Incoming Spikes Research Articles

A Possible Role of Incoming Spike Synchrony in Associative Memory Model with STDP Learning Rule

Although recent experiments suggested that a synchronous neural activity may play a significant functional role in cognitive processes, it is still unclear how the spike synchronization affects the network behavior in neural systems. In the first approach, we consider a specific problem: how the retrieval state of associative memory model of neural network is affected by incoming spike synchronization. Considering a simple network of Leaky-Integrate-and-Fire neurons, organizing through the learning rule by spike-timing dependent plasticity (STDP), we present that this network exhibits the nature of associative memory as a standard Hopfield model for asynchronous input, whereas a transition between the learned spike-trains can be triggered by a brief incoming spike-synchrony.

Short-Term Synaptic Depression Causes a Non-Monotonic Response to Correlated Stimuli

Unreliability is a ubiquitous feature of synaptic transmission in the brain. The information conveyed in the discharges of an ensemble of cells (e.g., in the spike count or in the timing of synchronous events) may not be faithfully transmitted to the postsynaptic cell because a large fraction of the spikes fail to elicit a synaptic response. In addition, short-term depression increases the failure rate with the presynaptic activity. We use a simple neuron model with stochastic depressing synapses to understand the transformations undergone by the spatiotemporal patterns of incoming spikes as these are first converted into synaptic current and afterward into the cell response. We analyze the mean and SD of the current produced by different stimuli with spatiotemporal correlations. We find that the mean, which carries information only about the spike count, rapidly saturates as the input rate increases. In contrast, the current deviation carries information about the correlations. If the afferent action potentials are uncorrelated, it saturates monotonically, whereas if they are correlated it increases, reaches a maximum, and then decreases to the value produced by the uncorrelated stimulus. This means that, at high input rates, depression erases from the synaptic current any trace of the spatiotemporal structure of the input. The non-monotonic behavior of the deviation can be inherited by the response rate provided that the mean current saturates below the current threshold setting the cell in the fluctuation-driven regimen. Afferent correlations therefore enable the modulation of the response beyond the saturation of the mean current.

Traffic Matrix Estimation Using Spike Flow Detection

This paper proposes a new method of estimating real-time traffic matrices that only incurs small errors in estimation. A traffic matrix represents flows of traffic in a network. It is an essential tool for capacity planning and traffic engineering. However, the high costs involved in measurement make it difficult to assemble an accurate traffic matrix. It is therefore important to estimate a traffic matrix using limited information that only incurs small errors. Existing approaches have used IP-related information to reduce the estimation errors and computational complexity. In contrast, our method, called spike flow measurement (SFM) reduces errors and complexity by focusing on spikes. A spike is transient excessive usage of a communications link. Spikes are easily monitored through an SNMP framework. This reduces the measurement costs compared to that of other approaches. SFM identifies spike flows from traffic byte counts by detecting pairs of incoming and outgoing spikes in a network. A matrix is then constructed from collected spike flows as an approximation of the real traffic matrix. Our experimental evaluation reveals that the average error in estimation is 28%, which is sufficiently small for the method to be applied to a wide range of network nodes, including Ethernet switches and IP routers.

A spike train analysis for detecting temporal integration in neurons

A multi-unit spike train analysis technique is developed to deduce the number of incoming spikes that are temporally integrated to produce the next spike firing by examining the dependency of spike firing in a given neuron with respect to the sequence of firings in another neuron. This analysis considers the contribution of temporal summation of multiple spikes in one neuron that is correlated to the probability of spike firing in another neuron. These simulation results show that the analysis can extract the number of spikes and the duration of temporal integration.

From Wetware to Hardware: Reverse Engineering Using Probabilistic RAMs

A model of neural processing is described which is able to incorporate a great deal of neurophysiological detail (synaptic noise, synapse-synapse interactions, cell surface geometry, temporal features) and which is capable of hardware realisation as a probabilistic random access memory’ (pRAM). The model can operate either in a binary mode or can integrate the effects of incoming spike trains to perform a real-to-binary mapping (integrating pRAM’ or i-pRAM). pRAMs can be trained using a variety of techniques, in particular reinforcement training, which has the advantage of being wholly realisable in hardware as well as having greater biological plausibility than supervised techniques. © 1992, Walter de Gruyter. All rights reserved.

GABA-Mediated Presynaptic Inhibition in Crayfish Primary Afferents by Non-A, Non-B GABA Receptors.

GABAergic presynaptic inhibition has been investigated in primary afferents using an in vitro preparation of the crayfish, Procambarus clarkii. Presynaptic terminals of a leg proprioceptor, the coxo-basal (CB) chordotonal organ, were impaled in the neuorpil of the 5th thoracic ganglion. Pressure ejection of small volumes of the GABAA or GABAB receptor agonists, muscimol and 3-aminopropylphosphinic acid (3-APA), both induce depolarizing responses in the impaled CB sensory terminal. These depolarizations are not blocked by the specific GABAA and GABAB receptor antagonists, SR-95531 and phaclofen, but they are abolished by picrotoxin. Both muscimol- and 3-APA-induced depolarizations are carried by an increase in conductance to Cl-. The presynaptic increase in conductance to Cl- by GABA receptor activation leads to a depression of sensory synaptic transmission through a shunting of the incoming spikes. Monosynaptic EPSPs elicited in motor neurons by CB sensory nerve stimulation are depressed by muscimol and 3-APA. GABA-mediated presynaptic modulation occurs in crayfish primary afferents which can adjust the gain of reflexes. These results show that GABA-activated Cl- channels can induce a modulation of synaptic transmission, but also that the distinction between GABAA and GABAB receptors, as in vertebrates, is not applicable to the crustacean primary afferents.

Intelligent software for spike separation in multiunit recordings.

An algorithm allowing unsupervised spike separation on the basis of three parameters is described. It rests on the assumption that the first incoming spike is part of the first family of spikes. The second spike is compared to the first and, if similar, is included in the first family; otherwise, it constitutes the first member of the second family, and so on. From the first to the 30th spikes of a given family, similarity is established on a percentage basis (10 per 100 around the family's centre of gravity), and when the family includes over 30 spikes, similarity is measured by the standard deviation (1·96 SD around the family's centre of gravity). As new families occur, the families are sorted according to the number of spikes they comprise. The algorithm, its implementation and related software are fully described. Results were tested with both artificial and natural material. Using artificial spikes as input, it was demonstrated that on average 68 per cent of spikes were correctly classified, 30 per cent were rejected, and only 2 per cent were wrongly classified. For natural spike inputs, 65 per cent of recorded spikes were classified, and their separation into several families were confirmed on a physiological basis.