Glutamate, obsessive-compulsive disorder, schizophrenia, and the stability of cortical attractor neuronal networks

Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling

BACKGROUND

Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of precisely what common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD.

METHODS

We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of common NLP measures of derailment (semantic distance between consecutive words or sentences) and tangentiality (how quickly meaning drifts away from the topic) in detecting and dissociating the 2 underlying impairments.

RESULTS

Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but decreased by limiting memory span. An NLP measure of tangentiality was uniquely predicted by limited memory span. The effects of limited memory span were nonmonotonic in that forgetting the global context resulted in sentences that were semantically closer to their local, intermediate context. Finally, different methods for encoding the meaning of sentences varied dramatically in performance.

CONCLUSIONS

This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we accompany the paper with a hands-on Python tutorial.

Association of Peripheral Inflammatory Biomarkers and Growth Factors Levels with Sex, Therapy and Other Clinical Factors in Schizophrenia and Patient Stratification Based on These Data

Multiple lines of evidence are known to confirm the pro-inflammatory state of some patients with schizophrenia and the involvement of inflammatory mechanisms in the pathogenesis of psychosis. The concentration of peripheral biomarkers is associated with the severity of inflammation and can be used for patient stratification. Here, we analyzed changes in serum concentrations of cytokines (IL-1β, IL-2, IL-4, IL-6, IL-10, IL-21, APRIL, BAFF, PBEF/Visfatin, IFN-α, and TNF-α) and growth/neurotrophic factors (GM-CSF, NRG1-β1, NGF-β, and GDNF) in patients with schizophrenia in an exacerbation phase. IL-1β, IL-2, IL-4, IL-6, BAFF, IFN-α, GM-CSF, NRG1-β1, and GDNF increased but TNF-α and NGF-β decreased in schizophrenia compared to healthy individuals. Subgroup analysis revealed the effect of sex, prevalent symptoms, and type of antipsychotic therapy on biomarker levels. Females, patients with predominantly negative symptoms, and those taking atypical antipsychotics had a more pro-inflammatory phenotype. Using cluster analysis, we classified participants into "high" and "low inflammation" subgroups. However, no differences were found in the clinical data of patients in these subgroups. Nevertheless, more patients (17% to 25.5%) than healthy donors (8.6% to 14.3%) had evidence of a pro-inflammatory condition depending on the clustering approach used. Such patients may benefit from personalized anti-inflammatory therapy.

Reduced resilience of brain state transitions in anti-N-methyl-D-aspartate receptor encephalitis

Patients with anti-N-methyl-aspartate receptor (NMDA) receptor encephalitis suffer from a severe neuropsychiatric syndrome, yet most patients show no abnormalities in routine magnetic resonance imaging. In contrast, advanced neuroimaging studies have consistently identified disrupted functional connectivity in these patients, with recent work suggesting increased volatility of functional state dynamics. Here, we investigate these network dynamics through the spatiotemporal trajectory of meta-state transitions, yielding a time-resolved account of brain state exploration in anti-NMDA receptor encephalitis. To this end, resting-state functional magnetic resonance imaging data were acquired in 73 patients with anti-NMDA receptor encephalitis and 73 age- and sex-matched healthy controls. Time-resolved functional connectivity was clustered into brain meta-states, giving rise to a time-resolved transition network graph with states as nodes and transitions between brain meta-states as weighted, directed edges. Network topology, robustness and transition cost of these transition networks were compared between groups. Transition networks of patients showed significantly lower local efficiency (t = -2.41, p_FDR  = .029), lower robustness (t = -2.01, p_FDR  = .048) and higher leap size (t = 2.18, p_FDR  = .037) compared with controls. Furthermore, the ratio of within-to-between module transitions and state similarity was significantly lower in patients. Importantly, alterations of brain state transitions correlated with disease severity. Together, these findings reveal systematic alterations of transition networks in patients, suggesting that anti-NMDA receptor encephalitis is characterized by reduced stability of brain state transitions and that this reduced resilience of transition networks plays a clinically relevant role in the manifestation of the disease.

Psychosis symptoms associated with Niemann-Pick disease type C

Niemann-Pick disease type C (NP-C) is a severe neurovisceral lipid storage disease that results in the accumulation of unesterified cholesterol in lysosomes or endosomes. The clinical presentations of NP-C are variable which include visceral symptoms, neurologic symptoms and psychiatric symptoms. Psychosis is the most common psychiatric manifestation of NP-C and is indistinguishable from a typical psychosis presentation of schizophrenia. The common psychotic presentations in NP-C include visual hallucinations, delusions, auditory hallucinations and thought disorders. Psychosis symptoms are more common in adult or adolescent-onset forms compared with pediatric-onset forms. The underlying pathophysiology of psychosis in NP-C is most probably due to dysconnectivity particularly between frontotemporal connectivity and subcortical structures. NP-C sometimes is mistaken for schizophrenia which causes delay in treatment due to lack of awareness and literature review. This review aims to summarize the relevant case reports on psychosis symptoms in NP-C and discuss the genetics and pathophysiology underlying the condition.

Beyond the disconnectivity hypothesis of schizophrenia

To go beyond the disconnectivity hypothesis of schizophrenia, directed (effective) connectivity was measured between 94 brain regions, to provide evidence on the source of the changes in schizophrenia and a mechanistic model. Effective connectivity (EC) was measured in 180 participants with schizophrenia and 208 controls. For the significantly different effective connectivities in schizophrenia, on average the forward (stronger) effective connectivities were smaller, whereas the backward connectivities tended to be larger. Further, higher EC in schizophrenia was found from the precuneus and posterior cingulate cortex (PCC) to areas such as the parahippocampal, hippocampal, temporal, fusiform, and occipital cortices. These are backward effective connectivities and were positively correlated with the positive symptoms of schizophrenia. Lower effective connectivities were found from temporal and other regions and were negatively correlated with the symptoms, especially the negative and general symptoms. Further, a signal variance parameter was increased for areas that included the parahippocampal gyrus and hippocampus, consistent with the hypothesis that hippocampal overactivity is involved in schizophrenia. This investigation goes beyond the disconnectivity hypothesis by drawing attention to differences in schizophrenia between backprojections and forward connections, with the backward connections from the precuneus and PCC implicated in memory stronger in schizophrenia.

Attractor cortical neurodynamics, schizophrenia, and depression

The local recurrent collateral connections between cortical neurons provide a basis for attractor neural networks for memory, attention, decision-making, and thereby for many aspects of human behavior. In schizophrenia, a reduction of the firing rates of cortical neurons, caused for example by reduced NMDA receptor function or reduced spines on neurons, can lead to instability of the high firing rate attractor states that normally implement short-term memory and attention in the prefrontal cortex, contributing to the cognitive symptoms. Reduced NMDA receptor function in the orbitofrontal cortex by reducing firing rates may produce negative symptoms, by reducing reward, motivation, and emotion. Reduced functional connectivity between some brain regions increases the temporal variability of the functional connectivity, contributing to the reduced stability and more loosely associative thoughts. Further, the forward projections have decreased functional connectivity relative to the back projections in schizophrenia, and this may reduce the effects of external bottom-up inputs from the world relative to internal top-down thought processes. Reduced cortical inhibition, caused by a reduction of GABA neurotransmission, can lead to instability of the spontaneous firing states of cortical networks, leading to a noise-induced jump to a high firing rate attractor state even in the absence of external inputs, contributing to the positive symptoms of schizophrenia. In depression, the lateral orbitofrontal cortex non-reward attractor network system is over-connected and has increased sensitivity to non-reward, providing a new approach to understanding depression. This is complemented by under-sensitivity and under-connectedness of the medial orbitofrontal cortex reward system in depression.

Computational Predictions for OCD Pathophysiology and Treatment: A Review

Obsessive compulsive disorder (OCD) can manifest as a debilitating disease with high degrees of co-morbidity as well as clinical and etiological heterogenity. However, the underlying pathophysiology is not clearly understood. Computational psychiatry is an emerging field in which behavior and its neural correlates are quantitatively analyzed and computational models are developed to improve understanding of disorders by comparing model predictions to observations. The aim is to more precisely understand psychiatric illnesses. Such computational and theoretical approaches may also enable more personalized treatments. Yet, these methodological approaches are not self-evident for clinicians with a traditional medical background. In this mini-review, we summarize a selection of computational OCD models and computational analysis frameworks, while also considering the model predictions from a perspective of possible personalized treatment. The reviewed computational approaches used dynamical systems frameworks or machine learning methods for modeling, analyzing and classifying patient data. Bayesian interpretations of probability for model selection were also included. The computational dissection of the underlying pathology is expected to narrow the explanatory gap between the phenomenological nosology and the neuropathophysiological background of this heterogeneous disorder. It may also contribute to develop biologically grounded and more informed dimensional taxonomies of psychopathology.

Functional Connectivity Changes in Obsessive-Compulsive Disorder Correspond to Interference Control and Obsessions Severity

Introduction: Deficits in neurocognitive mechanisms such as inhibition control and cognitive flexibility have been suggested to mediate the symptoms in obsessive-compulsive disorder (OCD). These mechanisms are proposedly controlled by the "affective" and "executive" orbitofronto-striato-thalamo-cortical (CSTC) circuits with well-documented morphological and functional alterations in OCD that are associated with OCD symptoms. The precuneus region has been suggested in OCD as another key structure associated with the mechanism of "thought-action fusion." Our study aimed to elucidate the association of the altered functional coupling of the CSTC nodes (and precuneus), the OCD symptoms, and interference control/cognitive flexibility. Methods: In a group of 36 (17 medicated and 19 drug-free) OCD patients and matched healthy volunteers, we tested functional connectivity (FC) within the constituents of the dorsolateral prefrontal cortex "executive" CSTC, the orbitofrontal cortex/anterior cingulate "affective" CSTC, and precuneus. The functional connections showing the strongest effects were subsequently entered as explanatory variables to multiple regression analyses to identify possible associations between observed alterations of functional coupling and cognitive (Stroop test) and clinical measures (obsessions, compulsions, and anxiety level). Results: We observed increased FC (FWE p < 0.05 corr.) between CSTC seeds and regions of the parieto-occipital cortex, and between the precuneus and the angular gyrus and dorsolateral prefrontal cortex. Decreased FC was observed within the CSTC loop (caudate nucleus and thalamus) and between the anterior cingulate cortex and the limbic lobe. Linear regression identified a relationship between the altered functional coupling of thalamus with the right somatomotor parietal cortex and the Stroop color-word score. Similar association of thalamus FC has been identified also for obsessions severity. No association was observed for compulsions and anxiety. Conclusions: Our findings demonstrate altered FC in OCD patients with a prevailing increase in FC originating in CSTC regions toward other cortical areas, and a decrease in FC within the constituents of CSTC loops. Moreover, our results support the role of precuneus in OCD. The association of the cognitive and clinical symptoms with the FC between the thalamus and somatomotor cortex indicates that cognitive flexibility and inhibitory control are strongly linked and both mechanisms might contribute to the symptomatology of OCD.

Neural and neuronal discoordination in schizophrenia: From ensembles through networks to symptoms

Despite the substantial knowledge accumulated by past research, the exact mechanisms of the pathogenesis of schizophrenia and causal treatments still remain unclear. Deficits of cognition and information processing in schizophrenia are today often viewed as the primary and core symptoms of this devastating disorder. These deficits likely result from disruptions in the coordination of neuronal and neural activity. The aim of this review is to bring together convergent evidence of discoordinated brain circuits in schizophrenia at multiple levels of resolution, ranging from principal cells and interneurons, neuronal ensembles and local circuits, to large-scale brain networks. We show how these aberrations could underlie deficits in cognitive control and other higher order cognitive-behavioural functions. Converging evidence from both animal models and patients with schizophrenia is presented in an effort to gain insight into common features of deficits in the brain information processing in this disorder, marked by disruption of several neurotransmitter and signalling systems and severe behavioural outcomes.

Neural dynamics in co-morbid schizophrenia and OCD: A computational approach

The co-morbidity of obsessive-compulsive disorder (OCD) and schizophrenia is higher than what would be expected by chance and the common underlying neuropathophysiology is not well understood. Repetitive stereotypes and routines can be caused by perseverative thoughts and motor sequences in both of these disorders. We extended a previously published computational model to investigate cortico-striatal network dynamics. Given the considerable overlap in symptom phenomenology and the high degree of co-morbidity between OCD and schizophrenia, we examined the dynamical consequences of functional connectivity variations in the overlapping network. This was achieved by focusing on the emergence of network oscillatory activity and examining parameter sensitivity. Opposing activity levels are present in orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC) in schizophrenia and OCD. We found that with over-compensation of the primary pathology, emergence of the other disorder can occur. The oscillatory behavior is delicately modulated by connections between the OFC/ACC to the ventral and dorsal striatum and by the coupling between the ACC and dorsolateral prefrontal cortex (DLPFC). Modulation on cortical self-inhibition (e.g. serotonin reuptake inhibitor treatment) together with dopaminergic input to the striatum (e.g. anti-dopaminergic medication) has non-trivial complex effects on the network oscillatory behavior, with an optimal modulatory window. Additionally, there are several disruption mechanisms and compensatory processes in the cortico-striato-thalamic network which may contribute to the underlying neuropathophysiology and clinical heterogeneity in schizo-obsessive spectrum disorders. Our mechanistic model predicts that dynamic over-compensation of the primarily occuring neuropathophysiology can lead to the secondary co-morbid disease.

The storage and recall of memories in the hippocampo-cortical system

A quantitative computational theory of the operation of the hippocampus as an episodic memory system is described. The CA3 system operates as a single attractor or autoassociation network (1) to enable rapid one-trial associations between any spatial location (place in rodents or spatial view in primates) and an object or reward and (2) to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, which is also important in episodic memory. The dentate gyrus performs pattern separation by competitive learning to create sparse representations producing, for example, neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells generate, by the very small number of mossy fibre connections to CA3, a randomizing pattern separation effect that is important during learning but not recall and that separates out the patterns represented by CA3 firing as being very different from each other. This is optimal for an unstructured episodic memory system in which each memory must be kept distinct from other memories. The direct perforant path input to CA3 is quantitatively appropriate for providing the cue for recall in CA3 but not for learning. The CA1 recodes information from CA3 to set up associatively learned backprojections to the neocortex to allow the subsequent retrieval of information to the neocortex, giving a quantitative account of the large number of hippocampo-neocortical and neocortical-neocortical backprojections. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described and support the theory.

Impairment of neural coordination in hippocampal neuronal ensembles after a psychotomimetic dose of dizocilpine

The discoordination hypothesis of schizophrenia posits discoordination of neural activity as the central mechanism that underlies some psychotic symptoms (including 'hallmark' cognitive symptoms) of schizophrenia. To test this proposition, we studied the activity of hippocampal neurons in urethane anesthetized Long Evans rats after 0.15mg/kg dizocilpine (MK-801), an N-Methyl-d-aspartate (NMDA) glutamate receptor antagonist, which can cause psychotic symptoms in humans and cognitive control impairments in animals. We observed that MK-801 altered the temporal coordination, but not rate, of neuronal firing. Coactivation between neurons increased, driven primarily by increased coincident firing of cell pairs that did not originally fire together before MK-801 injection. Increased pairwise coactivation manifested as disorganized discharge on the level of neuronal ensembles, which in turn could lead to disorganization in information processing. Disorganization of neuronal activity after a psychotomimetic dose of MK-801 supports the discoordination hypothesis of psychosis.

Validity of Quinpirole Sensitization Rat Model of OCD: Linking Evidence from Animal and Clinical Studies

Obsessive-compulsive disorder (OCD) is a neuropsychiatric disorder with 1–3% prevalence. OCD is characterized by recurrent thoughts (obsessions) and repetitive behaviors (compulsions). The pathophysiology of OCD remains unclear, stressing the importance of pre-clinical studies. The aim of this article is to critically review a proposed animal model of OCD that is characterized by the induction of compulsive checking and behavioral sensitization to the D2/D3 dopamine agonist quinpirole. Changes in this model have been reported at the level of brain structures, neurotransmitter systems and other neurophysiological aspects. In this review, we consider these alterations in relation to the clinical manifestations in OCD, with the aim to discuss and evaluate axes of validity of this model. Our analysis shows that some axes of validity of quinpirole sensitization model (QSM) are strongly supported by clinical findings, such as behavioral phenomenology or roles of brain structures. Evidence on predictive validity is contradictory and ambiguous. It is concluded that this model is useful in the context of searching for the underlying pathophysiological basis of the disorder because of the relatively strong biological similarities with OCD.

Stochastic Dynamics Underlying Cognitive Stability and Flexibility

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences.

In this work we develop a neurophysiologically inspired dynamical model that is capable of solving a complex behavioral task testing cognitive stability and flexibility. We can individually fit the behavior of each of 20 human subjects that conducted this stability-flexibility task during functional magnetic resonance imaging (fMRI). The physiological nature of our model allows us to estimate the energy consumption of the rule-representing module, which we use to predict the hemodynamic fMRI response. Through this model-based prediction, we localize the rule module to a frontoparietal network known to be required for cognitive stability and flexibility. In this way we both validate our model, which is based on noisy attractor dynamics, and specify the computational role of a cortical network that is well-established in human neuroimaging research. Additionally, we quantify the individual stability of the rule-representing states and relate this stability to individual differences in energy consumption during task switching versus distractor inhibition. Hereby we show that the activation of a thalamocorticostriatal network involved in the dopaminergic modulation of cognitive stability is modulated by the model-derived stability of the frontoparietal rule-representing network. Altogether, we show that noisy dynamic systems are likely to implement the basic computations underlying cognitive stability and flexibility.

Altered functional connectivity links in neuroleptic-naïve and neuroleptic-treated patients with schizophrenia, and their relation to symptoms including volition

In order to analyze functional connectivity in untreated and treated patients with schizophrenia, resting-state fMRI data were obtained for whole-brain functional connectivity analysis from 22 first-episode neuroleptic-naïve schizophrenia (NNS), 61 first-episode neuroleptic-treated schizophrenia (NTS) patients, and 60 healthy controls (HC). Reductions were found in untreated and treated patients in the functional connectivity between the posterior cingulate gyrus and precuneus, and this was correlated with the reduction in volition from the Positive and Negative Symptoms Scale (PANSS), that is in the willful initiation, sustenance, and control of thoughts, behavior, movements, and speech, and with the general and negative symptoms. In addition in both patient groups interhemispheric functional connectivity was weaker between the orbitofrontal cortex, amygdala and temporal pole. These functional connectivity changes and the related symptoms were not treated by the neuroleptics. Differences between the patient groups were that there were more strong functional connectivity links in the NNS patients (including in hippocampal, frontal, and striatal circuits) than in the NTS patients. These findings with a whole brain analysis in untreated and treated patients with schizophrenia provide evidence on some of the brain regions implicated in the volitional, other general, and negative symptoms, of schizophrenia that are not treated by neuroleptics so have implications for the development of other treatments; and provide evidence on some brain systems in which neuroleptics do alter the functional connectivity.

The NMDAr antagonist ketamine interferes with manipulation of information for transitive inference reasoning in non-human primates

One of the most remarkable traits of highly encephalized animals is their ability to manipulate knowledge flexibly to infer logical relationships. Operationally, the corresponding cognitive process can be defined as reasoning. One hypothesis is that this process relies on the reverberating activity of glutamate neural circuits, sustained by NMDA receptor (NMDAr) mediated synaptic transmission, in both parietal and prefrontal areas. We trained two macaque monkeys to perform a form of deductive reasoning - the transitive inference task - in which they were required to learn the relationship between six adjacent items in a single session and then deduct the relationship between nonadjacent items that had not been paired in the learning phase. When the animals had learned the sequence, we administered systemically a subanaesthetic dose of ketamine (a NMDAr antagonist) and measured their performance on learned and novel problems. We observed impairments in determining the relationship between novel pairs of items. Our results are consistent with the hypothesis that transitive inference premises are integrated during learning in a unified representation and that reducing NMDAr activity interferes with the use of this mental model, when decisions are required in comparing pairs of items that have not been learned.

Functional dysconnectivity in schizophrenia and its relationship to neural synchrony

Schizophrenia is a debilitating disorder of unknown cause. There is increasing momentum to consider functional dysconnectivity as an endophenotype of schizophrenia, and in particular, how it relates to cognition as a core feature of the disorder. Here, the authors review the conceptual models of functional dysconnectivity in schizophrenia to date, the evidence they are based on and some of the limitations of these models. The authors then propose 'neural synchrony' as a potential mechanism for functional dysconnectivity and review the current state of evidence for a link between neural synchrony and cognition in schizophrenia across behavioral, physiological, brain imaging, neurochemical and neurogenetic units of enquiry. The authors conclude by outlining the unmet needs in this field and give an outlook on how to fill these gaps.

A quantitative theory of the functions of the hippocampal CA3 network in memory

A quantitative computational theory of the operation of the hippocampal CA3 system as an autoassociation or attractor network used in episodic memory system is described. In this theory, the CA3 system operates as a single attractor or autoassociation network to enable rapid, one-trial, associations between any spatial location (place in rodents, or spatial view in primates) and an object or reward, and to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, also important in episodic memory. The dentate gyrus (DG) performs pattern separation by competitive learning to produce sparse representations suitable for setting up new representations in CA3 during learning, producing for example neurons with place-like fields from entorhinal cortex grid cells. The dentate granule cells produce by the very small number of mossy fiber (MF) connections to CA3 a randomizing pattern separation effect important during learning but not recall that separates out the patterns represented by CA3 firing to be very different from each other, which is optimal for an unstructured episodic memory system in which each memory must be kept distinct from other memories. The direct perforant path (pp) input to CA3 is quantitatively appropriate to provide the cue for recall in CA3, but not for learning. Tests of the theory including hippocampal subregion analyses and hippocampal NMDA receptor knockouts are described, and support the theory.

Noise in attractor networks in the brain produced by graded firing rate representations

Representations in the cortex are often distributed with graded firing rates in the neuronal populations. The firing rate probability distribution of each neuron to a set of stimuli is often exponential or gamma. In processes in the brain, such as decision-making, that are influenced by the noise produced by the close to random spike timings of each neuron for a given mean rate, the noise with this graded type of representation may be larger than with the binary firing rate distribution that is usually investigated. In integrate-and-fire simulations of an attractor decision-making network, we show that the noise is indeed greater for a given sparseness of the representation for graded, exponential, than for binary firing rate distributions. The greater noise was measured by faster escaping times from the spontaneous firing rate state when the decision cues are applied, and this corresponds to faster decision or reaction times. The greater noise was also evident as less stability of the spontaneous firing state before the decision cues are applied. The implication is that spiking-related noise will continue to be a factor that influences processes such as decision-making, signal detection, short-term memory, and memory recall even with the quite large networks found in the cerebral cortex. In these networks there are several thousand recurrent collateral synapses onto each neuron. The greater noise with graded firing rate distributions has the advantage that it can increase the speed of operation of cortical circuitry.