Spike-timing computation properties of a feed-forward neural network model

Abstract

Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g., serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape these transformations, we modeled feed-forward networks of 7–22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity (STDP) rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS) in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.

Auditory-somatosensory bimodal stimulation desynchronizes brain circuitry to reduce tinnitus in guinea pigs and humans

“…These DCN interneurons, which receive parallel-fiber synapses that exhibit STDP (17), provide recurrent inhibitory synapses onto fusiform cells. Cartwheel cells, therefore, may play an essential role in generating fusiform cell synchrony (19,20). Another potential player, the Golgi cell in the marginal region of the cochlear nucleus, provides feedback modulation of granule cell output, which may entrain parallel fibers into synchronized firing (52)(53)(54).…”

“…Importantly, these ‘learning rules’ are altered following noise exposure so that animals with tinnitus show a broader range of stimulus intervals that evoke LTP, whereas noise-exposed animals without tinnitus have broader range intervals that evoke LTD (18). Theoretical models of feedforward networks predict that LTP-driven synaptic strengthening will increase circuit connectivity and result in hypersynchrony (19). Hypersynchrony can also be driven by inhibitory network components (20), such as the cartwheel cells in the DCN (Fig.…”

A new design methodology to predict wind farm energy production by means of a spiking neural network–based system

“…32 The optimisation of the parameters of NeuCube has been achieved by trial and error tests on the SNN models, using an extended grid search approach. 33 The grid search approach is a straighforward but effective method to tune parameters. Suppose there are P parameters that have to be optimized simutaneously.…”

Hardware design of LIF with Latency neuron model with memristive STDP synapses

“…Synaptic plasticity is the ability of synapses to strengthen or weaken of the weight over time, in response to increases or decreases in their activity. A well-known type of synaptic plasticity is based on the precise timings of pre-and post-synaptic spikes, influencing the magnitude and direction of change of the synaptic strength [48].…”