Network Model Of Spiking Neurons Research Articles

Macroscopic neural mass model constructed from a current-based network model of spiking neurons.

Neural mass models (NMMs) are efficient frameworks for describing macroscopic cortical dynamics including electroencephalogram and magnetoencephalogram signals. Originally, these models were formulated on an empirical basis of synaptic dynamics with relatively long time constants. By clarifying the relations between NMMs and the dynamics of microscopic structures such as neurons and synapses, we can better understand cortical and neural mechanisms from a multi-scale perspective. In a previous study, the NMMs were analytically derived by averaging the equations of synaptic dynamics over the neurons in the population and further averaging the equations of the membrane-potential dynamics. However, the averaging of synaptic current assumes that the neuron membrane potentials are nearly time invariant and that they remain at sub-threshold levels to retain the conductance-based model. This approximation limits the NMM to the non-firing state. In the present study, we newly propose a derivation of a NMM by alternatively approximating the synaptic current which is assumed to be independent of the membrane potential, thus adopting a current-based model. Our proposed model releases the constraint of the nearly constant membrane potential. We confirm that the obtained model is reducible to the previous model in the non-firing situation and that it reproduces the temporal mean values and relative power spectrum densities of the average membrane potentials for the spiking neurons. It is further ensured that the existing NMM properly models the averaged dynamics over individual neurons even if they are spiking in the populations.

Fundamental Activity Constraints Lead to Specific Interpretations of the Connectome.

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function.

On the applicability of STDP-based learning mechanisms to spiking neuron network models

The ways to creating practically effective method for spiking neuron networks learning, that would be appropriate for implementing in neuromorphic hardware and at the same time based on the biologically plausible plasticity rules, namely, on STDP, are discussed. The influence of the amount of correlation between input and output spike trains on the learnability by different STDP rules is evaluated. A usability of alternative combined learning schemes, involving artificial and spiking neuron models is demonstrated on the iris benchmark task and on the practical task of gender recognition.

Effective Suppression of Pathological Synchronization in Cortical Networks by Highly Heterogeneous Distribution of Inhibitory Connections.

Even without external random input, cortical networks in vivo sustain asynchronous irregular firing with low firing rate. In addition to detailed balance between excitatory and inhibitory activities, recent theoretical studies have revealed that another feature commonly observed in cortical networks, i.e., long-tailed distribution of excitatory synapses implying coexistence of many weak and a few extremely strong excitatory synapses, plays an essential role in realizing the self-sustained activity in recurrent networks of biologically plausible spiking neurons. The previous studies, however, have not considered highly non-random features of the synaptic connectivity, namely, bidirectional connections between cortical neurons are more common than expected by chance and strengths of synapses are positively correlated between pre- and postsynaptic neurons. The positive correlation of synaptic connections may destabilize asynchronous activity of networks with the long-tailed synaptic distribution and induce pathological synchronized firing among neurons. It remains unclear how the cortical network avoids such pathological synchronization. Here, we demonstrate that introduction of the correlated connections indeed gives rise to synchronized firings in a cortical network model with the long-tailed distribution. By using a simplified feed-forward network model of spiking neurons, we clarify the underlying mechanism of the synchronization. We then show that the synchronization can be efficiently suppressed by highly heterogeneous distribution, typically a lognormal distribution, of inhibitory-to-excitatory connection strengths in a recurrent network model of cortical neurons.

Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task.

Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a “Stop” process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception.

The role of horizontal connections for the modulation of border-ownership selective neurons in visual cortex

Border-ownership selectivity (BOS) codes for the direction of a foreground object relative to the background [1]. Attention modulates the responses of BOS neurons in cortical areas V2, both for the firing rate [2] and in terms of spike synchrony which can occur over long cortical distances [3]. Here, we develop a network model of spiking neurons based on dis-inhibitory feedback [4]. Specifically, we consider the potential influence of intra-areal (horizontal) connections for attention-induced modulation of BOS neurons in visual cortex. In our model (Figure ​(Figure1),1), horizontal connections (oblique lines) connect excitatory BOS (EBO) neurons representing one part of a figure to feed-forward inhibitory (FFI) neurons representing another figure part. Since horizontal connections are weakly myelinated or non-myelinated, signal propagation along them is slow and interactions through them are subject to substantial delays, roughly proportional to the distance between the neurons they connect. We therefore investigated the influence of communication delays on spike-spike synchrony between BOS neurons. Our simulation results of the network with short delays are in agreement with experimental data, showing attentional enhancement of firing rates of EBO neurons and, at the same time, reduction of spike-spike synchrony. In contrast, simulations with more realistic (longer) delays could not reproduce experimental results. Our results suggest that horizontal connections in early cortical areas cannot explain the observed synchrony structure between BOS neurons and are unlikely to form the substrate of figure-ground segregation. Figure 1 Circuit of the dis-inhibition model. The network was organized into 2 identical microcircuit units and consisted of 4 types of neurons [4]: excitatory BOS neurons (triangles, EBO), excitatory grouping neurons (hexagon, G), feedforward inhibitory neurons ...

A spiking neuron network model for the delayed motion direction discrimination task

Drawing the correct decision in a given situation usually depends on past experiences. While the neural basis of decision making per se has been studied extensively, how a memory of a previous situation could influence a later decision is less clear. One behavioral paradigm for investigating decision making in relation to an earlier reference is the delayed motion direction discrimination task (DMD) [1]. In this task, a variable reference direction is first presented to the subject by displaying a coherent random dot kinematograms (RDK). After memorizing, a delayed presentation of the test direction (RDK) follows and the subject needs to report whether the test direction is clockwise (CW) or anticlockwise (ACW) rotated in relation to the reference direction (Figure ​(Figure1A).1A). Although phenomenological models have been proposed, the underlying neural mechanism remains relatively unknown. Here, we hypothesize that a mechanism based on the idea of asymmetric connections similar to those used in neural networks explaining angular path integration in the head-direction system [2], might explain the behavioral results. Figure 1 A. DMD task: either ACW or CW has to be reported in relation to the reference. B. Network structure. Pyramid cells of WMC (CPC and APC) are arranged on a circle according to their preferred motion directions and with rotation invariant Gaussian shaped ... To test this hypothesis, we build a spiking neuron network model and investigate whether it can solve the DMD task. The network consists of three parts (Figure ​(Figure1.B):1.B): 1) a working memory circuit (WMC) 2) two information extraction circuits, referred to as clockwise-preferred circuit (CPC) and anticlockwise-preferred circuit (APC) respectively, and 3) a decision-making circuit. At the core of the network is the assumption of an asymmetric offset and rotational invariance of the connectivity profile. The former ensures that CPC (or APC) has stronger response when the test direction is CW (or ACW) rotated with respect to the reference direction and the latter guarantees the feasibility of the network under variable references. The simulations demonstrate that the proposed network is indeed capable of solving the DMD task (Figure ​(Figure1.C)1.C) showing a similar detection threshold (2°) as previous behavioral data [3]. Furthermore, the model correctly predicts that with higher similarity of reference and test direction or lower coherence level of the RDK, the performance gets worse (lower accuracy and longer reaction time). Our results suggest a possible neural mechanism for memory-guided decision making.

More flexibility for code generation with GeNN v2.1

Background GeNN (GPU enhanced Neuronal Networks) [1,2] is a software framework that was designed to facilitate the use of GPUs (Graphics Processing Units) for the simulation of spiking neuronal networks. It is built on top of the CUDA (Common Unified Device Architecture) [3] application programming interface provided by NVIDIA Corporation and is entirely based on code generation: Users provide a compact description of a spiking neuronal network model and GeNN generates CUDA and C++ code to simulate it, also taking into account the specifics of the GPU hardware detected at compile time.

Computational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activity.

Epileptic seizure dynamics span multiple scales in space and time. Understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. Mathematical models have been developed to reproduce seizure dynamics across scales ranging from the single neuron to the neural population. In this study, we develop a network model of spiking neurons and systematically investigate the conditions, under which the network displays the emergent dynamic behaviors known from the Epileptor, which is a well-investigated abstract model of epileptic neural activity. This approach allows us to study the biophysical parameters and variables leading to epileptiform discharges at cellular and network levels. Our network model is composed of two neuronal populations, characterized by fast excitatory bursting neurons and regular spiking inhibitory neurons, embedded in a common extracellular environment represented by a slow variable. By systematically analyzing the parameter landscape offered by the simulation framework, we reproduce typical sequences of neural activity observed during status epilepticus. We find that exogenous fluctuations from extracellular environment and electro-tonic couplings play a major role in the progression of the seizure, which supports previous studies and further validates our model. We also investigate the influence of chemical synaptic coupling in the generation of spontaneous seizure-like events. Our results argue towards a temporal shift of typical spike waves with fast discharges as synaptic strengths are varied. We demonstrate that spike waves, including interictal spikes, are generated primarily by inhibitory neurons, whereas fast discharges during the wave part are due to excitatory neurons. Simulated traces are compared with in vivo experimental data from rodents at different stages of the disorder. We draw the conclusion that slow variations of global excitability, due to exogenous fluctuations from extracellular environment, and gap junction communication push the system into paroxysmal regimes. We discuss potential mechanisms underlying such machinery and the relevance of our approach, supporting previous detailed modeling studies and reflecting on the limitations of our methodology.

Physiology and Impact of Horizontal Connections in Rat Neocortex.

Cortical information processing at the cellular level has predominantly been studied in local networks, which are dominated by strong vertical connectivity between layers. However, recent studies suggest that the bulk of axons targeting pyramidal neurons most likely originate from outside this local range, emphasizing the importance of horizontal connections. We mapped a subset of these connections to L5B pyramidal neurons in rat somatosensory cortex with photostimulation, identifying intact projections up to a lateral distance of 2 mm. Our estimates of the spatial distribution of cells presynaptic to L5B pyramids support the idea that the majority is located outside the local volume. The synaptic physiology of horizontal connections does not differ markedly from that of local connections, whereas the layer and cell-type-dependent pattern of innervation does. Apart from L2/3, L6A provides a strong source of horizontal connections. Implementing our data into a spiking neuronal network model shows that more horizontal connections promote robust asynchronous ongoing activity states and reduce noise correlations in stimulus-induced activity.

Local and long-range phase-amplitude coupling in a cortical spiking network model

Phase-amplitude coupling (PAC) between neural oscillations in different frequency bands has been observed in population recordings of the hippocampus and neocortex [1,2]. Specifically, bursts of gamma (>30 Hz) oscillations are often found to be phase-locked to a slower oscillation in the theta range (4-8 Hz), raising questions about the mechanisms and computational consequences of this pattern. Recently, inter-areal gamma synchronization was also found to be systematically modulated by theta rhythms [3]. We study mechanisms for these experimental findings and their potential role in long-range inter-areal coupling using spiking neuron network models. Using dynamical systems theory, bursting has previously been analyzed in detail for single neuron dynamics [4], requiring interaction of fast and slow adapting variables, driving the neuron through alternations of active (limit cycle) and resting (stable equilibrium) periods. On the population level, bursting can be studied within the same framework, treating average synaptic activity and time courses as slow/fast variables. Our model (Panel A) is based on observations of dynamically distinct interneuron populations in the cortex, namely fast-spiking, non-adapting (FS) and adapting interneurons (low-threshold spiking, LTS) exhibiting fast and slow synaptic time courses [5]. Coupled with a population of principal cells (regular spiking, RS), this network displays spontaneous phase-amplitude coupling as sparsely synchronized population bursts. Feed-forward connections between populations of this type lead to theta modulated gamma synchronization (Panel B and C). We study in detail the parameter range/bifurcations of this model. Considering that in-vivo neural circuits are generally only sparsely synchronized, we contrast oscillatory patterns on the population level with stochastic firing of single neurons. Figure 1 Local and long-range phase-amplitude coupling in a cortical spiking network model A) Schematic showing two populations of 1,000 spiking neurons and connections on the population level. Intra-/inter-areal connections are shown in black/blue respectively. ...

Energy landscapes of resting-state brain networks

During rest, the human brain performs essential functions such as memory maintenance, which are associated with resting-state brain networks (RSNs) including the default-mode network (DMN) and frontoparietal network (FPN). Previous studies based on spiking-neuron network models and their reduced models, as well as those based on imaging data, suggest that resting-state network activity can be captured as attractor dynamics, i.e., dynamics of the brain state toward an attractive state and transitions between different attractors. Here, we analyze the energy landscapes of the RSNs by applying the maximum entropy model, or equivalently the Ising spin model, to human RSN data. We use the previously estimated parameter values to define the energy landscape, and the disconnectivity graph method to estimate the number of local energy minima (equivalent to attractors in attractor dynamics), the basin size, and hierarchical relationships among the different local minima. In both of the DMN and FPN, low-energy local minima tended to have large basins. A majority of the network states belonged to a basin of one of a few local minima. Therefore, a small number of local minima constituted the backbone of each RSN. In the DMN, the energy landscape consisted of two groups of low-energy local minima that are separated by a relatively high energy barrier. Within each group, the activity patterns of the local minima were similar, and different minima were connected by relatively low energy barriers. In the FPN, all dominant local minima were separated by relatively low energy barriers such that they formed a single coarse-grained global minimum. Our results indicate that multistable attractor dynamics may underlie the DMN, but not the FPN, and assist memory maintenance with different memory states.

Combining spiking neuronal network model with presynaptic and astrocyte interface models

Combining spiking neuronal network model with presynaptic and astrocyte interface models

How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest

In recent years the study of the intrinsic brain dynamics in a relaxed awake state in the absence of any specific task has gained increasing attention, as spontaneous neural activity has been found to be highly structured at a large scale. This so called resting-state activity has been found to be comprised by nonrandom spatiotemporal patterns and fluctuations, and several Resting-State Networks (RSN) have been found in BOLD-fMRI as well as in MEG signal power envelope correlations. The underlying anatomical connectivity structure between areas of the brain has been identified as being a key to the observed functional network connectivity, but the mechanisms behind this are still underdetermined. Theoretical large-scale brain models for fMRI data have corroborated the importance of the connectome in shaping network dynamics, while the importance of delays and noise differ between studies and depend on the models' specific dynamics. In the current study, we present a spiking neuron network model that is able to produce noisy, distributed alpha-oscillations, matching the power peak in the spectrum of group resting-state MEG recordings. We studied how well the model captured the inter-node correlation structure of the alpha-band power envelopes for different delays between brain areas, and found that the model performs best for propagation delays inside the physiological range (5–10m/s). Delays also shift the transition from noisy to bursting oscillations to higher global coupling values in the model. Thus, in contrast to the asynchronous fMRI state, delays are important to consider in the presence of oscillation.

Dynamics and lifetime of persistent activity states in random networks of spiking neurons with strong synapses

The selective persistent activation of populations of neurons in the prefrontal cortex during active memory tasks is one of the best-studied neural correlates of a higher cognitive function, the so-called working memory. Yet, the respective roles of network and neuron properties in this activity are not fully understood. Spiking neuron network models often rely on a selective excitatory connectivity and generate spike trains that are more regular than what is experimentally observed, thus necessitating additional assumptions, such as intrinsic cellular bistability or additional external noisy input. Recently, it was shown that very simple random networks of excitatory and inhibitory spiking neurons can generate persistent asynchronous-irregular activity only by admitting a certain fraction of comparably strong synaptic weights [1,2]. A large number of experiments showed that synaptic weight distributions are indeed commonly characterized by a large fraction of weak weights and a heavy tail of quite strong weights [e.g. 2,3]. Here, we demonstrate how including such strong synaptic couplings leads to the occurrence of a firing-rate attractor at moderate rates with highly irregular individual spike trains. Based on the firing-rate transfer of the network, we show that the firing-rate dynamics becomes bistable if the synapses are sufficiently strong: in addition to the quiescent state, a second stable fixed point at moderate firing rates emerges by a saddle-node bifurcation. However, the population firing-rate is characterized by pronounced inherent fluctuations that perpetually perturb this fixed point. It is the trade-off between the size of the population fluctuations and the size of the basin of attraction that thus determines the onset and lifetime of persistent activity states. Moreover, individual neuronal activity turns out to be very irregular, switching between long periods of low firing rate to short burst-like states. We show that this is an effect of the strong coupling strength in the network combined with the finite memory time constant of the neurons. Thus, such irregular neuron dynamics can be a pure network phenomenon, and do not require intracellular bistability or additional high-variability noise as previously suggested.

A model of perceptual discrimination under sequential sensory evidence

Perceptual discrimination may be interpreted as a decision between alternatives based on available sensory evidence. In many experiments, the different alternatives are encoded by quite distinct neuronal groups. In this case, proposed neural models consider that the decision results from the competition between decision-specific neuronal groups, each of these integrating distinct sensory evidence [1]. Alternatively, evidence may be presented in a sequential manner, and the different stimuli may be encoded by the same neuronal group, as exemplified by experiments where monkeys are engaged in a vibrotactile discrimination task [2]. To achieve discrimination in this case, the nervous system needs to keep a trace of the previously presented stimuli. How the correct discrimination can be learned and implemented is poorly understood. To address these questions, we use a modeling approach. We concentrate on the particular case of the vibrotactile discrimination task on which experimental insight has been accumulated over the last years [2]. The partial differential (PD) neurons in monkey area VPC, encoding both sequentially presented vibrotactile stimuli (with frequencies f1 and f2) by keeping the memory of the first one during a delay period, have been reproduced in a spiking neuron network model with short-term facilitating synapses [3]. We want to explore how these PD neurons may be used to discriminate between both stimuli configurations: f1 > f2 or f1 < f2. Based on the experimental evidence, we model a heterogeneous PD neuronal population, encoding both frequencies in multiple ways. Downstream to the first network, we add a competition-based decision making spiking neuron network [1]. To obtain the desired neural dynamics of the coupled two-networks model, we choose the model parameters using a simplified mean-field description of the model. To make the best possible decisions, the strengths of the synapses projecting from the PD neurons to the decision neurons must be learned. Based on reinforcement learning theory, we use a learning rule which maximizes reward. It depends on a reward prediction error, evaluated using the reward history. We instantiate this rule using a reward based spike-timing-dependent plasticity [4]. Learning takes place after the second stimulus presentation. We find that the task can be efficiently learned for any number of PD neuron types, even when their stimulus encoding function is nonlinear and noisy. In conclusion, the present biologically plausible two-networks model is able to solve a perceptual discrimination task under sequential sensory evidence.

P.2.013 Modelling serotonergic neuromodulation of behavioural performance in spatial working memory

The executive functions of prefrontal cortex (PFC) are regulated by ascending monoaminergic systems. In particular, the dorsal PFC is associated with spatial working memory (SWM), linked to persistent neuronal activity, and it receives anatomical projections from the brainstem monoamine centres. Catecholamine regulation of SWM has been studied at mechanistic and computational levels. However, the role of serotonin (5-HT) in SWM is unclear. Thus, while 5-HT is clearly associated with orbitofrontal cortex functions, its association with SWM is controversial, with positive and negative reports. An indirect association between PFC 5-HT and SWM is suggested by the pro-cognitive effects of antipsychotic drugs targeting 5-HT receptors. Several neurocognitive problems are core deficits of schizophrenia and SWM deficits mark a genetic predisposition for the disease. These weak associations between 5-HT and SWM are in contrast with neurobiological observations predicting a stronger link. Indeed, 5-HT receptors are densely expressed in various PFC subfields, and serotonergic projections from the raphe nuclei target specifically PFC and modulate neuronal activity. Moreover, 5-HT2A receptor activation/blockade in dorsal PFC reduces persistent neuronal activity during SWM tasks. Here, we sought to develop a computational model of serotonergic modulation of the PFC network in order to address the possible reasons for this discrepancy. We used a computational network model of spiking neurons [1] which is consistent with PFC neurophysiological data from behaving monkeys in SWM. This model falls within the general class of attractor models proposed to account for behavioural deficits of schizophrenia, and provides a rich set of behavioural output predictions that can be experimentally tested. We incorporated 5-HT receptor mechanisms in the model following electrophysiological evidence, and we tested the effects of 5-HT receptor ligands on network simulation performance during SWM. We found that 5-HT modulated network’s SWM performance non-monotonously via 5-HT2A and especially via 5-HT1A receptors, following an inverted-U shape. This may partly explain the weak behavioural effects of serotonergic agents in previous SWM studies. Our simulations showed that errors committed at low and high 5-HT concentrations are due to different network dynamics instabilities, suggesting that these two conditions can be distinguished experimentally based on their dependency with delay length, on the response confidence declared in error trials and on PFC activation contrasts in neuroimaging studies. We derived specific predictions regarding the expected behavioural effects of serotonin agents in two classic working-memory tasks: an oculomotor delayed-response task and a delayed matchto-sample task. In brief, the probability of memory storage in the oculomotor task and the rate of misses in the match-to-sample task will be most sensitive to serotonin modulation, depending on delay duration for increases but not for decreases in baseline 5-HT. Our study underscores the relevance of identifying different error types in SWM tasks in order to reveal the association between neuromodulatory systems and SWM. Testing these model-derived predictions in electrophysiological and psychophysical studies of SWM will help advance our understanding of the neural basis of SWM and its neuromodulation by 5-HT receptors.

Associative memory model with long-tail-distributed Hebbian synaptic connections

The postsynaptic potentials of pyramidal neurons have a non-Gaussian amplitude distribution with a heavy tail in both hippocampus and neocortex. Such distributions of synaptic weights were recently shown to generate spontaneous internal noise optimal for spike propagation in recurrent cortical circuits. However, whether this internal noise generation by heavy-tailed weight distributions is possible for and beneficial to other computational functions remains unknown. To clarify this point, we construct an associative memory (AM) network model of spiking neurons that stores multiple memory patterns in a connection matrix with a lognormal weight distribution. In AM networks, non-retrieved memory patterns generate a cross-talk noise that severely disturbs memory recall. We demonstrate that neurons encoding a retrieved memory pattern and those encoding non-retrieved memory patterns have different subthreshold membrane-potential distributions in our model. Consequently, the probability of responding to inputs at strong synapses increases for the encoding neurons, whereas it decreases for the non-encoding neurons. Our results imply that heavy-tailed distributions of connection weights can generate noise useful for AM recall.

Network properties of a computational model of the dorsal raphe nucleus

Serotonin (5-HT) plays an important role in regulating mood, cognition and behaviour. The midbrain dorsal raphe nucleus (DRN) is one of the primary sources of 5-HT. Recent studies show that DRN neuronal activities can encode rewarding (e.g., appetitive) and unrewarding (e.g., aversive) behaviours. Experiments have also shown that DRN neurons can exhibit heterogeneous spiking behaviours. In this work, we build and study a basic spiking neuronal network model of the DRN constrained by neuronal properties observed in experiments. We use an efficient adaptive quadratic integrate-and-fire neuronal model to capture slow afterhyperpolarization current, occasional bursting behaviours in 5-HT neurons, and fast spiking activities in the non-5-HT inhibitory neurons. Provided that our noisy and heterogeneous spiking neuronal network model adopts a feedforward inhibitory network architecture, it is able to replicate the main features of DRN neuronal activities recorded in monkeys performing a reward-based memory-guided saccade task. The model exhibits theta band oscillation, especially among the non-5-HT inhibitory neurons during the rewarding outcome of a simulated trial, thus forming a model prediction. By varying the inhibitory synaptic strengths and the afferent inputs, we find that the network model can oscillate over a range of relatively low frequencies, allow co-existence of multiple stable frequencies, and spike synchrony can spread from within a local neural subgroup to global. Our model suggests plausible network architecture, provides interesting model predictions that can be experimentally tested, and offers a sufficiently realistic multi-scale model for 5-HT neuromodulation simulations.

Contrast invariant feature selectivity in balanced random networks

Event Abstract Back to Event Contrast invariant feature selectivity in balanced random networks Sadra Sadeh1*, Stefano Cardanobile1 and Stefan Rotter1 1 Bernstein Center Freiburg & Faculty of Biology, University of Freiburg, Germany Neurons in the primary visual cortex (V1) of mammals are highly selective for the orientation of a light bar [1, 2, 3], while they receive afferent connections from neurons in lateral geniculate nucleus (LGN) which are themselves not selective. The emergence of this property in cortex has been subject of debate since decades, and served as a paradigm to unravel the key mechanisms of information processing in the brain. We investigate the problem from a network point of view, by focusing on random recurrent networks with no specific pattern of connectivity. By exploring a large-scale network model of spiking neurons through simulations and theoretical analysis, we show how a purely random network operating in the inhibition dominated regime contributes to feature selectivity. In particular, we demonstrate how tuning amplification can happen in these networks as a consequence of 'selective attenuation', a general mechanism which selectively suppresses the common mode. By systematically investigating the relevant parameter space, we pinpoint different regimes of orientation selectivity, which yields testable predictions for the biological cortex. The 'selective attenuation' mechanism also yields contrast invariance [4, 3] of feature selectivity, which ensures feature detection for a wide range of input scales. Therefore, we suggest that the basic mechanism of contrast invariance is a consequence of intracortical interactions, and neither a single cell property, nor a purely feedforward mechanism. We argue in favor of 'tuning amplification' as the key process of the recurrent processing stage, for which no specific structure is needed. This mechanism could, in principle, also work for other sensory features and other sensory modalities as well, since the neural hardware necessary to achieve it is readily available in the cortex. Acknowledgements Funding by the German Ministry of Education and Research (BCCN Freiburg, grant 01GQ0420 and BFNT Freiburg*Tubingen, grant 01GQ0830) is acknowledged.