2006 Special IssueSelective attention through phase relationship of excitatory and inhibitory input synchrony in a model cortical neuron
Introduction
Neural correlates of selective attention have been studied using single-unit recordings from primate extrastriate area . It was found that attention increases the neuron’s firing rate in response to a single stimulus placed in its receptive field. When more than one stimulus is presented, selective attention can modulate the neuron’s response based on its stimulus selectivity. When attention is directed to the neuron’s preferred stimulus the neuron’s firing rate is increased; when attention is directed to the non-preferred stimulus its firing rate is decreased (Reynolds et al., 1999, Reynolds et al., 2000, Reynolds and Desimone, 2003). This phenomenon has been conceptually explained as a biased competition (Desimone and Duncan, 1995, Reynolds et al., 1999) wherein active input populations from multiple stimuli compete with one another to generate a neuronal response intermediate between the responses to the individual stimuli. Attending to a stimulus can bias this competition producing a shift in the neuron’s response towards the response that would be obtained if the attended stimulus population was active alone.
Several models have been proposed to elucidate the mechanisms underlying stimulus competition and attentional bias. In a phenomenological model by Reynolds et al. (1999), attention bias was conceived as an increase in the synaptic weights of the inputs from the neurons that receive visual information from the attended stimulus. However, the time course of synaptic modification being generally slow, it is not clear how such synaptic biases can emerge at the time scales of attention shifts. In other network models cell populations selective to specific stimulus features such as orientation were used with feedforward and feedback connections to a global inhibitory network pool. In the presence of multiple stimuli, a principal cell’s response to a preferred stimulus was suppressed by inhibitory inputs recruited from other principal cells that selectively responded to the non-preferred stimuli (Deco et al., 2002, Usher and Niebur, 1996). The competition was biased in favor of a particular feature/ orientation by providing an external excitatory top-down drive preferentially to the principal cells tuned to that orientation. Hence, the attention effect was modeled by modulating the total amount of input excitation and inhibition to a neuron. In a single cell multicompartmental model, stimulus competition was implemented by spatially segregating the inputs projecting onto a neuron to different regions of its dendritic tree (Archie & Mel, 2000). The authors also modeled the attentional bias by increasing the amount of feedforward excitatory input to the attended stimulus (Archie & Mel, 2004).
The above models hypothesize that a neuron must receive increased excitatory inputs to exhibit attentional modulation. This increase assumes the existence of a mechanism that can recruit new excitatory inputs, or that can selectively increase the firing rate of the input population corresponding to the attended stimulus. An alternative hypothesis is that the amount of excitatory inputs (number or rate) remains unchanged, but that attentional bias is achieved by a modulation of their correlation. Correlations imply no change in the sum total input spikes to a neuron, nor a change in synaptic strengths, but a possibly rapid change in the relative spike timing of these inputs such that spikes from different neurons arrive close together in time and have therefore a greater postsynaptic impact.
Model simulations have shown that input correlations increase the gain of a post-synaptic neuron’s input–output firing rate curve (Chance et al., 2002, Fellous et al., 2003, Salinas and Sejnowski, 2000, Salinas and Sejnowski, 2002). This is achieved by increased fluctuations around the spiking threshold of the neuron. Correlation in either the excitatory or inhibitory inputs can separately elicit this effect; however correlations between excitatory and inhibitory input annul this increase in gain. Physiologically, correlations have been observed as gamma frequency range oscillations in visual cortex (reviewed in Engel, Roelfsema, Fries, Brecht, and Singer (1997), Singer (1999), Singer and Gray (1995)), and neuronal assemblies that have a common orientation preference to synchronize with one another (Eckhorn et al., 1988, Gray and Singer, 1989, Gray et al., 1990). More recently neurons receiving their preferred stimulus input have been shown to have spike field coherence in the gamma frequency range in spatial attention (Fries, Reynolds, Rorie, & Desimone, 2001) as well as visual search tasks (Bichot, Rossi, & Desimone, 2005). In addition to excitatory neurons, there is vast accumulating evidence that networks of inhibitory interneurons mutually synchronize and are capable of generating gamma frequency range oscillations in the hippocampus and cortex (Deans et al., 2001, Fisahn et al., 1998, Wang and Buzsaki, 1996). Based on model simulations it was proposed that the attention effects to single stimuli could be mediated by the modulation of the synchrony of interneuron networks (Tiesinga, Fellous, Salinas, Jose, & Sejnowski, 2004). In this model when the temporal dispersion of the inhibitory inputs to the neuron was reduced, leading to greater synchrony, the neuron displayed a firing rate gain akin to that seen when a stimulus is attended. However, increasing the synchrony of the interneuron network corresponding to an attended stimulus always increased the model response. Hence a synchrony manipulation on its own cannot account for a decrease in firing when a non-preferred stimulus is attended.
Recently Tiesinga (2005) also proposed an inhibitory correlation mechanism for biased stimulus competition termed stimulus competition by inhibitory interference. The firing rate of the postsynaptic neuron was modulated with attention to the preferred or non-preferred stimulus by changing the phase delay between two separate inhibitory populations that represented either stimulus. When the two inhibitory populations oscillating in the gamma frequency range were in phase or had constructive interference the postsynaptic neuron’s firing rate was increased. A reduction in firing rate was achieved when the two inhibitory populations were out of phase. In this model excitatory inputs were modeled as asynchronous events, which may not be entirely compatible with evidence from recordings in striate and extrastriate cortex (reviewed in Engel et al. (1997), Singer (1999), Singer and Gray (1995)). Given the evidence for synchronized oscillation in both excitation and inhibition in cortex we investigate a mechanism wherein both these components are correlated to attain biased stimulus competition.
Section snippets
Model and quantitative assumptions
We used a multi-compartmental reconstruction of a layer 4 spiny stellate neuron (Mainen & Sejnowski, 1996) to represent the neuron in our model. Voltage gated Na+ and K+ Hodgkin–Huxley channels were inserted in the soma and axon. The soma was also provided with a -type K+ current to allow for spike frequency adaptation as well as Ca2+ dependent K+ after-hyperpolarizing currents that prevented excessive spike bursts to synaptic inputs. The dendrites were modeled as passive and all
Stimulus preference: Tuning by synapse number
Neurons in area display tuning to features of visual stimuli such as orientation and color as well as their combinations. We first generated the neuronal connectivity from to that gave rise to such stimulus tuning. For this purpose the population encoding the preferred stimulus feature projected to the neuron with the maximum number of excitatory synapses used in our simulations (400). This population shown in Fig. 1 is the preferred population.
populations representing
Response to stimulus pairs
In a multi-compartment model of a cortical neuron receiving inputs from two excitatory synaptic pools and a feedforward inhibitory pool, we have analyzed the neuron’s response to combined inputs from both excitatory pools in the absence and presence of presynaptic spike correlations. We generated stimulus preference by modeling a greater number of excitatory synapses from one set of inputs than the other . Feedforward inhibition was modeled as broadly tuned with a set of
Acknowledgements
This work was supported by the Howard Hughes Medical Institute, NSF-IGERT grant and National Institute of Health grant # 5R01 MH068481-03.
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2020, Current Opinion in PhysiologyCitation Excerpt :This enhances the impact of the synchronized, relevant signals, and diminishes it for irrelevant signals [38,39]. Theoretical and modeling studies suggest that this mechanism, termed communication-through-coherence (CTC) or routing-by-synchrony (RBS), allows the postsynaptic neurons to process with high selectivity the synchronized input [40–44,45••]. Furthermore, the strength, speed, and direction of information flow between coupled oscillatory populations in spiking networks depend on the phase relations and strength of their oscillations [40,43,44,45••,46–48].
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2012, Neural NetworksOscillations and filtering networks support flexible routing of information
2010, NeuronCitation Excerpt :Using oscillatory mechanisms to turn on and off direct interregion connections could alleviate the need for this additional circuitry. Prior studies have focused on how oscillatory mechanisms could reproduce effects of attention on V4 neuronal responses (Buehlmann and Deco, 2008; Niebur et al., 1993; Zeitler et al., 2008; Mishra et al., 2006). Our focus was not to reproduce a particular experimental result but rather to build a network that performs the specific computational task of signal gating—turning on and off functional connectivity for a given fixed anatomical connectivity.
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2009, Neural NetworksCitation Excerpt :The PRP is relevant because it is a summary of the feed-forward activity from which the V4 neurons sample their inputs. A common hypothesis is that the PRP corresponding to the location of the NP stimulus provides fewer (or weaker) synaptic inputs to the V4 neuron, compared with the P stimulus (Mishra, Fellous, & Sejnowski, 2006; Tiesinga, 2005). Taken together this suggests that in V4 the columns compete, whereas in V1 the hypercolumns compete.
Cortical Enlightenment: Are Attentional Gamma Oscillations Driven by ING or PING?
2009, NeuronCitation Excerpt :Modeling studies show that changes in the level of synchrony of the inhibitory inputs modulate the response gain, but only for synchrony in the gamma-frequency range (Borgers et al., 2005; Tiesinga et al., 2004, 2008). When both the excitatory and inhibitory inputs arrive in synchronous volleys, their relative phase can modulate the gain to other inputs (Buia and Tiesinga, 2006; Mishra et al., 2006). Recent experiments have shown that the effectiveness of long-distance communication depends on the relative gamma phase between the input spikes and the local field potential (LFP), termed the communication through coherence (CTC) hypothesis (Womelsdorf et al., 2007).
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Present address: University of Arizona, Neural Systems Memory and Aging, Life Sciences North, #384, Tucson, AZ, 85724, United States.