Excitatory Units Research Articles

Endogenous cannabinoids in the piriform cortex tune olfactory perception

Sensory perception depends on interactions between external inputs transduced by peripheral sensory organs and internal network dynamics generated by central neuronal circuits. In the sensory cortex, desynchronized network states associate with high signal-to-noise ratio stimulus-evoked responses and heightened perception. Cannabinoid-type-1-receptors (CB1Rs) - which influence network coordination in the hippocampus - are present in anterior piriform cortex (aPC), a sensory paleocortex supporting olfactory perception. Yet, how CB1Rs shape aPC network activity and affect odor perception is unknown. Using pharmacological manipulations coupled with multi-electrode recordings or fiber photometry in the aPC of freely moving male mice, we show that systemic CB1R blockade as well as local drug infusion increases the amplitude of gamma oscillations in aPC, while simultaneously reducing the occurrence of synchronized population events involving aPC excitatory neurons. In animals exposed to odor sources, blockade of CB1Rs reduces correlation among aPC excitatory units and lowers behavioral olfactory detection thresholds. These results suggest that endogenous endocannabinoid signaling promotes synchronized population events and dampen gamma oscillations in the aPC which results in a reduced sensitivity to external sensory inputs.

Diverse role of NMDA receptors for dendritic integration of neural dynamics.

Neurons, represented as a tree structure of morphology, have various distinguished branches of dendrites. Different types of synaptic receptors distributed over dendrites are responsible for receiving inputs from other neurons. NMDA receptors (NMDARs) are expressed as excitatory units, and play a key physiological role in synaptic function. Although NMDARs are widely expressed in most types of neurons, they play a different role in the cerebellar Purkinje cells (PCs). Utilizing a computational PC model with detailed dendritic morphology, we explored the role of NMDARs at different parts of dendritic branches and regions. We found somatic responses can switch from silent, to simple spikes and complex spikes, depending on specific dendritic branches. Detailed examination of the dendrites regarding their diameters and distance to soma revealed diverse response patterns, yet explain two firing modes, simple and complex spike. Taken together, these results suggest that NMDARs play an important role in controlling excitability sensitivity while taking into account the factor of dendritic properties. Given the complexity of neural morphology varying in cell types, our work suggests that the functional role of NMDARs is not stereotyped but highly interwoven with local properties of neuronal structure.

Disruption of layer-specific visual processing in a model of focal neocortical epilepsy.

The epileptic brain is the result of a sequence of events transforming normal neuronal populations into hyperexcitable networks supporting recurrent seizure generation. These modifications are known to induce fundamental alterations of circuit function and, ultimately, of behavior. However, how hyperexcitability affects information processing in cortical sensory circuits is not yet fully understood. Here, we investigated interlaminar alterations in sensory processing of the visual cortex in a mouse model of focal epilepsy. We found three main circuit dynamics alterations in epileptic mice: (i) a spreading of visual contrast-driven gamma modulation across layers, (ii) an increase in firing rate that is layer-unspecific for excitatory units and localized in infragranular layers for inhibitory neurons, and (iii) a strong and contrast-dependent locking of firing units to network activity. Altogether, our data show that epileptic circuits display a functional disruption of layer-specific organization of visual sensory processing, which could account for visual dysfunction observed in epileptic subjects. Understanding these mechanisms paves the way to circuital therapeutic interventions for epilepsy.

Backtracking activation impacts the criticality of excitable networks

Networks of excitable elements are widely used to model real-world biological and social systems. The dynamic range of an excitable network quantifies the range of stimulus intensities that can be robustly distinguished by the network response, and is maximized at the critical state. In this study, we examine the impacts of backtracking activation on system criticality in excitable networks consisting of both excitatory and inhibitory units. We find that, for dynamics with refractory states that prohibit backtracking activation, the critical state occurs when the largest eigenvalue of the weighted non-backtracking (WNB) matrix for excitatory units, , is close to one, regardless of the strength of inhibition. In contrast, for dynamics without refractory state in which backtracking activation is allowed, the strength of inhibition affects the critical condition through suppression of backtracking activation. As inhibitory strength increases, backtracking activation is gradually suppressed. Accordingly, the system shifts continuously along a continuum between two extreme regimes—from one where the criticality is determined by the largest eigenvalue of the weighted adjacency matrix for excitatory units, , to the other where the critical state is reached when is close to one. For systems in between, we find that and at the critical state. These findings, confirmed by numerical simulations using both random and synthetic neural networks, indicate that backtracking activation impacts the criticality of excitable networks.

Inhibition and Excitation Shape Activity Selection: Effect of Oscillations in a Decision-Making Circuit.

Decision making is a complex task, and its underlying mechanisms that regulate behavior, such as the implementation of the coupling between physiological states and neural networks, are hard to decipher. To gain more insight into neural computations underlying ongoing binary decision-making tasks, we consider a neural circuit that guides the feeding behavior of a hypothetical animal making dietary choices. We adopt an inhibition motif from neural network theory and propose a dynamical system characterized by nonlinear feedback, which links mechanism (the implementation of the neural circuit and its coupling to the animal's nutritional state) and function (improving behavioral performance). A central inhibitory unit influences evidence-integrating excitatory units, which in our terms correspond to motivations competing for selection. We determine the parameter regime where the animal exhibits improved decision-making behavior and explain different behavioral outcomes by making the link between accessible states of the nonlinear neural circuit model and decision-making performance. We find that for given deficits in nutritional items, the variation of inhibition strength and ratio of excitation and inhibition strengths in the decision circuit allows the animal to enter an oscillatory phase that describes its internal motivational state. Our findings indicate that this oscillatory phase may improve the overall performance of the animal in an ongoing foraging task and underpin the importance of an integrated functional and mechanistic study of animal activity selection.

Irrelevance by inhibition: Learning, computation, and implications for schizophrenia.

Symptoms of schizophrenia may arise from a failure of cortical circuits to filter-out irrelevant inputs. Schizophrenia has also been linked to disruptions in cortical inhibitory interneurons, consistent with the possibility that in the normally functioning brain, these cells are in some part responsible for determining which sensory inputs are relevant versus irrelevant. Here, we develop a neural network model that demonstrates how the cortex may learn to ignore irrelevant inputs through plasticity processes affecting inhibition. The model is based on the proposal that the amount of excitatory output from a cortical circuit encodes the expected magnitude of reward or punishment (“relevance”), which can be trained using a temporal difference learning mechanism acting on feedforward inputs to inhibitory interneurons. In the model, irrelevant and blocked stimuli drive lower levels of excitatory activity compared with novel and relevant stimuli, and this difference in activity levels is lost following disruptions to inhibitory units. When excitatory units are connected to a competitive-learning output layer with a threshold, the relevance code can be shown to “gate” both learning and behavioral responses to irrelevant stimuli. Accordingly, the combined network is capable of recapitulating published experimental data linking inhibition in frontal cortex with fear learning and expression. Finally, the model demonstrates how relevance learning can take place in parallel with other types of learning, through plasticity rules involving inhibitory and excitatory components, respectively. Altogether, this work offers a theory of how the cortex learns to selectively inhibit inputs, providing insight into how relevance-assignment problems may emerge in schizophrenia.

A Neurodynamic Model of Feature-Based Spatial Selection.

Huang and Pashler (2007) suggested that feature-based attention creates a special form of spatial representation, which is termed a Boolean map. It partitions the visual scene into two distinct and complementary regions: selected and not selected. Here, we developed a model of a recurrent competitive network that is capable of state-dependent computation. It selects multiple winning locations based on a joint top-down cue. We augmented a model of the WTA circuit that is based on linear-threshold units with two computational elements: dendritic non-linearity that acts on the excitatory units and activity-dependent modulation of synaptic transmission between excitatory and inhibitory units. Computer simulations showed that the proposed model could create a Boolean map in response to a featured cue and elaborate it using the logical operations of intersection and union. In addition, it was shown that in the absence of top-down guidance, the model is sensitive to bottom-up cues such as saliency and abrupt visual onset.

Feedforward architectures driven by inhibitory interactions.

Directed information transmission is paramount for many social, physical, and biological systems. For neural systems, scientists have studied this problem under the paradigm of feedforward networks for decades. In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them, while inhibitory neurons play only a stabilizing role for the network dynamics. Motivated by recent experimental discoveries of hippocampal circuitry, cortical circuitry, and the diversity of inhibitory neurons throughout the brain, here we illustrate that one can construct such networks even if the connectivity between the excitatory units in the system remains random. This is achieved by endowing inhibitory nodes with a more active role in the network. Our findings demonstrate that apparent feedforward activity can be caused by a much broader network-architectural basis than often assumed.

A Simple Network Architecture Accounts for Diverse Reward Time Responses in Primary Visual Cortex.

Heterogeneity in single-neuron responses is a common feature of neuronal systems, although sometimes, in theoretical approaches, it is treated as a nuisance and seldom considered as conveying a different aspect of a signal. In this study, we focus on the heterogeneous responses in the primary visual cortex of rodents trained with a predictable delayed reward time. We describe under what conditions this heterogeneity can arise by self-organization, and what information it can convey. This study, while focusing on a specific system, provides insight onto how heterogeneity can arise in general while also shedding light onto mechanisms of reinforcement learning using realistic biological assumptions.

Response: "Commentary: Comparison of spike parameters from optically identified GABAergic and glutamatergic neurons in sparse cortical cultures".

We are pleased to note that our publication “Comparison of spike parameters from optically identified GABAergic and glutamatergic neurons in sparse cortical cultures” by Weir et al. (2015) raised some discussion on the feasibility of solely electrophysiological discrimination of distinct neuronal subpopulations in vitro. We agree with Becchetti and Wanke (2015) that their report and our study on the same question were conducted with different technical approaches and that this may explain the observed differences between both studies. Although we obviously recorded a reduced spontaneous neuronal activity under our sparse culture conditions, these conditions were necessary to enable the unequivocal identification of single units in recordings with extracellular electrodes. Our combined approach of extracellular single unit recordings and cellular calcium imaging analyses in sparse neuronal cultures from GAD67-GFP transgenic mice clearly allowed the definite identification of inhibitory GAD67-positive interneurons and excitatory GAD67-negative neurons. The ratio of excitatory to inhibitory neurons and thus also the relative density of inhibitory connections was carefully assessed in our study and comparable to data from the cerebral cortex in vivo. For technical reasons this unambiguous assignment of a single neuron to one recording electrode is not possible at high cellular densities. We performed additional experiments and now also analyzed Fano factors of spike counts recorded in culture medium and under different culture conditions (low density culture 15.6 ± 1.8 recorded neurons; medium density culture 54.3 ± 3.1 recorded neurons). Fano factors of these cultures were compared to those of clearly identified GFP-positive, inhibitory and GFP-negative, excitatory units published in our previous study (Weir et al., 2015). Fano factors were calculated for all units by dividing the variation by the average spike counts within a time window of 800 ms. Note, that Becchetti et al. previously used a time window of 6 s for calculation of Fano factors (Becchetti et al., 2012). However, our additional analyses revealed that this methodological difference in the bin width does not account for the different experimental outcome, since Fano factors calculated with 6 s and 800 ms bins were strongly correlated (Low density culture R2 = 0.596, p < 0.0001; High density culture R2 = 0.649, p < 0.0001). As shown in Figure ​Figure11 below, all four experimental groups show a similar distribution of Fano factors and no significant differences in average Fano factor in different conditions (low density cultures 2.91 ± 0.16, n = 204 cells; medium dense cultures 2.6 ± 0.17, n = 152 cells; GFP-negative neurons 2.83 ± 0.3, n = 81 cells: GFP-positive interneurons 3.23 ± 1.03, n = 20 cells; One-Way ANOVA R2 = 0.005, p = 0.54). Single unit recordings at culture conditions with 1000 cells per mm2 as suggested by Becchetti and Wanke are not possible because spike sorting algorithms under this condition revealed a high failure rate. Figure 1 Distribution of Fano factors (calculated as described in Weir et al., 2015) of single units in low and medium dense cultures recorded in culture medium. For comparison, Fano factors of visually-identified GFP-positive and GFP-negative units recorded in ... Thus, on the basis of our new and additional experiments and analyses we cannot confirm that the Fano factor represents a sufficient parameter to reliably discriminate under in vitro conditions between excitatory and GABAergic inhibitory neurons. However, we would like to point out that a number of previous studies (Bartho et al., 2004; Sirota et al., 2008; Sakata and Harris, 2009) and also our own analyses in adult rat cerebral cortex (Reyes-Puerta et al., 2014) were able to identify inhibitory interneurons recorded under in vivo conditions on the basis of their extracellular firing properties. Therefore, it cannot be excluded that a more immature functional state of neurons cultured under in vitro conditions hamper the identification of inhibitory interneurons on the basis of the extracellular spiking properties.

Attentional Bias Through Oscillatory Coherence Between Excitatory Activity and Inhibitory Minima.

An implementation of attentional bias is presented for a network model that couples excitatory and inhibitory oscillatory units in a manner that is inspired by the mechanisms that generate cortical gamma oscillations. Attentional biases are implemented as oscillatory coherences between excitatory units that encode the spatial location or features of the target and the pool of inhibitory units. This form of attentional bias is motivated by neurophysiological findings that relate selective attention to spike field coherence. Including also pattern recognition mechanisms, we demonstrate how this implementation of attentional bias leads to selection of an attentional target while suppressing distracters for cases of spatial and feature-based attention. With respect to neurophysiological observations, we argue that the recently found positive correlation between high firing rates and strong gamma locking with attention (Vinck, Womelsdorf, Buffalo, Desimone, & Fries, 2013) may point to an essential mechanism of the brain's attentional selection and suppression processes.

On a spike train probability model with interacting neural units

We investigate an extension of the spike train stochastic model based on the conditional intensity, in which the recovery function includes an interaction between several excitatory neural units. Such function is proposed as depending both on the time elapsed since the last spike and on the last spiking unit. Our approach, being somewhat related to the competing risks model, allows to obtain the general form of the interspike distribution and of the probability of consecutive spikes from the same unit. Various results are finally presented in the two cases when the free firing rate function (i) is constant, and (ii) has a sinusoidal form.

Temporally evolving surround suppression helps decoding in a spiking model of motion processing

We find that a neural network with temporally evolving surround suppression improves the linear decodability of the network’s population response. We present a novel model of motion processing that is fully implemented in a spiking neural network. We examine the role of lateral inhibition in V1 and MT. We model the response of the retina, V1 and MT, where each neuron is a single compartment conductance-based dynamical system. We apply a linear decoder to estimate the speed and direction of optic flow from a population response of a small spatial region in MT. Before training the decoder on population vector responses from MT with labeled speeds, we allow the spiking neural network to adapt the weights of the recurrent inhibitory neurons with spike-timing dependent plasticity (STDP). This allows the individual cells to adapt their dynamic range to the statistics reflected in the activity of the excitatory feed-forward network. Also, we impose a random onset latency of 1-10 msec for each feed-forward neuron. The combination of the onset latency and the inhibitory STDP results in a surround suppression with a magnitude that modulates throughout the course of the response, balancing the incoming excitatory drive. The temporally evolving surround suppression affects the activity of excitatory and inhibitory units in V1 and MT. The result is a population response of MT excitatory units that is more informative for decoding. The early response is less direction selective but drives the inhibition that sculpts the later responses. One source of improvement is that inhibition removes the non-selective response, but still preserves a robust selective response. Also the inhibition acts as gain control, which limits how much saturation corrupts the linear code. We measure

Reinforcement Learning of Targeted Movement in a Spiking Neuronal Model of Motor Cortex

Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint “forearm” to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. The model consisted of 144 excitatory and 64 inhibitory event-based neurons, each with AMPA, NMDA, and GABA synapses. Proprioceptive cell input to this model encoded the 2 muscle lengths. Plasticity was only enabled in feedforward connections between input and output excitatory units, using spike-timing-dependent eligibility traces for synaptic credit or blame assignment. Learning resulted from a global 3-valued signal: reward (+1), no learning (0), or punishment (−1), corresponding to phasic increases, lack of change, or phasic decreases of dopaminergic cell firing, respectively. Successful learning only occurred when both reward and punishment were enabled. In this case, 5 target angles were learned successfully within 180 s of simulation time, with a median error of 8 degrees. Motor babbling allowed exploratory learning, but decreased the stability of the learned behavior, since the hand continued moving after reaching the target. Our model demonstrated that a global reinforcement signal, coupled with eligibility traces for synaptic plasticity, can train a spiking sensorimotor network to perform goal-directed motor behavior.

Reinforcement learning of 2-joint virtual arm reaching in motor cortex simulation

Few attempts have been made to model learning of sensory-motor control using spiking neural units. We trained a 2-degree-of-freedom virtual arm to reach for a target using a spiking-neuron model of motor cortex that maps proprioceptive representations of limb position to motor commands and undergoes learning based on reinforcement mechanisms suggested by the dopaminergic reward system. A 2-layer model of layer 5 motor cortex (M1) passed motor commands to the virtual arm and received proprioceptive position information from it. The reinforcement algorithm trained synapses of M1 using reward (punishment) signals based on visual perception of decreasing (increasing) distance of the virtual hand from the target. Output M1 units were partially driven by noise, creating stochastic movements that were shaped to achieve desired outcomes. The virtual arm consisted of a shoulder joint, upper arm, elbow joint, and forearm. The upper- and forearm were each controlled by a pair of flexor/extensor muscles. These muscles received rotational commands from 192 output cells of the M1 model, while the M1 model received input from muscle-specific groups of sensory cells, each of which were tuned to fire over a range of muscle lengths. The M1 model had 384 excitatory and 192 inhibitory event-based integrate-and-fire neurons, with AMPA/NMDA and GABA synapses. Excitatory and inhibitory units were interconnected probabilistically. Plasticity was enabled in the feedforward connections between input and output excitatory units. Poisson noise was added to the output units for driving stochastic movements. The reinforcement learning (RL) algorithm used eligibility traces for synaptic credit/blame assignment, and a global signal (+1=reward, -1=punishment) corresponding to dopaminergic bursting/dipping. Eligibility traces were spike-timing-dependent, with pre-before-post spiking required. Reward (punishment) was delivered when the distance between the hand and target decreased (increased) [1]. RL learning occurred over 100 training sessions with the arm starting at 15 different initial positions. Each sub-session consisted of 15 s of RL training from a specific starting position. After training, the network was tested for its ability to reach the arm to target from each starting position, over the course of a 15 s trial. Compared to the naive network, the network post-training was able to reach the target from all starting positions. This was most clearly pronounced when the arm started at a large distance from the target. After reaching the target, the hand tended to oscillate around the target. Learning was most effective when recurrent connectivity in the output units was turned off or at low levels. Best overall performance was achieved with no recurrent connectivity and moderate maximal weights. Although learning typically increased average synaptic weight gains in the input-to-output M1 connections, there were frequent reductions in weights as well. Our model predicts that optimal motor performance is sensitive to perturbations in both strength and density of recurrent connectivity within motor cortex and that therefore the wiring of recurrent connectivity during development might be carefully regulated.

Group sparse coding with a collection of winner-take-all networks

Degeneracy is a ubiquitous feature of computation and coding in biological systems. Degenerate codes—codes in which multiple code words have the same meaning or interpretation—arise in a wide range of biological processes, from the many-to-one mapping of codons to amino acids to the numerous instances of degenerate coding in the nervous system, both in the periphery and in the cortex. There are a number of reasons why neural systems might seek some degree of degeneracy; by enabling a number of inputs (stimuli) to be mapped to the same output code, the system is endowed with a certain level of robustness to noise and to cell death. Furthermore, degenerate codes provide invariance to certain types of variability in the raw sensory input. This is particularly useful in object recognition for instance, where despite photometric variability, the representation of an object under different illumination conditions should be equivalent at some level of processing even if the neural representations for these states are slightly different. In tandem with nature's desire to produce degeneracy and invariance in the brain’s representation of sensory inputs, sparsity also appears to also play an important role in neural coding; a sparse code is one that requires a small number of active neurons relative to the size of the population. Sparse codes are also associated with a high degree of specificity: each cell in the population only responds to a limited number of inputs. Sparse coding has been observed in the visual cortex of macaque, mushroom body in locusts, and auditory cortex in grasshoppers. Experimental studies suggest that neural systems might seek to strike an appropriate balance between the degeneracy and the sparsity of the neural code. Here, we describe a framework in which one can tradeoff between these two objectives in a natural way. In contrast to models for global sparse coding such as the locally competitive architectures in LCA [1] which find a population code that captures the primary features of the stimulus while minimizing the number of active neurons in the population, our goal is to find a representation of the sensory input that minimizes the number of ‘groups’ that must be active to encode the input. The idea is that when the excitatory cells are grouped in a meaningful way, the activation of a small set of groups might have a particular meaning to the organism. If we view the activation of these ‘functional groups’ as a high-level or coarse scale representation of the stimulus, the resulting code is stable to a wide-range of perturbations in the input space. This two-level representation (the coarse and the fine) provides a robust and degenerate mapping of the input space that is also sparse. After motivating the utility of group sparse coding in neural systems, we show how one can implement these sparse coding networks by coupling a collection of winner-take-all (WTAs) networks that were first introduced for state-dependent computation in [2]. WTAs are particularly attractive as they represent neurally plausible microcircuits that have been hypothesized to serve as a computational primitive in complex cortical networks. An interesting property of these microcircuits is the fact that a single inhibitory unit delivers a common inhibitory signal to all of the excitatory units within the WTA. This architecture is in stark contrast the highly specified point-to-point inhibition structure required to faithfully implement a LCA, i.e., the inhibition between every pair of cells can be different. To solve sparse coding problems with collections of WTAs, we adapt the analytical approach in [1] and show how one can couple a collection of WTAs to descend an energy function that promotes grouped sparse representations. In addition to building in invariances into the code, we demonstrate that there are a number of computational advantages to employing this type of grouped network model for sparse coding. First, we demonstrate that we require fewer “long-range” connections to produce group sparse codes with collections of WTAs than that required for a global LCA. Second, the lateral and recurrent inhibition in the network modulates the threshold of excitatory units collectively: this means that fewer interneurons are required to produce a representation, the number of long range messages which must be sent is reduced, and the network converges to a solution faster than the global LCA.

Local model for contextual modulation in the cerebral cortex

A neural response to a sensory stimulus in cerebral cortex is modulated when other stimuli are presented simultaneously. The other stimuli can modulate responses even when they do not drive the neural output alone, indicating a non-linear summation of synaptic activity. The mechanisms of the nonlinearity have remained unclear. Here, I explore a model which considers both network and intracellular processes, and which can account for various types of contextual modulation. The processes include synaptic sensitivity function, determination of inhibition strength, dendritic decay of membrane voltage, and summation of excitatory and inhibitory membrane voltages. First, the model assumes that excitatory and inhibitory units have the same input sensitivity function, which is more broadly tuned than the output tuning function. Second, a central property of the model is that inhibition is a fraction of excitation, determined by covariance between the input and the sensitivity function. With proper fraction, a model neuron sums apparently decorrelated input, regardless of correlations in the original input. Third, the model assumes that synaptic input lands anisotropically on the dendrites, which together with passive dendritic decay cause exponential decay in summation along the input space. This explains the difference between input sensitivity function and output tuning function, and thus accounts for the division between driving classical and modulating extra-classical receptive fields. The model simulations replicate single-cell area summation function, far surround facilitation, and a shift in tuning function due to contextual stimulation. The model is very general, and should be applicable to various interactions between cortical representations.

Pattern recognition through compatibility of excitatory and inhibitory rhythms

A pattern recognition mechanism (“windowing mechanism”) is proposed that is based on Hebbian memory and oscillatory inhibition. It is related to properties of cortical gamma oscillations as expressed with the communication-through-coherence hypothesis. The rhythm dominating among excitatory units is imprinted on the inhibitory units through columnar couplings. Excitatory units that participate in the dominating rhythm may escape the feedback from the inhibitory pool through being active in the time windows given by the minima of the inhibitory effect. The activity of other units is suppressed. Constituting an excitatory rhythm that is compatible with the inhibitory one, the participating units comprise the winning patterns. This windowing mechanism is specified through giving an oscillatory network model and demonstrating it with image processing examples. Thereby, we also provide a gradient system formulation for inhibitory generation of synchrony.

Spontaneous activity in a self-organizing recurrent network reflects prior learning

Event Abstract Back to Event Spontaneous activity in a self-organizing recurrent network reflects prior learning Andreea Lazar1*, Gordon Pipa2 and Jochen Triesch1 1 Frankfurt Institute for Advanced Studies, Germany 2 Max Planck Institute for Brain Research, Germany In the neocortex, spontaneous activity in the absence of sensory input exhibits nonrandom spatiotemporal patterns [Tsodyks et al., 1999, Fiser et al., 2004]. Following the repetitive presentation of a given visual stimulus, the spontaneous activity patterns show similarities with the sensory-evoked responses [Han et al., 2008]. It has been hypothesized that spontaneous activity reflects prior information, learned via plasticity based on past experience, which when integrated with sensory-evoked activity enables Bayesian inference [Berkes et al., 2009]. We explore the characteristics of spontaneous activity following unsupervised learning of spatio-temporal stimuli in a self-organizing recurrent network (SORN) shaped by synaptic and neuronal plasticity. The SORN model [Lazar et al., 2009] consists of a population of excitatory cells and a smaller population of inhibitory cells. The connectivity among excitatory units is sparse and subject to a simple spike-timing-dependent plasticity rule. Additionally, synaptic normalization keeps the sum of an excitatory neuron's afferent weights constant, while intrinsic plasticity regulates a neuron’s firing threshold to maintain a low average activity level. The network receives input sequences composed of different letters and learns the structure embedded in these sequences in an unsupervised manner. Following a learning interval, we omit the input and analyse the characteristics of spontaneous activity. We find that the network revisits states similar to those embedded during input stimulation and that it follows similar trajectories through its high-dimensional state space. Furthermore we show that the spontaneous activity reflects the statistical properties of the data: during spontaneous activity the network preferentially visits states that are similar to evoked activity patterns for inputs with a higher prior probability. Our results establish a novel link between STDP-based unsupervised learning in recurrent networks and concepts of statistical inference. Acknowledgments: This work was supported by the Hertie Foundation, grant PLICON (EC MEXT-CT-2006-042484), and GABA Project (EU-04330). We thank Sophie Deneve for literature suggestions. Conference: Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010. Presentation Type: Poster Presentation Topic: Poster session I Citation: Lazar A, Pipa G and Triesch J (2010). Spontaneous activity in a self-organizing recurrent network reflects prior learning. Front. Neurosci. Conference Abstract: Computational and Systems Neuroscience 2010. doi: 10.3389/conf.fnins.2010.03.00336 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 19 Feb 2010; Published Online: 19 Feb 2010. * Correspondence: Andreea Lazar, Frankfurt Institute for Advanced Studies, Franffurt am Main, Germany, andreea.lazar@brain.mpg.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andreea Lazar Gordon Pipa Jochen Triesch Google Andreea Lazar Gordon Pipa Jochen Triesch Google Scholar Andreea Lazar Gordon Pipa Jochen Triesch PubMed Andreea Lazar Gordon Pipa Jochen Triesch Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Phase space analysis of networks based on biologically realistic parameters

We study cortical network dynamics for a spatially embedded network model. It represents, in terms of spatial scale, a large piece of cortex allowing for long-range connections, resulting in a rather sparse connectivity. The spatial embedding also permits us to include distance-dependent conduction delays. We use two different types of conductance-based I&F neurons as excitatory and inhibitory units, as well as specific connection probabilities. In order to remain computationally tractable, we reduce neuron density, modelling part of the missing internal input via external poissonian spike trains. Compared to previous studies, we observe significant changes in the dynamical phase space: Altered activity patterns require another regularity measures than the coefficient of variation. Hence, we compare three different regularity measure on the basis of artificial inter-spike-interval distributions. We identify two types of mixed states, where different phases coexist in certain regions of the phase space. More notably, our boundary between high and low activity states depends predominantly on the relation between excitatory and inhibitory synaptic strength instead of the input rate.