Javier Iglesias

Recurrent spatiotemporal firing patterns in large spiking neural networks with ontogenetic and epigenetic processes.

Neural development and differentiation are characterized by an overproduction of cells and a transient exuberant number of connections followed by cell death and selective synaptic pruning. We simulated large spiking neural networks (10,000 units at its maximum size) with and without an ontogenetic process corresponding to a brief initial phase of apoptosis driven by an excessive firing rate mimicking cell death due to glutamatergic neurotoxicity and glutamate-triggered apoptosis. This phase was followed by the onset of spike timing dependent synaptic plasticity (STDP), driven by spatiotemporal patterns of stimulation. Despite the reduction in cell counts the apoptosis tended to increase the excitatory/inhibitory ratio because the inhibitory cells were affected at first. Recurrent spatiotemporal firing patterns emerged in both developmental condition but they differed in dynamics. They were less numerous but repeated more often after apoptosis. The results suggest that initial cell death may be necessary for the emergence of stable cell assemblies, able to sustain and process temporal information, from the initially randomly connected networks.

Emergence of oriented cell assemblies associated with spike-timing-dependent plasticity

We studied the emergence of cell assemblies out of a locally connected random network of 10,000 integrate-and-fire units distributed on a 100×100 2D lattice. The network was composed of 80% excitatory and 20% inhibitory units with balanced excitatory/inhibitory synaptic weights. Excitatory–excitatory synapses were modified according to a spike-timing-dependent synaptic plasticity (STDP) rule associated with synaptic pruning. In presence of a stimulus and with independent random background noise (5 spikes/s), we observed that after 5·105 ms of simulated time, about 8% of the exc–exc connections remained active and were reinforced with respect to the initial strength. The projections that remained active after pruning tended to be oriented following a feed-forward converging–diverging pattern. This result suggests that topologies compatible with synfire chains may appear during unsupervised pruning processes.

Nonlinear dynamics emerging in large scale neural networks with ontogenetic and epigenetic processes

We simulated a large scale spiking neural network characterized by an initial developmental phase featuring cell death driven by an excessive firing rate, followed by the onset of spike-timing-dependent synaptic plasticity (STDP), driven by spatiotemporal patterns of stimulation. The network activity stabilized such that recurrent preferred firing sequences appeared along the STDP phase. The analysis of the statistical properties of these patterns give hints to the hypothesis that a neural network may be characterized by a particular state of an underlying dynamical system that produces recurrent firing patterns.

Neuronal cell death and synaptic pruning driven by spike-timing dependent plasticity

The embryonic nervous system is refined over the course of development as a result of two main processes: apoptosis (programmed cell death) and selective axon pruning. We simulated a large scale spiking neural network characterized by an initial apoptotic phase, driven by an excessive firing rate, followed by the onset of spike-timing-dependent plastiticity (STDP), driven by spatiotemporal patterns of stimulation. In the apoptotic phase the cell death affected the inhibitory more than the excitatory units. The network activity stabilized such that recurrent preferred firing sequences appeared along the STDP phase, thus suggesting the emergence of cell assemblies from large randomly connected networks.

The POEtic Electronic Tissue and Its Role in the Emulation of Large-Scale Biologically Inspired Spiking Neural Networks Models

One of the major obstacles found when trying to construct artefacts derived from principles observed in living beings is the lack of actual dynamic hardware with autonomous capabilities. Even if programmable devices offer the possibility of modifying the functionality implemented in the device, they rely on external hardware and software elements to provide its physical configuration. In this paper we present a new family of electronic devices, called POEtic, whose architecture has been derived from the basic properties that can be extracted from the three major organization principles present in living beings: phylogenesis, ontogenesis and epigenesis. We will demonstrate that the capabilities present in these new programmable devices make them an ideal candidate for the real-time emulation of large-scale biologically inspired spiking neural network models.

Stimulus-driven unsupervised synaptic pruning in large neural networks

We studied the emergence of cell assemblies out of locally connected random networks of integrate-and-fire units distributed on a 2D lattice stimulated with a spatiotemporal pattern in presence of independent random background noise. Networks were composed of 80% excitatory and 20% inhibitory units with initially balanced synaptic weights. Excitatory–excitatory synapses were modified according to a spike-timing-dependent synaptic plasticity (stdp) rule associated with synaptic pruning. We show that the application, in presence of background noise, of a recurrent pattern of stimulation let appear cell assemblies characterized by an internal pattern of converging projections and a feed-forward topology not observed with an equivalent random stimulation.

Computer assisted neurophysiological analysis of cell assemblies activity

Abstract A critical feature of brain theories is whether neurons convey a noisy rate code or a precise temporal code. One of most valuable ways to test these theories consists in collecting the electrophysiological activity of cell assembles under several experimental conditions. The sequences of cell discharges—the spike trains—form time series whose dynamics is strongly related to the information processing carried out in the brain areas under study. Our purpose is to provide a user-friendly framework of a ‘Virtual Laboratory’ where computational neuroscience analyses and display of results can be distributed over a computer network, like Internet.

Dynamics of Firing Patterns in Evolvable Hierarchically Organized Neural Networks

A scalable hardware platform made of custom reconfigurable devices endowed with bio-inspired ontogenetic and epigenetic features is configured to run an artificial neural network with developmental and evolvable capabilities. The hardware architecture allows internetwork communication and this study analyzes the simulated activity of two hierarchically organized spiking neural networks. The main features were an initial developmental phase characterized by cell death (apoptosis driven by excessive firing rate), followed by spike timing dependent synaptic plasticity in presence of background noise. The emergence of precise firing sequences formed by recurrent patterns of spike intervals above chance levels suggested the build-up of a connectivity, out of initially randomly connected networks, able to sustain temporal information processing. The relative frequency of precise firing sequences was higher in the downstream network and their dynamics suggested the emergence of an unsupervised hierarchical activity-driven connectivity.

Implementation of biologically plausible spiking neural networks models on the POEtic tissue

Recent experimental findings appear to confirm that the nature of the states governing synaptic plasticity is discrete rather than continuous. This means that learning models based on discrete dynamics have more chances to provide a ground basis for modelling the underlying mechanisms associated with plasticity processes in the brain. In this paper we shall present the physical implementation of a learning model for Spiking Neural Networks (SNN) that is based on discrete learning variables. After optimizing the model to facilitate its hardware realization it is physically mapped on the POEtic tissue, a flexible hardware platform for the implementation of bio-inspired models. The implementation estimates obtained show that is possible to conceive a large-scale implementation of the model able to handle real-time visual recognition tasks.

Effect of Feedback Strength in Coupled Spiking Neural Networks

We simulated the coupling of two large spiking neural networks (104units each) composed by 80% of excitatory units and 20% of inhibitory units, randomly connected by projections featuring spike-timing dependent plasticity, locality preference and synaptic pruning. Only the first network received a complex spatiotemporal stimulus and projected on the second network, in a setup akin to coupled semiconductor lasers. In a series of simulations, the strength of the feedback from the second network to the first was modified to evaluate the effect of the bidirectional coupling on the firing dynamics of the two networks. We observed that, unexpectedly, the number of neurons which activity is altered by the introduction of feedback increases in the second network more than in the first network, suggesting a qualitative change in the dynamics of the first network when feedback is increased.