional electrically pumped VCSEL, a threshold reduction of up to 50% can be achieved under nanosecond scale spin relaxation time and circular polarization optical pumping, the feasibility of realizing high accuracy (88%) pattern classification near the reduced threshold was also verified. Therefore, optically pumped Spin-VCSEL neurons can become a valuable new choice for high-performance PSNN with reduced power consumption.

Observing Cell Assemblies From Spike Train Recordings Based on the Biological Basis of Synaptic Connectivity.

Cell assemblies are difficult to observe because they consist of many neurons. We aimed to observe cell assemblies based on biological statistics, such as synaptic connectivity. We developed an estimation method to estimate the activity and synaptic connectivity of cell assemblies from spike trains using mathematical models of individual neurons and cell assemblies. Synaptic transmissions were averaged to generate postsynaptic currents with the same timing and waveform but different amplitudes, as the number of presynaptic neurons was large. We estimated the average synaptic transmission and synaptic connectivity from active cell assemblies based on the stochastic prediction of membrane potentials and verified the estimation ability of the average synaptic transmission and synaptic connectivity using the proposed method on simulated neural activity. Different cell assembly activities evoked by electrical stimuli were correctly sorted into various clusters in experiments using rat cortical neurons cultured on microelectrode arrays. We observed multiple cell assemblies from the spontaneous activity of rat cortical networks on microelectrode arrays, based on the synaptic connectivity patterns estimated by the proposed method. The proposed method was superior to the conventional method for detecting the activity of multiple cell assemblies. Using the proposed method, it is possible to observe multiple cell assemblies based on the biological basis of synaptic connectivity. In summary, we report a novel method to observe cell assemblies from spike train recordings based on the biological basis of synaptic connectivity, rather than merely relying on a statistical method.

Sum Rate of MISO Neuro-Spike Communication Channel With Constant Spiking Threshold.

Communication among neurons, known as neuro-spike communication, is the most promising technique for realization of a bio-inspired nanoscale communication paradigm to achieve biocompatible nanonetworks. In neuro-spike communication, the information, encoded into spike trains, is communicated to various brain regions through neuronal network. An output neuron needs to receive signal from multiple input neurons to generate a spike. Hence, in this paper, we aim to quantify the information transmitted through the multiple-input single-output (MISO) neuro-spike communication channel by considering models for axonal propagation, synaptic transmission, and spike generation. Moreover, the spike generation and propagation in each neuron requires opening and closing of numerous ionic channels on the cell membrane, which consumes considerable amount of ATP molecules called metabolic energy. Thus, we evaluate how applying a constraint on available metabolic energy affects the maximum achievable mutual information of this system. To this aim, we derive a closed form equation for the sum rate of the MISO neuro-spike communication channel and analyze it under the metabolic cost constraints. Finally, we discuss the impacts of changes in number of pre-synaptic neurons on the achievable rate and quantify the tradeoff between maximum achievable sum rate and the consumed metabolic energy.

Short-Term Synaptic Plasticity Compensates for Variability in Number of Motor Neurons at a Neuromuscular Junction

We studied how similar postsynaptic responses are maintained in the face of interindividual variability in the number of presynaptic neurons. In the stomatogastric ganglion of the lobster, Homarus americanus, the pyloric (PY) neurons exist in variable numbers across animals. We show that each individual fiber of the stomach muscles innervated by PY neurons received synaptic input from all neurons present. We performed intracellular recordings of excitatory junction potentials (EJPs) in the muscle fibers to determine the consequences of differences in the number of motor neurons. Despite the variability in neuron number, the compound electrical response of muscle fibers to natural bursting input was similar across individuals. The similarity of total synaptic activation was not due to differences in the spiking activity of individual motor neurons across animals with different numbers of PY neurons. The amplitude of a unitary EJP in response to a single spike in a single motor neuron also did not depend on the number of PY neurons present. Consequently, the compound EJP in response to a single stimulus that activated all motor axons present was larger in individuals with more PY neurons. However, when axons were stimulated with trains of pulses mimicking bursting activity, EJPs facilitated more in individuals with fewer PY neurons. After a few stimuli, this resulted in depolarizations similar to the ones in individuals with more PY neurons. We interpret our findings as evidence that compensatory or homeostatic regulatory mechanisms can act on short-term synaptic dynamics instead of absolute synaptic strength.

Synchrony-asynchrony transitions in neuronal networks

Irregular spike timing over long durations or relative asynchronous spiking between cells is a ubiquitous phenomenon observed in many brain nuclei both in vivo and in vitro. Subthalamic neurons (STN) and globus pallidus (GP) neurons of the basal ganglia are good examples of such neuronal irregularity. They fire asynchronously normally, but become synchronous in Parkinson’s disease [1,2]. These cells have been shown to be autonomous, i.e., their oscillations are sustained in the absence of synaptic transmission. The prevalence of irregularity in such oscillatory neurons, and their tendency to become synchronous under disease conditions poses several challenges. They necessitate a detailed study of the interaction of oscillatory mechanisms that may contribute to synchronization between neurons with the sparsity of their interactions, the size of the network, and the external synaptic input. We study synchrony-asynchrony transitions in a model network of synaptically coupled STN or GP neurons with varying degrees of network size (N) and connectivity (number of presynaptic neurons per postsynaptic neuron, M <=N). We find the critical size (Ncrit) and critical number of connections (Mcrit for given N) needed in order to achieve global synchrony, as well as explore the nature and size of local cluster sates in the asynchronous state. The STN and GP neurons are modeled using Hodgkin-Huxley type of equations that incorporate sodium, potassium, calcium, a low-threshold calcium (T), and an afterhyperpolarization (AHP) activated potassium current [3]. In a network of two or more mutually excitatory neurons, for example, spike-to-spike synchrony can be achieved by increasing the coupling conductance beyond a critical level. In the unsynchronized regime, a phase drift between spike times of the neurons as well as multiple-frequency locked states can result. The level of mutual coupling required for synchrony in an all-to-all coupled network is found to increase as square root of N. In a sparsely connected network local cluster states emerge for weaker coupling, but spike-to-spike synchrony is also achieved for stronger coupling. Synchrony breaks down if the number of presynaptic neurons is fewer than a critical number. An external common synaptic inhibitory input given at Poisson intervals with a fixed arrival rate can cause asynchrony as well as enhance the frequency of the network at all network sizes.

Correlation between uncoupled conductance-based integrate-and-fire neurons due to common and synchronous presynaptic firing.

We investigate the firing characteristics of conductance-based integrate-and-fire neurons and the correlation of firing for uncoupled pairs of neurons as a result of common input and synchronous firing of multiple synaptic inputs. Analytical approximations are derived for the moments of the steady state potential and the effective time constant. We show that postsynaptic firing barely depends on the correlation between inhibitory inputs; only the inhibitory firing rate matters. In contrast, both the degree of synchrony and the firing rate of excitatory inputs are relevant. A coefficient of variation CV > 1 can be attained with low inhibitory firing rates and (Poisson-modulated) synchronized excitatory synaptic input, where both the number of presynaptic neurons in synchronous firing assemblies and the synchronous firing rate should be sufficiently large. The correlation in firing of a pair of uncoupled neurons due to common excitatory input is initially increased for increasing firing rates of independent inhibitory inputs but decreases for large inhibitory firing rates. Common inhibitory input to a pair of uncoupled neurons barely induces correlated firing, but amplifies the effect of common excitation. Synchronous firing assemblies in the common input further enhance the correlation and are essential to attain experimentally observed correlation values. Since uncorrelated common input (i.e., common input by neurons, which do not fire in synchrony) cannot induce sufficient postsynaptic correlation, we conclude that lateral couplings are essential to establish clusters of synchronously firing neurons.