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• W2564523059 abstract "•Slow dynamics of LIP local population can appear one-dimensional or high-dimensional•Model of coupling between local networks reconciles conflicting data•Reveals LIP’s internal, recurrent circuitry underlying surround suppression•Data show two-dimensional slow dynamics as predicted by model Little is known about the internal circuitry of the primate lateral intraparietal area (LIP). During two versions of a delayed-saccade task, we found radically different network dynamics beneath similar population average firing patterns. When neurons are not influenced by stimuli outside their receptive fields (RFs), dynamics of the high-dimensional LIP network during slowly varying activity lie predominantly in one multi-neuronal dimension, as described previously. However, when activity is suppressed by stimuli outside the RF, slow LIP dynamics markedly deviate from a single dimension. The conflicting results can be reconciled if two LIP local networks, each underlying an RF location and dominated by a single multi-neuronal activity pattern, are suppressively coupled to each other. These results demonstrate the low dimensionality of slow LIP local dynamics, and suggest that LIP local networks encoding the attentional and movement priority of competing visual locations actively suppress one another. Little is known about the internal circuitry of the primate lateral intraparietal area (LIP). During two versions of a delayed-saccade task, we found radically different network dynamics beneath similar population average firing patterns. When neurons are not influenced by stimuli outside their receptive fields (RFs), dynamics of the high-dimensional LIP network during slowly varying activity lie predominantly in one multi-neuronal dimension, as described previously. However, when activity is suppressed by stimuli outside the RF, slow LIP dynamics markedly deviate from a single dimension. The conflicting results can be reconciled if two LIP local networks, each underlying an RF location and dominated by a single multi-neuronal activity pattern, are suppressively coupled to each other. These results demonstrate the low dimensionality of slow LIP local dynamics, and suggest that LIP local networks encoding the attentional and movement priority of competing visual locations actively suppress one another. It has become increasingly appreciated that neural functions need to be understood in terms of neuronal populations and the dynamics of the circuits to which they belong (Miller and Wilson, 2008Miller E.K. Wilson M.A. All my circuits: using multiple electrodes to understand functioning neural networks.Neuron. 2008; 60: 483-488Abstract Full Text Full Text PDF PubMed Scopus (60) Google Scholar, Shenoy et al., 2013Shenoy K.V. Sahani M. Churchland M.M. Cortical control of arm movements: a dynamical systems perspective.Annu. Rev. Neurosci. 2013; 36: 337-359Crossref PubMed Scopus (394) Google Scholar). However, the field of systems neuroscience in nonhuman primates has traditionally been dominated by studies of the properties of single neurons. While we have a wealth of knowledge of single-neuron behaviors in many areas of the primate brain, this knowledge remains largely phenomenological—we know what neurons do, but not how they do it. Especially on the circuit level, the mechanisms and connectivity underlying neuronal behaviors are often obscure. Such is the case in the lateral intraparietal area (LIP), where a large body of literature has revealed that the activity of single neurons encodes visual attention and saccadic eye movements, as well as decision making variables, abstract categories, and other cognitive variables (Bisley and Goldberg, 2010Bisley J.W. Goldberg M.E. Attention, intention, and priority in the parietal lobe.Annu. Rev. Neurosci. 2010; 33: 1-21Crossref PubMed Scopus (697) Google Scholar, Freedman and Assad, 2011Freedman D.J. Assad J.A. A proposed common neural mechanism for categorization and perceptual decisions.Nat. Neurosci. 2011; 14: 143-146Crossref PubMed Scopus (98) Google Scholar, Gold and Shadlen, 2007Gold J.I. Shadlen M.N. The neural basis of decision making.Annu. Rev. Neurosci. 2007; 30: 535-574Crossref PubMed Scopus (2264) Google Scholar, Kable and Glimcher, 2009Kable J.W. Glimcher P.W. The neurobiology of decision: consensus and controversy.Neuron. 2009; 63: 733-745Abstract Full Text Full Text PDF PubMed Scopus (597) Google Scholar). However, little is known about the circuitry inside or outside the LIP network that produces such activity, and therefore the role of LIP in many of these functions is controversial. A step in understanding this circuitry was taken by Ganguli et al., 2008Ganguli S. Bisley J.W. Roitman J.D. Shadlen M.N. Goldberg M.E. Miller K.D. One-dimensional dynamics of attention and decision making in LIP.Neuron. 2008; 58: 15-25Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar, who analyzed LIP network dynamics during two different tasks: a delayed saccade task (Bisley and Goldberg, 2003Bisley J.W. Goldberg M.E. Neuronal activity in the lateral intraparietal area and spatial attention.Science. 2003; 299: 81-86Crossref PubMed Scopus (653) Google Scholar, Bisley and Goldberg, 2006Bisley J.W. Goldberg M.E. Neural correlates of attention and distractibility in the lateral intraparietal area.J. Neurophysiol. 2006; 95: 1696-1717Crossref PubMed Scopus (98) Google Scholar) and a random-dot motion discrimination task (Roitman and Shadlen, 2002Roitman J.D. Shadlen M.N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task.J. Neurosci. 2002; 22: 9475-9489Crossref PubMed Google Scholar). They found that the dynamics of the high-dimensional LIP network are dominated by one multi-neuronal dimension on slow timescales, which could be explained by a simple circuit model. This one-dimensionality was key to explaining an unexpected correspondence between LIP single-neuron responses and the timing of attentional shifts (examined in more detail below). More recently, Fitzgerald et al., 2013Fitzgerald J.K. Freedman D.J. Fanini A. Bennur S. Gold J.I. Assad J.A. Biased associative representations in parietal cortex.Neuron. 2013; 77: 180-191Abstract Full Text Full Text PDF PubMed Scopus (28) Google Scholar found further evidence for one-dimensional dynamics in three experiments in which LIP encoded learned associations between visual stimuli. Using a delayed-saccade task similar to the task of Bisley and Goldberg, 2003Bisley J.W. Goldberg M.E. Neuronal activity in the lateral intraparietal area and spatial attention.Science. 2003; 299: 81-86Crossref PubMed Scopus (653) Google Scholar, Bisley and Goldberg, 2006Bisley J.W. Goldberg M.E. Neural correlates of attention and distractibility in the lateral intraparietal area.J. Neurophysiol. 2006; 95: 1696-1717Crossref PubMed Scopus (98) Google Scholar (hereafter BG), Falkner et al., 2010Falkner A.L. Krishna B.S. Goldberg M.E. Surround suppression sharpens the priority map in the lateral intraparietal area.J. Neurosci. 2010; 30: 12787-12797Crossref PubMed Scopus (67) Google Scholar (hereafter FK) reported “surround suppression” in LIP (see also Louie et al., 2011Louie K. Grattan L.E. Glimcher P.W. Reward value-based gain control: divisive normalization in parietal cortex.J. Neurosci. 2011; 31: 10627-10639Crossref PubMed Scopus (191) Google Scholar), i.e., stimuli outside the receptive field (RF) of a cell suppress the cell’s activity. In the FK study, the population-averaged activity over time is very similar to that in the BG study, as expected given the very similar tasks. However, we find that the pattern of activity across neurons changes over time in a very different way in the FK study. In particular, the network dynamics in the FK dataset markedly deviate from the one-dimensional dynamics observed in the BG dataset, calling into question the validity of the one-dimensional LIP model of Ganguli et al. We show that the two sets of conflicting results can be reconciled and well characterized by a more general low-dimensional model: each of two local LIP networks in isolation has its own single dominant dimension, and suppressive coupling between them gives rise to two dominant dimensions. We further show that the FK data directly confirm the two-dimensional dynamics predicted by the model. Our study thus represents a step forward in discovering circuit mechanisms and connectivity from single-neuron recordings, and in understanding mechanisms behind LIP functions. We begin by describing the first of the two conflicting datasets (Bisley and Goldberg, 2003Bisley J.W. Goldberg M.E. Neuronal activity in the lateral intraparietal area and spatial attention.Science. 2003; 299: 81-86Crossref PubMed Scopus (653) Google Scholar), along with the one-dimensional model (Ganguli et al., 2008Ganguli S. Bisley J.W. Roitman J.D. Shadlen M.N. Goldberg M.E. Miller K.D. One-dimensional dynamics of attention and decision making in LIP.Neuron. 2008; 58: 15-25Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar) to which it gave rise. The delayed-saccade task of BG is illustrated in Figure 1A (details in Supplemental Information [SI] section 1, available online). During this task, LIP neurons exhibit a large transient visual response to the onset of a saccade target or distractor in the RF, and sustained delay period activity (delay activity) when a saccade is planned to the RF (Figure 1C). When a distractor is flashed away from the target location during the delay period, attention is transiently attracted away from the target location to the distractor location. At the same time, the average visual response level of LIP neurons whose RFs contain the distractor location (the distractor population) rises above the average delay activity level of neurons whose RFs contain the target location (the target population). As the visual activity of the distractor population decays back to baseline, the locus of attention shifts back to the target location. This shift in attention coincides with the shift in the peak of LIP activity from the distractor population to the target population: when the decaying visual activity of the distractor population drops to a level statistically indistinguishable from the sustained delay activity of the target population (the “crossing time”—when the decaying red trace crosses the blue trace in Figure 1C), neither the target nor the distractor location has attentional advantage, whereas 100–250 ms before or after this crossing time, the distractor or target location, respectively, is the clear locus of attention. Further analyses of these results (Bisley and Goldberg, 2006Bisley J.W. Goldberg M.E. Neural correlates of attention and distractibility in the lateral intraparietal area.J. Neurophysiol. 2006; 95: 1696-1717Crossref PubMed Scopus (98) Google Scholar) revealed that this correspondence between activity crossing and attentional switching also held at the level of single LIP neurons. The crossing time of a single neuron is defined as the time at which the neuron’s decaying response to a distractor, on trials in which a distractor is in its RF (distractor trials), crosses its own level of delay activity on trials in which a target is in its RF (target trials). These single-neuron crossing times are surprisingly invariant across neurons and closely aligned with the monkey’s attentional switching time, despite high variability across neurons in their peak visual responses, time constants of visual response decay, and delay period responses. Ganguli et al., 2008Ganguli S. Bisley J.W. Roitman J.D. Shadlen M.N. Goldberg M.E. Miller K.D. One-dimensional dynamics of attention and decision making in LIP.Neuron. 2008; 58: 15-25Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar explained this observation with the proposal that the dynamics of a local network (LN) of LIP neurons are dominated on slow timescales by one multi-neuronal activity pattern (i.e., a pattern, or vector, of relative firing rates across the cells of the network). Throughout this paper, we use the term “local network” (or LN) to mean a network of LIP neurons that share the same RF (explained more fully in the section “Simple model of coupled local networks reconciles the results”). Ganguli et al. proposed that the recurrent connectivity of an LN causes certain multi-neuronal activity patterns to persist longer in the absence of input; given steady input, these slowly decaying patterns also build up to be strongly amplified. If the network has only a single pattern that decays slowly, we refer to it as the network’s “slow mode,” where “mode” is a term borrowed from physics that describes a characteristic pattern of a system’s response. As the visual response to a distractor decays, it becomes dominated by this slow mode after all other patterns decay away. Because the slow mode is more strongly amplified than other patterns, it also dominates steady-state responses, such as delay activity and activity during the initial fixation before target onset (fixation activity). Thus, after the other patterns in the distractor response decay away, the decaying distractor activity and the ongoing delay activity are both dominated by the slow mode, meaning that the pattern of distractor activity across neurons is very nearly a scaled-up version of the delay activity pattern. Then as the distractor activity decays further, it becomes very nearly identical to the delay activity pattern, which happens at the crossing time. Thus, each individual neuron has roughly the same activity in its delay response as in its distractor response at the crossing time, so that all neurons have about the same single-neuron crossing time. This one-dimensional model predicts that multi-neuronal activity patterns that change on slow timescales are all highly correlated with one another because all are dominated by the same strongly amplified pattern. These include fixation and delay activity patterns and, to a lesser extent, slowly decaying visual activity patterns and slowly increasing activity patterns during decision-making tasks. On the other hand, during the initial transient visual response, many other activity patterns are activated, so the transient visual activity pattern is not highly correlated with the steady-state activity patterns. Ganguli et al., 2008Ganguli S. Bisley J.W. Roitman J.D. Shadlen M.N. Goldberg M.E. Miller K.D. One-dimensional dynamics of attention and decision making in LIP.Neuron. 2008; 58: 15-25Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar confirmed these predictions using the following analysis, which reveals network dynamics from the activity of a population of singly recorded neurons. At any millisecond time point t, we represent the trial-averaged activity of a population of N neurons as an N-dimensional vector, r→(t), in an N-dimensional multi-neuronal firing rate space; each of the N elements of r→(t) is the activity of one neuron at time t, averaged over trials. We also compute the N-dimensional fixation activity vector, F→, where each element is the activity of one neuron averaged over the fixation period before target onset and over target trials. Then, at each time point t over the course of the trial, a correlation coefficient is computed between F→ and r→(t). Figure 1E shows that the correlation to fixation activity is indeed high for delay activity or distractor activity after the transient visual response decays away, indicating that fixation, delay, and post-transient distractor activity patterns all lie roughly in a single dimension, corresponding to the dominant activity pattern. The drop in correlation coefficient during the visual response indicates the transient deviation of activity from this one dimension caused by the transient activation of other non-dominant patterns. We continue by describing the second of the two conflicting datasets (Falkner et al., 2010Falkner A.L. Krishna B.S. Goldberg M.E. Surround suppression sharpens the priority map in the lateral intraparietal area.J. Neurosci. 2010; 30: 12787-12797Crossref PubMed Scopus (67) Google Scholar) and how it exhibits large deviations on both fast and slow timescales from the predictions of the one-dimensional model. The task of FK (Figure 1B) is very similar to that of BG. For both tasks, we analyze data in each trial during time windows ending shortly after distractor onset (i.e., before the onset of the probe in the BG task; see Figure 1A), up to which point the two tasks are virtually identical aside from three differences. First, BG used a flashed target while FK presented a target that stayed visible during the delay. This does not result in qualitatively different delay activity levels (compare delay activity between Figures 1C and 1D), consistent with LIP encoding the attentional and saccadic priority of the target location regardless of the visibility of the target. Second, BG randomly interleaved target trials and distractor trials, while FK presented target and distractor trials in blocks. Thus, in the FK experiment, on almost every trial the monkey had an expectation of where the target and distractor would be. This is reflected in higher anticipatory firing on target trials compared to distractor trials during the fixation period before target onset. The third difference is likely to be the key difference that led to different neural responses observed during the two tasks. In the BG task, the target and distractor are in opposite visual quadrants and equidistant from the fixation spot. In the FK task, in contrast, either the target or the distractor is in the RF of the cell being recorded in a given session, and the other stimulus is at the location eliciting maximum surround suppression of the recorded neuron. With this placement of stimuli, a saccade plan to the surround significantly suppressed the visual response to the distractor, while distractor appearance in the surround transiently and weakly, but significantly, suppressed delay activity during saccade planning (Figure 1D; quantified in Falkner et al., 2010Falkner A.L. Krishna B.S. Goldberg M.E. Surround suppression sharpens the priority map in the lateral intraparietal area.J. Neurosci. 2010; 30: 12787-12797Crossref PubMed Scopus (67) Google Scholar). Surround suppression was not observed in the BG dataset (quantified in Bisley and Goldberg, 2006Bisley J.W. Goldberg M.E. Neural correlates of attention and distractibility in the lateral intraparietal area.J. Neurophysiol. 2006; 95: 1696-1717Crossref PubMed Scopus (98) Google Scholar), in which the stimulus locations were not selected for suppression. Other than the surround suppression of response amplitudes, the FK dataset displays the same overall pattern of fixation, visual, and delay activity as the BG dataset (compare Figures 1C and 1D). However, beneath this apparent similarity in population average activity, the network dynamics are radically different; moreover, the FK dynamics appear to clearly violate the predictions of the one-dimensional model. Figure 1F shows the result of the correlation analysis on the FK data. Most strikingly, on distractor trials (red trace), even though the appearance of the target in the surround only minimally affects the mean firing rate of the population, target appearance causes a large, sustained drop in correlation, when the one-dimensional model would predict an unchanging and high level of correlation, as in Figure 1E. This indicates that the activity pattern of the population has changed dramatically while its mean firing rate has remained about the same. Furthermore, the later appearance of the distractor in the RF causes a large, transient rise in correlation that subsequently returns to the steady low level present before distractor onset, when the one-dimensional model would predict the opposite change—a large and transient drop in correlation upon distractor onset, as in Figure 1E. In target trials (blue traces), the difference is more subtle, with target onset evoking a small, sustained drop in correlation, similar to the sustained drop in the BG case, but without the initially larger transient decrease. Note that in the BG dataset, the two trial types are randomly interleaved; thus, the monkey does not know the trial type during the initial fixation, and fixation activities are the same in the two trial types. In the FK dataset, however, fixation activities are different on the two trial types due to the block design. We chose to use the fixation activity on target trials as opposed to distractor trials to calculate correlations because it reveals salient patterns in the network dynamics. Using distractor trial fixation activity is another angle from which to examine the network dynamics that give less informative results, i.e., correlations do not rise and drop saliently over time (Figure S1A). Thus, the results of BG and of FK seem incompatible. The robust one-dimensional dynamics observed in the BG data require that the local anatomical connectivity of LIP selectively amplify only one multi-neuronal activity pattern. How can this same anatomical connectivity realize dynamics that deviate so far from the one activity pattern that it so strongly amplifies? We found the answer in a simple model of the interactions between two coupled LIP LNs. This model replicates the FK findings and yet reduces to the one-dimensional dynamics that characterize the BG findings when the two LNs are not coupled. We model two LNs in LIP, each composed of excitatory (E) and inhibitory (I) neurons that share an RF, with randomly distributed neuronal time constants (Figures 2A and 2B ; see SI section 2.2 for details of the model). Connections between the neurons are sparse, and their strengths are randomly distributed. Within each LN, excitatory connections are, on average, stronger than inhibitory connections. This dominance of excitation is consistent with evidence based on dendritic structure of increased connectivity between excitatory cells in LIP compared to primary sensory cortices (Elston and Rosa, 1997Elston G.N. Rosa M.G. The occipitoparietal pathway of the macaque monkey: comparison of pyramidal cell morphology in layer III of functionally related cortical visual areas.Cereb. Cortex. 1997; 7: 432-452Crossref PubMed Scopus (184) Google Scholar). Such connectivity within an LN, when it’s not connected to another LN, amplifies a single pattern, one of increased activity across most cells, more strongly than all other patterns. The LIP cortical surface contains rough topological maps of visual space (Blatt et al., 1990Blatt G.J. Andersen R.A. Stoner G.R. Visual receptive field organization and cortico-cortical connections of the lateral intraparietal area (area LIP) in the macaque.J. Comp. Neurol. 1990; 299: 421-445Crossref PubMed Scopus (446) Google Scholar, Patel et al., 2010Patel G.H. Shulman G.L. Baker J.T. Akbudak E. Snyder A.Z. Snyder L.H. Corbetta M. Topographic organization of macaque area LIP.Proc. Natl. Acad. Sci. USA. 2010; 107: 4728-4733Crossref PubMed Scopus (50) Google Scholar). Neurons sharing an RF, which are more likely to be located close to each other on the cortical surface, make up an LN in our model. We model the connections of I cells to be restricted to the LN to which they belong, for inhibitory interneurons generally only make short-range projections, whereas E cells can potentially make long-range projections to the other LN. Since no significant interaction between RFs was observed in the BG dataset (quantified in Bisley and Goldberg, 2006Bisley J.W. Goldberg M.E. Neural correlates of attention and distractibility in the lateral intraparietal area.J. Neurophysiol. 2006; 95: 1696-1717Crossref PubMed Scopus (98) Google Scholar), we infer that for these RFs, the corresponding LNs are not directly connected (Figure 2A). In contrast, by maximizing surround suppression, FK selected for RFs that did interact. Since the interaction observed was predominantly suppressive, it’s likely that the excitatory connections from each LN are stronger to the I cells than to the E cells of the other LN. For simplicity, we model the across-network connections as being from the E cells of each LN to the other LN’s I cells only, with sparse and random connectivity (Figure 2B). Our results do not change if we include weaker across-network E-to-E connections (data not shown). We use a standard linear firing rate model (SI section 2.2; Dayan and Abbott, 2005Dayan P. Abbott L.F. Theoretical Neuroscience. The MIT Press, 2005Google Scholar) to simulate the trial-averaged activity in the experiments. We do not explicitly simulate single trials,for we have no knowledge of the single-trial population dynamics during the tasks. The experiments involve a variety of sensory, motor, and cognitive processes that likely give rise to a variety of external inputs to LIP during a trial, which we model as the following four types. (1) Fixation input, which is spontaneous firing from the external input sources when there is no stimulus in or saccade plan to the RF, such as during the fixation period. (2) Visual input, which is bottom-up input to an LN when a visual stimulus is in the RF, which is strong upon stimulus onset and becomes weak as the stimulus is sustained. Visual input arrives from areas that could include V2, V3, V3A, V4, middle temporal area (MT), and inferotemporal cortex (Baizer et al., 1991Baizer J.S. Ungerleider L.G. Desimone R. Organization of visual inputs to the inferior temporal and posterior parietal cortex in macaques.J. Neurosci. 1991; 11: 168-190Crossref PubMed Google Scholar, Blatt et al., 1990Blatt G.J. Andersen R.A. Stoner G.R. Visual receptive field organization and cortico-cortical connections of the lateral intraparietal area (area LIP) in the macaque.J. Comp. Neurol. 1990; 299: 421-445Crossref PubMed Scopus (446) Google Scholar, Lewis and Van Essen, 2000Lewis J.W. Van Essen D.C. Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey.J. Comp. Neurol. 2000; 428: 112-137Crossref PubMed Scopus (644) Google Scholar). (3) Delay input, which is persistent top-down input to an LN when a saccade is being planned to the RF, arriving from frontal areas such as the frontal eye field (FEF) or dorsolateral prefrontal cortex (dlPFC; Blatt et al., 1990Blatt G.J. Andersen R.A. Stoner G.R. Visual receptive field organization and cortico-cortical connections of the lateral intraparietal area (area LIP) in the macaque.J. Comp. Neurol. 1990; 299: 421-445Crossref PubMed Scopus (446) Google Scholar, Stanton et al., 1995Stanton G.B. Bruce C.J. Goldberg M.E. Topography of projections to posterior cortical areas from the macaque frontal eye fields.J. Comp. Neurol. 1995; 353: 291-305Crossref PubMed Scopus (293) Google Scholar; in SI section 3, we discuss other possible mechanisms underlying delay activity and their implications for our model). (4) Expectation input, which is top-down input to one LN during the fixation period before target onset, when the animal is in a block of trials during which the target always appears in the RF of that LN (as in the blocked experiment of FK). Expectation input likely also arrives from frontal areas such as FEF or dlPFC (Coe et al., 2002Coe B. Tomihara K. Matsuzawa M. Hikosaka O. Visual and anticipatory bias in three cortical eye fields of the monkey during an adaptive decision-making task.J. Neurosci. 2002; 22: 5081-5090Crossref PubMed Google Scholar, Roesch and Olson, 2003Roesch M.R. Olson C.R. Impact of expected reward on neuronal activity in prefrontal cortex, frontal and supplementary eye fields and premotor cortex.J. Neurophysiol. 2003; 90: 1766-1789Crossref PubMed Scopus (232) Google Scholar). The total external input to the neurons at any time is the sum of one or more of these four types of input. For each of the four types of input, input to each cell is independently drawn from a uniform distribution, with ranges of the distributions chosen to fit experimentally observed neural responses. Thus, it is important that the inputs from different sources are uncorrelated. In addition, the external input contains weak, temporally correlated noise that is independent for different neurons, simply to produce small firing rate fluctuations similar to those seen in the experiments. In the experiments, different neurons are recorded from different LIP locations and have different RF positions. As in Ganguli et al., 2008Ganguli S. Bisley J.W. Roitman J.D. Shadlen M.N. Goldberg M.E. Miller K.D. One-dimensional dynamics of attention and decision making in LIP.Neuron. 2008; 58: 15-25Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar, we interpret this to mean that these neurons are situated in different LNs, which share the same set of connectivity, neuronal, and input statistics. To model this, we run the simulation multiple times, each time with a different random instantiation of network connectivity, neuronal time constants, and input patterns, and “record” from a single randomly chosen cell during each simulation. Each simulation includes target and distractor trials for the recorded cell. Figures 2C and 2D show the population peristimulus time histograms (PSTHs) from such simulations of the BG (Figure 2C) and FK (Figure 2D) experiments, which reproduce the experimentally observed firing patterns, including the observed absence or presence of surround interactions. More significantly, our model reproduces the apparently conflicting network dynamics of the two experiments, as revealed from the correlation analysis: the BG model shows one-dimensional dynamics on slow timescales (Figure 2E), and the FK model shows the same higher-dimensional dynamics as experimentally observed (Figure 2F). If we compute correlatio" @default.
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• W2564523059 title "Coupling between One-Dimensional Networks Reconciles Conflicting Dynamics in LIP and Reveals Its Recurrent Circuitry" @default.
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• W2564523059 doi "https://doi.org/10.1016/j.neuron.2016.11.023" @default.
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