Stimulus competition by inhibitory interference

When two stimuli are present in the receptive field of a V4 neuron, the firing rate response is between the weakest and strongest response elicited by each of the stimuli alone (Reynolds et al, 1999, Journal of Neuroscience 19:1736-1753). When attention is directed towards the stimulus eliciting the strongest response (the preferred stimulus), the response to the pair is increased, whereas the response decreases when attention is directed to the other stimulus (the poor stimulus). These experimental results were reproduced in a model of a V4 neuron under the assumption that attention modulates the activity of local interneuron networks. The V4 model neuron received stimulus-specific asynchronous excitation from V2 and synchronous inhibitory inputs from two local interneuron networks in V4. Each interneuron network was driven by stimulus-specific excitatory inputs from V2 and was modulated by a projection from the frontal eye fields. Stimulus competition was present because of a delay in arrival time of synchronous volleys from each interneuron network. For small delays, the firing rate was close to the rate elicited by the preferred stimulus alone, whereas for larger delays it approached the firing rate of the poor stimulus. When either stimulus was presented alone the neuron's response was not altered by the change in delay. The model suggests that top-down attention biases the competition between V2 columns for control of V4 neurons by changing the relative timing of inhibition rather than by changes in the degree of synchrony of interneuron networks. The mechanism proposed here for attentional modulation of firing rate - gain modulation by inhibitory interference - is likely to have more general applicability to cortical information processing.Comment: 20 pages, 7 figures, 1 tabl

Reconciling coherent oscillation with modulation of irregular spiking activity in selective attention: gamma-range synchronization between sensory and executive cortical areas

[EN] In this computational work, we investigated gamma-band synchronization across cortical circuits associated with selective attention. The model explicitly instantiates a reciprocally connected loop of spiking neurons between a sensory-type (area MT) and an executive-type (prefrontal/parietal) cortical circuit (the source area for top-down attentional signaling). Moreover, unlike models in which neurons behave as clock-like oscillators, in our model single-cell firing is highly irregular (close to Poisson), while local field potential exhibits a population rhythm. In this "sparsely synchronized oscillation" regime, the model reproduces and clarifies multiple observations from behaving animals. Top-down attentional inputs have a profound effect on network oscillatory dynamics while only modestly affecting single-neuron spiking statistics. In addition, attentional synchrony modulations are highly selective: interareal neuronal coherence occurs only when there is a close match between the preferred feature of neurons, the attended feature, and the presented stimulus, a prediction that is experimentally testable. When interareal coherence was abolished, attention-induced gain modulations of sensory neurons were slightly reduced. Therefore, our model reconciles the rate and synchronization effects, and suggests that interareal coherence contributes to large-scale neuronal computation in the brain through modest enhancement of rate modulations as well as a pronounced attention-specific enhancement of neural synchrony.This work was funded by the Volkswagen Foundation, the Spanish Ministry of Science and Innovation, and the European Regional Development Fund. A.C. is supported by the Researcher Stabilization Program of the Health Department of the Generalitat de Catalunya. X.-J.W. is supported by the National Institutes of Health Grant 2R01MH062349 and the Kavli Foundation. We are thankful to Stefan Treue for fruitful discussions and to Jorge Ejarque for technical support in efficiently implementing the search optimization procedure in a grid cluster computing system. Also, we thankfully acknowledge the computer resources and assistance from the Barcelona Supercomputing Center-Centro Nacional de Supercomputación, Spain.Ardid-Ramírez, JS.; Wang, X.; Gomez-Cabrero, D.; Compte, A. (2010). Reconciling coherent oscillation with modulation of irregular spiking activity in selective attention: gamma-range synchronization between sensory and executive cortical areas. Journal of Neuroscience. 30(8):2856-2870. https://doi.org/10.1523/JNEUROSCI.4222-09.2010S2856287030

Biased competition through variations in amplitude of γ-oscillations

Experiments in visual cortex have shown that the firing rate of a neuron in response to the simultaneous presentation of a preferred and non-preferred stimulus within the receptive field is intermediate between that for the two stimuli alone (stimulus competition). Attention directed to one of the stimuli drives the response towards the response induced by the attended stimulus alone (selective attention). This study shows that a simple feedforward model with fixed synaptic conductance values can reproduce these two phenomena using synchronization in the gamma-frequency range to increase the effective synaptic gain for the responses to the attended stimulus. The performance of the model is robust to changes in the parameter values. The model predicts that the phase locking between presynaptic input and output spikes increases with attention

Regulation of spike timing in visual cortical circuits

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role

Regulation of spike timing in visual cortical circuits

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role

Allocation of Computational Resources in the Nervous System.

The nervous system integrates past information together with predictions about the future in order to produce rewarding actions for the organism. This dissertation focuses on the resources underlying these computations, and the task-dependent allocation of these resources. We present evidence that principles from optimal coding and optimal estimation account for overt and covert orienting phenomena, as observed from both behavioral experiments and neuronal recordings. First, we review behavioral measurements related to selective attention and discuss models that account for these data. We show that reallocation of resources emerges as a natural property of systems that encode their inputs efficiently under non-uniform constraints. We continue by discussing the attentional modulation of neuronal activity, and showthat: (1) Modulation of coding strategies does not require special mechanisms: it is possible to obtain dramatic modulation even when signals informing the system about fidelity requirements enter the system in a fashion indistinguishable from sensory signals. (2) Optimal coding under non-uniform fidelity requirements is sufficient to account for the firing rate modulation observed during selective attention experiments. (3) The response of a single neuron cannot bewell characterized by measurements of attentional modulation of only a single sensory stimulus. (4) The magnitude of the activity modulation depends on the capacity of the neural circuit. A later chapter discusses the neural mechanisms for resource allocation, and the relation between attentional mechanisms and receptive field formation. The remainder of the dissertation focuses on overt orienting phenomena and active perception. We present a theoretical analysis of the allocation of resources during state estimation of multiple targets with different uncertainties, together with eye-tracking experiments that confirm our predictions. We finish by discussing the implications of these results to our current understanding of orienting phenomena and the neural code

The recruitment and function of inhibitory interneurons in olfactory bulb processing

Inhibitory interneurons are the “shush”-ers of the brain—their output causes a reduction in the output of other neurons. Inhibitory interactions play a critical role in the olfactory bulb, where they shape olfactory representations that guide behavior. However, the mechanisms by which interneuron activation improves olfactory function remain debated. In particular, the relative importance neural activity over short periods of time (~tens of milliseconds) versus long periods of time (hundreds to thousands of milliseconds) has provoked significant debate. Granule cells are inhibitory interneurons in the olfactory bulb that can respond and influence olfactory bulb activity across a wide range of timescales. The first part of this dissertation investigates the physiological mechanisms driving the timing of granule cell recruitment. We found that the specific timing of recruitment depends on the timing of synaptic excitation delivered from tufted cells. Tufted cells (unlike the more commonly studied mitral cells) are able to fire at long latencies due to intrinsic membrane properties that allow them to integrate weak inputs slowly while responding rapidly to strong inputs. Computational modeling revealed that the long-latency inhibition generated by this mechanism can improve performance on stimulus discrimination tasks. The second portion of this dissertation focuses on the downstream effects of granule cell recruitment. Highly correlated spiking can be advantageous for propagating information. However, these same correlations limit encoding by introducing redundancy. We investigated how granule cell recruitment altered correlations between mitral cell pairs across timescales. We found that granule cell recruitment increased fast timescale correlations (i.e. synchronous spiking) while simultaneously decreasing slow timescale correlations (i.e. firing rate similarity). Using computational modeling, we show that timescale-dependent correlation changes are functionally advantageous because they can circumvent the tradeoff between propagation and encoding. Taken together, these studies extend our understanding of olfactory bulb physiology by providing a mechanistic description of how inhibitory circuits shape activity across timescales. Our results indicate that granule cell recruitment requires dynamic and stimulus-dependent interactions between mitral, tufted, and granule cells, and that the inhibition recruited by this mechanism works at multiple timescales to effectively encode and propagate stimulus information

Oscillatory dynamics as a mechanism of integration in complex networks of neurons

The large-scale integrative mechanisms of the brain, the means by which the activity of functionally segregated neuronal regions are combined, are not well understood. There is growing agreement that a flexible mechanism of integration must be present in order to support the myriad changing cognitive demands under which we are placed. Neuronal communication through phase-coherent oscillation stands as the prominent theory of cognitive integration. The work presented in this thesis explores the role of oscillation and synchronisation in the transfer and integration of information in the brain. It is first shown that complex metastable dynamics suitable for modelling phase-coherent neuronal synchronisation emerge from modularity in networks of delay and pulse-coupled oscillators. Within a restricted parameter regime these networks display a constantly changing set of partially synchronised states where some modules remain highly synchronised while others desynchronise. An examination of network phase dynamics shows increasing coherence with increasing connectivity between modules. The metastable chimera states that emerge from the activity of modular oscillator networks are demonstrated to be synchronous with a constant phase relationship as would be required of a mechanism of large-scale neural integration. A specific example of functional phase-coherent synchronisation within a spiking neural system is then developed. Competitive stimulus selection between converging population encoded stimuli is demonstrated through entrainment of oscillation in receiving neurons. The behaviour of the model is shown to be analogous to well-known competitive processes of stimulus selection such as binocular rivalry, matching key experimentally observed properties for the distribution and correlation of periods of entrainment under differing stimuli strength. Finally two new measures of network centrality, knotty-centrality and set betweenness centrality, are developed and applied to empirically derived human structural brain connectivity data. It is shown that human brain organisation exhibits a topologically central core network within a modular structure consistent with the generation of synchronous oscillation with functional phase dynamics

The Attentional Routing Circuit: A Neural Model of Attentional Modulation and Control of Functional Connectivity

Several decades of physiology, imaging and psychophysics research on attention has generated an enormous amount of data describing myriad forms of attentional effects. A similar breadth of theoretical models have been proposed that attempt to explain these effects in varying amounts of detail. However, there remains a need for neurally detailed mechanistic models of attention that connect more directly with various kinds of experimental data -- behavioural, psychophysical, neurophysiological, and neuroanatomical -- and that provide experimentally testable predictions. Research has been conducted that aims to identify neurally consistent principles that underlie selective attentional processing in cortex. The research specifically focuses on describing the functional mechanisms of attentional routing in a large-scale hierarchical model, and demonstrating the biological plausibility of the model by presenting a spiking neuron implementation that can account for a variety of attentional effects. The thesis begins by discussing several significant physiological effects of attention, and prominent brain areas involved in selective attention, which provide strong constraints for developing a model of attentional processing in cortex. Several prominent models of attention are then discussed, from which a set of common limitations in existing models is assembled that need to be addressed by the proposed model. One central limitation is that, for many existing models, it remains to be demonstrated that their computations can be plausibly performed in spiking neurons. Further, few models address attentional effects for more than a single neuron or single cortical area. And finally, few are able to account for different forms of attentional modulation in a single detailed model. These and other limitations are addressed by the Attentional Routing Circuit (ARC) proposed in this thesis. The presentation of the ARC begins with the proposal of a high-level mathematical model for selective routing in the visual hierarchy. The mathematical model is used to demonstrate that the suggested mechanisms allow for scale- and position-invariant representations of attended stimuli to be formed, and provides a functional context for interpreting detailed physiological effects. To evaluate the model's biological plausibility, the Neural Engineering Framework (NEF) is used to implement the ARC as a detailed spiking neuron model. Simulation results are then presented which demonstrate that selective routing can be performed efficiently in spiking neurons in a way that is consistent with the mathematical model. The neural circuitry for computing and applying attentional control signals in the ARC is then mapped on to neural populations in specific cortical laminae using known anatomical interlaminar and interareal connections to support the plausibility of its cortical implementation. The model is then tested for its ability to account for several forms of attentional modulation that have been reported in neurophysiological experiments. Three experiments of attention in macaque are simulated using the ARC, and for each of these experiments, the model is shown to be quantitatively consistent with measured data. Specifically, a study by Womelsdorf et al. (2008) demonstrates that spatial shifts of attention result in a shifting and shrinking of receptive fields depending on the target's position. An experiment by Treue and Martinez-Trujllo (1999) reports that attentional shifts between receptive field stimuli produce a multiplicative scaling of responses, but do not affect the neural tuning sensitivity. Finally, a study by Lee and Maunsell (2010) demonstrates that attentional shifts result in a multiplicative scaling of neural contrast-response functions that is consistent with a response-gain effect. The model accounts for each of these experimentally observed attentional effects using a single mechanism for selectively processing attended stimuli. In conclusion, it is suggested that the ARC is distinguished from previous models by providing a unifying interpretation of attentional effects at the level of single cells, neural populations, cortical areas, and over the bulk of the visual hierarchy. As well, there are several advantages of the ARC over previous models, including: (1) scalability to larger implementations without affecting the model's principles; (2) a significant increase in biological plausibility; (3) the ability to account for experimental results at multiple levels of analysis; (4) a detailed description of the model's anatomical substrate; (5) the ability to perform selective routing while preserving biological detail; and (6) generating a variety of experimentally testable predictions