STDP in Recurrent Neuronal Networks

Recent results about spike-timing-dependent plasticity (STDP) in recurrently connected neurons are reviewed, with a focus on the relationship between the weight dynamics and the emergence of network structure. In particular, the evolution of synaptic weights in the two cases of incoming connections for a single neuron and recurrent connections are compared and contrasted. A theoretical framework is used that is based upon Poisson neurons with a temporally inhomogeneous firing rate and the asymptotic distribution of weights generated by the learning dynamics. Different network configurations examined in recent studies are discussed and an overview of the current understanding of STDP in recurrently connected neuronal networks is presented

Dendritic and Axonal Propagation Delays May Shape Neuronal Networks With Plastic Synapses

Biological neuronal networks are highly adaptive and plastic. For instance, spike-timing-dependent plasticity (STDP) is a core mechanism which adapts the synaptic strengths based on the relative timing of pre- and postsynaptic spikes. In various fields of physiology, time delays cause a plethora of biologically relevant dynamical phenomena. However, time delays increase the complexity of model systems together with the computational and theoretical analysis burden. Accordingly, in computational neuronal network studies propagation delays were often neglected. As a downside, a classic STDP rule in oscillatory neurons without propagation delays is unable to give rise to bidirectional synaptic couplings, i.e., loops or uncoupled states. This is at variance with basic experimental results. In this mini review, we focus on recent theoretical studies focusing on how things change in the presence of propagation delays. Realistic propagation delays may lead to the emergence of neuronal activity and synaptic connectivity patterns, which cannot be captured by classic STDP models. In fact, propagation delays determine the inventory of attractor states and shape their basins of attractions. The results reviewed here enable to overcome fundamental discrepancies between theory and experiments. Furthermore, these findings are relevant for the development of therapeutic brain stimulation techniques aiming at shifting the diseased brain to more favorable attractor states

The Timing of Vision – How Neural Processing Links to Different Temporal Dynamics

In this review, we describe our recent attempts to model the neural correlates of visual perception with biologically inspired networks of spiking neurons, emphasizing the dynamical aspects. Experimental evidence suggests distinct processing modes depending on the type of task the visual system is engaged in. A first mode, crucial for object recognition, deals with rapidly extracting the glimpse of a visual scene in the first 100 ms after its presentation. The promptness of this process points to mainly feedforward processing, which relies on latency coding, and may be shaped by spike timing-dependent plasticity (STDP). Our simulations confirm the plausibility and efficiency of such a scheme. A second mode can be engaged whenever one needs to perform finer perceptual discrimination through evidence accumulation on the order of 400 ms and above. Here, our simulations, together with theoretical considerations, show how predominantly local recurrent connections and long neural time-constants enable the integration and build-up of firing rates on this timescale. In particular, we review how a non-linear model with attractor states induced by strong recurrent connectivity provides straightforward explanations for several recent experimental observations. A third mode, involving additional top-down attentional signals, is relevant for more complex visual scene processing. In the model, as in the brain, these top-down attentional signals shape visual processing by biasing the competition between different pools of neurons. The winning pools may not only have a higher firing rate, but also more synchronous oscillatory activity. This fourth mode, oscillatory activity, leads to faster reaction times and enhanced information transfers in the model. This has indeed been observed experimentally. Moreover, oscillatory activity can format spike times and encode information in the spike phases with respect to the oscillatory cycle. This phenomenon is referred to as “phase-of-firing coding,” and experimental evidence for it is accumulating in the visual system. Simulations show that this code can again be efficiently decoded by STDP. Future work should focus on continuous natural vision, bio-inspired hardware vision systems, and novel experimental paradigms to further distinguish current modeling approaches

Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) with asymmetric learning windows is commonly found in the brain and useful for a variety of spike-based computations such as input filtering and associative memory. A natural consequence of STDP is establishment of causality in the sense that a neuron learns to fire with a lag after specific presynaptic neurons have fired. The effect of STDP on synchrony is elusive because spike synchrony implies unitary spike events of different neurons rather than a causal delayed relationship between neurons. We explore how synchrony can be facilitated by STDP in oscillator networks with a pacemaker. We show that STDP with asymmetric learning windows leads to self-organization of feedforward networks starting from the pacemaker. As a result, STDP drastically facilitates frequency synchrony. Even though differences in spike times are lessened as a result of synaptic plasticity, the finite time lag remains so that perfect spike synchrony is not realized. In contrast to traditional mechanisms of large-scale synchrony based on mutual interaction of coupled neurons, the route to synchrony discovered here is enslavement of downstream neurons by upstream ones. Facilitation of such feedforward synchrony does not occur for STDP with symmetric learning windows.Comment: 9 figure

Oscillatory mechanisms for controlling information flow in neural circuits

Mammalian brains generate complex, dynamic structures of oscillatory activity, in which distributed regions transiently engage in coherent oscillation, often at specific stages in behavioural or cognitive tasks. Much is now known about the dynamics underlying local circuit synchronisation and the phenomenology of where and when such activity occurs. While oscillations have been implicated in many high level processes, for most such phenomena we cannot say with confidence precisely what they are doing at an algorithmic or implementational level. This thesis presents work towards understanding the dynamics and possible function of large scale oscillatory network activity. We first address the question of how coherent oscillatory activity emerges between local networks by measuring phase response curves of an oscillating network in vitro. The network phase response curves provide mechanistic insight into inter-region synchronisation of local network oscillators. Highly simplified firing models are shown to reproduce the experimental data with remarkable accuracy. We then focus on one hypothesised computational function of network oscillations; flexibly controlling the gain of signal flow between anatomically connected networks. We investigate coding strategies and algorithmic operations that support flexible control of signal flow by oscillations, and their implementation by network dynamics. We identify two readout algorithms which selectively recover population rate coded signal with specific oscillatory modulations while ignoring other distracting inputs. By designing a spiking network model that implements one of these mechanisms, we demonstrate oscillatory control of signal flow in convergent pathways. We then investigate constraints on the structures of oscillatory activity that can be used to accurately and selectively control signal flow. Our results suggest that for inputs to be accurately distinguished from one another their oscillatory modulations must be close to orthogonal. This has implications for interpreting in vivo oscillatory activity, and may be an organising principle for the spatio-temporal structure of brain oscillations

The Role of Rhythmic Neural Synchronization in Rest and Task Conditions

Rhythmic neural synchronization is found throughout the brain during many different tasks and even at rest. Beyond their underlying mechanisms, the question of their role is still controversial. Modeling can bring insight on this difficult question. We review here our recent modeling results concerning this issue in different situations. During rest, we show how local rhythmic synchrony can induce a spatiotemporally organized spontaneous activity at the brain level. Then, we show how rhythmic synchrony decreases reaction time in attention and enhances the strength and speed of information transfer between different groups of neurons. Finally, we show that when rhythmic synchrony creates firing phases, the learning with spike timing-dependent plasticity of repeatedly presented input patterns is greatly enhanced

Functional Properties of Striatal Fast-Spiking Interneurons

Striatal fast-spiking interneurons (FSIs) have a major influence over behavioral output, and a deficit in these cells has been observed in dystonia and Tourette syndrome. FSIs receive cortical input, are coupled together by gap junctions, and make perisomatic GABAergic synapses onto many nearby projection neurons. Despite being critical components of striatal microcircuits, until recently little was known about FSI activity in behaving animals. Striatal FSIs are near-continuously active in awake rodents, but even neighboring FSIs show uncorrelated activity most of the time. A coordinated “pulse” of increased FSI firing occurs throughout striatum when rats initiate one chosen action while suppressing a highly trained alternative. This pulse coincides with a drop in globus pallidus population activity, suggesting that pallidostriatal disinhibition may have a important role in timing or coordinating action execution. In addition to changes in firing rate, FSIs show behavior-linked modulation of spike timing. The variability of inter-spike intervals decreases markedly following instruction cues, and FSIs also participate in fast striatal oscillations that are linked to rewarding events and dopaminergic drugs. These studies have revealed novel and unexpected properties of FSIs, that should help inform new models of striatal information processing in both normal and aberrant conditions

Information processing in a midbrain visual pathway

Visual information is processed in brain via the intricate interactions between neurons. We investigated a midbrain visual pathway: optic tectum and its isthmic nucleus) that is motion sensitive and is thought as part of attentional system. We determined the physiological properties of individual neurons as well as their synaptic connections with intracellular recordings. We reproduced the center-surround receptive field structure of tectal neurons in a dynamical recurrent feedback loop. We reveal in a computational model that the anti-topographic inhibitory feedback could mediate competitive stimulus selection in a complex visual scene. We also investigated the dynamics of the competitive selection in a rate model. The isthmotectal feedback loop gates the information transfer from tectum to thalamic rotundus. We discussed the role of a localized feedback projection in contributing to the gating mechanisms with both experimental and numerical approaches. We further discussed the dynamics of the isthmotectal system by considering the propagation delays between different components. We conclude that the isthmotectal system is involved in attention-like competitive stimulus selection and control the information coding in the motion sensitive SGC-I neurons by modulating the retino-tectal synaptic transmission