Postsynaptic Threshold Research Articles

Temporal Coding of Binary Patterns for Learning of Spiking Neuromorphic Systems Based on Nanocomposite Memristors

The metal/nanocomposite/metal (M/NC/M) memristive structures based on (Co40Fe40B20)x(LiNbO3)100–x have been studied. It has been shown that such memristors may change their conductance according to the bioinspired spike-timing-dependent plasticity (STDP) rules. Spiking neural network with 4 presynaptic inputs connected by memristor-synapses with a postsynaptic threshold neuron-integrator has been created, in which the images clustering with temporal coding has been implemented using the STDP rule. Thus, the fundamental possibility of using a temporal coding method, which is more effective than population-frequency coding, has been demonstrated for self-learning of spiking neuromorphic systems with synaptic weights based on nanocomposite memristors.

A feature-based neurocomputational model of semantic memory.

According with a featural organization of semantic memory, this work is aimed at investigating, through an attractor network, the role of different kinds of features in the representation of concepts, both in normal and neurodegenerative conditions. We implemented new synaptic learning rules in order to take into account the role of partially shared features and of distinctive features with different saliency. The model includes semantic and lexical layers, coding, respectively for object features and word-forms. Connections among nodes are strongly asymmetrical. To account for the feature saliency, asymmetrical synapses were created using Hebbian rules of potentiation and depotentiation, setting different pre-synaptic and post-synaptic thresholds. A variable post-synaptic threshold, which automatically changed to reflect the feature frequency in different concepts (i.e., how many concepts share a feature), was used to account for partially shared features. The trained network solved naming tasks and word recognition tasks very well, exploiting the different role of salient versus marginal features in concept identification. In the case of damage, superordinate concepts were preserved better than the subordinate ones. Interestingly, the degradation of salient features, but not of marginal ones, prevented object identification. The model suggests that Hebbian rules, with adjustable post-synaptic thresholds, can provide a reliable semantic representation of objects exploiting the statistics of input features.

The effects of pair-wise and higher order correlations on the firing rate of a post-synaptic neuron.

Coincident firing of neurons projecting to a common target cell is likely to raise the probability of firing of this postsynaptic cell. Therefore, synchronized firing constitutes a significant event for postsynaptic neurons and is likely to play a role in neuronal information processing. Physiological data on synchronized firing in cortical networks are based primarily on paired recordings and cross-correlation analysis. However, pair-wise correlations among all inputs onto a postsynaptic neuron do not uniquely determine the distribution of simultaneous postsynaptic events. We develop a framework in order to calculate the amount of synchronous firing that, based on maximum entropy, should exist in a homogeneous neural network in which the neurons have known pair-wise correlations and higher-order structure is absent. According to the distribution of maximal entropy, synchronous events in which a large proportion of the neurons participates should exist even in the case of weak pair-wise correlations. Network simulations also exhibit these highly synchronous events in the case of weak pair-wise correlations. If such a group of neurons provides input to a common postsynaptic target, these network bursts may enhance the impact of this input, especially in the case of a high postsynaptic threshold. The proportion of neurons participating in synchronous bursts can be approximated by our method under restricted conditions. When these conditions are not fulfilled, the spike trains have less than maximal entropy, which is indicative of the presence of higher-order structure. In this situation, the degree of synchronicity cannot be derived from the pair-wise correlations.

A new stochastic model for auditory-nerve discharge

This paper investigates the mechanisms for history-dependent probability of eight-nerve discharge, which is modeled as the probability that the excitatory postsynaptic potential (EPSP) process crosses afferent membrane threshold, with the discharge history dependence due to the dependence of postsynaptic threshold voltage on time since previous action potential. The model parameters are the Poisson intensity alpha t of vesicle release, the duration epsilon and probability density PV(upsilon) of single-vesicle EPSP's, and the threshold voltage curve theta (tau) for spiking. It is proven that the infinitesimal conditional probabilities of discharge exhibit two distinct behaviors. The first is associated with the time tau = T D, exactly the time the neuron releases from absolute refractory where there is no intensity [theta(tau) = infinity, for tau < T D]. At this time the neuron has a nonzero probability of discharge [symbol:see text] (T D) = lim delta-->0 Pr(Nt,t+delta = 1/t - wNt = T D). The second regime corresponds to the time since previous spike being greater than dead time, tau > T D, during which time the intensity exists lambda t(tau) = lim delta-->0(1/delta) Pr(Nt,t+delta = 1/t - wNt = tau > T D). The fact that there is a nonzero probability of discharge following passage from the absolute refractory period predicts the nonmonotonic hazard intensity seen in high spontaneous neurons [R. P. Gaumond, Ph.D. thesis, Washington University, St. Louis (1980)] and high driven rate neurons. It is shown that for the lowest range of vesicle release intensities where the vesicle-release-rate/membrane-integration-time product alpha t epsilon small, the nonzero probability of discharge at a point is approximately equal to 0. The discharge intensity is dominated by a term linear in vesicle release intensity: lambda t(tau) approximately alpha t exp(-integral of t-epsilon t alpha sigma d sigma) integral of theta (tau) infinity Pv(upsilon)d upsilon. This is precisely the Siebert-Gaumond intensity product form with monotonic recovered discharge probability. At high vesicle release rates, such as for driven rate responses, the nonzero probability of discharging at a point becomes of nonsignificant size, and the intensity of discharge grows nonlinearly with alpha t, implying the product model does not hold. The model is demonstrated via the analysis of auditory nerve fibers from the cat.

Coding of envelope modulation in the auditory nerve and anteroventral cochlear nucleus.

We have investigated responses of the auditory nerve fibres (ANFS) and anteroventral cochlear nucleus (AVCN) units to narrowband 'single-formant' stimuli (SFSS). We found that low and medium spontaneous rate (SR) ANFS maintain greater amplitude modulation (AM) in their responses at high sound levels than do high SR units when sound level is considered in dB SPL. However, this partitioning of high and low SR units disappears if sound level is considered in dB relative to unit threshold. Stimuli with carrier frequencies away from unit best frequency (BF) were found to generate higher AM in responses at high sound levels than that observed even in most low and medium SR units for stimuli with carrier frequencies near BF. AVCN units were shown to have increased modulation depth in their responses when compared with high SR ANFS with similar BFS and to have increased or comparable modulation depth when compared with low SR ANFS. At sound levels where AM almost completely disappears in high SR ANFS, most AVCN units we studied still show significant AM in their responses. Using a dendritic model, we investigated possible mechanisms of enhanced AM in AVCN units, including the convergence of inputs from different SR groups of ANFS and a postsynaptic threshold mechanism in the soma.

Pharmacological induction of use-dependent receptive field modifications in the visual cortex.

Lasting modifications of the receptive fields of neurons in the visual cortex can be induced by pairing visual stimuli with iontophoretic application of the neuromodulators acetylcholine and noradrenaline or the excitatory amino acids N-methyl-D-aspartate (NMDA) and L-glutamate. The modifications are obtained in less than 1 hour and persist for more than 40 minutes. Thus, acetylcholine and norepinephrine have a permissive role in use-dependent neuronal plasticity. These results support the notion of a postsynaptic threshold for neuronal malleability that differs from that of sodium-dependent action potentials.

Transmitter potentiality of homonymous and heteronymous monosynaptic reflex connections of individual motoneurons.

An assemblage of 110 individual tricipital motoneurons has been examined with the aim of determining those factors that predispose certain motoneurons to heteronymous response in post-tetanic potentiation. Motoneurons that respond most readily to homonymous volleys are not those that respond most readily to post-tetanically potentiated heteronymous volleys. Hence differences in presynaptic organization rather than differences in mean postsynaptic threshold determine differences in readiness for response. Every motoneuron exhibits a distinct asymmetry in transmitter potentiality of homonymous and heteronymous monosynaptic reflex connections. The range of transmitter potentialities is wide and that of heteronymous connections to some motoneurons is greater than that of homonymous connections to some other motoneurons.