Modulating the Granularity of Category Formation by Global Cortical States

The unsupervised categorization of sensory stimuli is typically attributed to feedforward processing in a hierarchy of cortical areas. This purely sensory-driven view of cortical processing, however, ignores any internal modulation, e.g., by top-down attentional signals or neuromodulator release. To isolate the role of internal signaling on category formation, we consider an unbroken continuum of stimuli without intrinsic category boundaries. We show that a competitive network, shaped by recurrent inhibition and endowed with Hebbian and homeostatic synaptic plasticity, can enforce stimulus categorization. The degree of competition is internally controlled by the neuronal gain and the strength of inhibition. Strong competition leads to the formation of many attracting network states, each being evoked by a distinct subset of stimuli and representing a category. Weak competition allows more neurons to be co-active, resulting in fewer but larger categories. We conclude that the granularity of cortical category formation, i.e., the number and size of emerging categories, is not simply determined by the richness of the stimulus environment, but rather by some global internal signal modulating the network dynamics. The model also explains the salient non-additivity of visual object representation observed in the monkey inferotemporal (IT) cortex. Furthermore, it offers an explanation of a previously observed, demand-dependent modulation of IT activity on a stimulus categorization task and of categorization-related cognitive deficits in schizophrenic patients

Experience-driven formation of parts-based representations in a model of layered visual memory

Growing neuropsychological and neurophysiological evidence suggests that the visual cortex uses parts-based representations to encode, store and retrieve relevant objects. In such a scheme, objects are represented as a set of spatially distributed local features, or parts, arranged in stereotypical fashion. To encode the local appearance and to represent the relations between the constituent parts, there has to be an appropriate memory structure formed by previous experience with visual objects. Here, we propose a model how a hierarchical memory structure supporting efficient storage and rapid recall of parts-based representations can be established by an experience-driven process of self-organization. The process is based on the collaboration of slow bidirectional synaptic plasticity and homeostatic unit activity regulation, both running at the top of fast activity dynamics with winner-take-all character modulated by an oscillatory rhythm. These neural mechanisms lay down the basis for cooperation and competition between the distributed units and their synaptic connections. Choosing human face recognition as a test task, we show that, under the condition of open-ended, unsupervised incremental learning, the system is able to form memory traces for individual faces in a parts-based fashion. On a lower memory layer the synaptic structure is developed to represent local facial features and their interrelations, while the identities of different persons are captured explicitly on a higher layer. An additional property of the resulting representations is the sparseness of both the activity during the recall and the synaptic patterns comprising the memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in Computational Neuroscience (Special Issue on Complex Systems Science and Brain Dynamics), http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009

Multiscale and multimodal network dynamics underpinning working memory

Working memory (WM) allows information to be stored and manipulated over short time scales. Performance on WM tasks is thought to be supported by the frontoparietal system (FPS), the default mode system (DMS), and interactions between them. Yet little is known about how these systems and their interactions relate to individual differences in WM performance. We address this gap in knowledge using functional MRI data acquired during the performance of a 2-back WM task, as well as diffusion tensor imaging data collected in the same individuals. We show that the strength of functional interactions between the FPS and DMS during task engagement is inversely correlated with WM performance, and that this strength is modulated by the activation of FPS regions but not DMS regions. Next, we use a clustering algorithm to identify two distinct subnetworks of the FPS, and find that these subnetworks display distinguishable patterns of gene expression. Activity in one subnetwork is positively associated with the strength of FPS-DMS functional interactions, while activity in the second subnetwork is negatively associated. Further, the pattern of structural linkages of these subnetworks explains their differential capacity to influence the strength of FPS-DMS functional interactions. To determine whether these observations could provide a mechanistic account of large-scale neural underpinnings of WM, we build a computational model of the system composed of coupled oscillators. Modulating the amplitude of the subnetworks in the model causes the expected change in the strength of FPS-DMS functional interactions, thereby offering support for a mechanism in which subnetwork activity tunes functional interactions. Broadly, our study presents a holistic account of how regional activity, functional interactions, and structural linkages together support individual differences in WM in humans

Self Organisation and Hierarchical Concept Representation in Networks of Spiking Neurons

The aim of this work is to introduce modular processing mechanisms for cortical functions implemented in networks of spiking neurons. Neural maps are a feature of cortical processing found to be generic throughout sensory cortical areas, and self-organisation to the fundamental properties of input spike trains has been shown to be an important property of cortical organisation. Additionally, oscillatory behaviour, temporal coding of information, and learning through spike timing dependent plasticity are all frequently observed in the cortex. The traditional self-organising map (SOM) algorithm attempts to capture the computational properties of this cortical self-organisation in a neural network. As such, a cognitive module for a spiking SOM using oscillations, phasic coding and STDP has been implemented. This model is capable of mapping to distributions of input data in a manner consistent with the traditional SOM algorithm, and of categorising generic input data sets. Higher-level cortical processing areas appear to feature a hierarchical category structure that is founded on a feature-based object representation. The spiking SOM model is therefore extended to facilitate input patterns in the form of sets of binary feature-object relations, such as those seen in the field of formal concept analysis. It is demonstrated that this extended model is capable of learning to represent the hierarchical conceptual structure of an input data set using the existing learning scheme. Furthermore, manipulations of network parameters allow the level of hierarchy used for either learning or recall to be adjusted, and the network is capable of learning comparable representations when trained with incomplete input patterns. Together these two modules provide related approaches to the generation of both topographic mapping and hierarchical representation of input spaces that can be potentially combined and used as the basis for advanced spiking neuron models of the learning of complex representations

Adaptive Gain Modulation in V1 Explains Contextual Modifications during Bisection Learning

The neuronal processing of visual stimuli in primary visual cortex (V1) can be modified by perceptual training. Training in bisection discrimination, for instance, changes the contextual interactions in V1 elicited by parallel lines. Before training, two parallel lines inhibit their individual V1-responses. After bisection training, inhibition turns into non-symmetric excitation while performing the bisection task. Yet, the receptive field of the V1 neurons evaluated by a single line does not change during task performance. We present a model of recurrent processing in V1 where the neuronal gain can be modulated by a global attentional signal. Perceptual learning mainly consists in strengthening this attentional signal, leading to a more effective gain modulation. The model reproduces both the psychophysical results on bisection learning and the modified contextual interactions observed in V1 during task performance. It makes several predictions, for instance that imagery training should improve the performance, or that a slight stimulus wiggling can strongly affect the representation in V1 while performing the task. We conclude that strengthening a top-down induced gain increase can explain perceptual learning, and that this top-down signal can modify lateral interactions within V1, without significantly changing the classical receptive field of V1 neurons

The psychiatric risk gene Cacna1c regulates mitochondrial function in cellular stress responses

Affective disorders such as major depression and bipolar disorder are among the most prevalent forms of mental illness, and their pathophysiology involves complex interactions between genetic and environmental risk factors. However, the underlying mechanisms explaining how genetic and environmental alterations affect the risk for psychiatric disorders are still largely unknown. Confirmed by several genome-wide association studies over the past ten years, CACNA1C represents one of the strongest and most replicable psychiatric risk genes. Besides genetic predispositions, environmental influences such as childhood maltreatment or chronic stress also contribute to disease vulnerability. In addition, increasing evidence suggests a crucial role for mitochondrial dysfunction, oxidative stress, excitotoxicity, and neuroinflammation in the development of major neuropsychiatric disorders. Furthermore, mitochondrial dysfunction in peripheral blood mononuclear cells (PBMCs) is currently being discussed as a potential biomarker for affective disorders supporting early diagnosis, control of disease progression, and evaluation of treatment response. In a translational setting, the present project focused on the effects of defined gene-environment interactions on brain mitochondrial integrity and function in order to provide new insights into pathophysiological mechanisms of affective disorders and to identify novel therapeutic targets with potential relevance for future treatment strategies. Using immortalized mouse hippocampal HT22 cells, a well-established model system to investigate glutamate-mediated oxidative stress, it was demonstrated that both siRNA-mediated Cacna1c gene silencing and L-type calcium channel (LTCC) blockade with nimodipine significantly prevented the glutamate-mediated rise in lipid peroxidation, excessive ROS formation, collapse of mitochondrial membrane potential, loss of ATP, reduction in mitochondrial respiration, and ultimately neuronal cell death. Moreover, both Cacna1c knockdown and pharmacological LTCC inhibition altered CaV1.2-dependent gene transcription, thereby suppressing the glutamate-induced expression of the inner mitochondrial membrane calcium uptake protein MCU. Accordingly, downregulation of Cacna1c substantially diminished the elevation in mitochondrial calcium levels after glutamate treatment. In the employed paradigm of oxidative glutamate toxicity, Cacna1c depletion also protected against detrimental mitochondrial fission and stimulated mitochondrial biogenesis without affecting mitophagy, thus promoting the turnover of mitochondria and preventing the accumulation of dysfunctional mitochondria in neuronal HT22 cells. These data imply that upstream genetic modifications, e.g. reduced CACNA1C expression, converge to control mitochondrial function, resulting in cellular resilience against oxidative stress. In primary cortical rat neurons, heterozygous Cacna1c knockout partially reduced Cacna1c expression but had no impact on either initial increase in [Ca2+]i or delayed perturbations in mitochondrial bioenergetics, ATP levels, and cell viability in response to glutamate-mediated excitotoxicity. Furthermore, Cacna1c mRNA and protein expression levels were subject to strong regulation and degradation in this model of neuronal excitotoxicity. Partial neuroprotection against long-term glutamate toxicity by pharmacological LTCC blockade highlighted a potential dose-effect-dependency and the involvement of LTCCs in this cell death pathway. In primary rat microglia cultures, both Cacna1c haploinsufficiency and nimodipine treatment were associated with reduced morphological changes and glycolytic metabolism upon lipopolysaccharide (LPS) stimulation. The LPS-induced shift from oxidative phosphorylation towards glycolysis seems essential for the inflammatory response, since the downstream release of NO, IL-1α, IL-1β, IL-6, IL-10, and TNF-α was also decreased in heterozygous Cacna1c as well as nimodipine-treated microglial cells. These results indicate a major functional role for CaV1.2-dependent signaling in the pro-inflammatory activation of microglia, the innate immune cells of the central nervous system. By simulating the interaction of psychiatric disease-relevant genetic and environmental factors in vivo, the present study additionally evaluated their potential effect on brain mitochondrial function using a constitutive heterozygous Cacna1c rat model in combination with a four-week exposure to either post-weaning social isolation, standard housing, or social and physical environmental enrichment during the juvenile developmental period. In this specific gene-environment setting, isolated mitochondria from prefrontal cortex and hippocampus, both representing particularly susceptible brain regions in neuropsychiatric disorders, did not reveal considerable differences in mitochondrial bioenergetics, respiratory chain complex protein levels, superoxide formation, and membrane potential between the investigated conditions. Finally, mitochondrial function was investigated in human PBMCs from probands recruited in the Marburg/Münster Affective Disorders Cohort Study (MACS). However, neither a family history of psychiatric disorders nor an experience of maltreatment during childhood had a significant effect on mitochondrial superoxide levels and respiratory parameters in PBMCs from healthy female subjects. Consequently, further research is required in order to shed more light on the early pathological mechanisms underlying neuropsychiatric disorders. Overall, the present findings suggest that the GWAS-confirmed psychiatric risk gene CACNA1C plays a significant role in oxidative stress as well as neuroinflammatory pathways with particular impact on mitochondrial integrity and function, thereby adding to a better understanding of the intracellular processes likely involved in the pathophysiology of CACNA1C-associated disorders. Thus, modulating L-type calcium signaling may offer an effective therapeutic strategy in psychiatric disorders, where neuronal atrophy and inflammation contribute to disease pathophysiology

Global neural rhythm control by local neuromodulation

Neural oscillations are a ubiquitous form of neural activity seen across scales and modalities. These neural rhythms correlate with diverse cognitive functions and brain states. One mechanism for changing the oscillatory dynamics of large neuronal populations is through neuromodulator activity. An intriguing phenomenon explored here is when local neuromodulation of a distinct neuron type within a single brain nucleus exerts a powerful influence on global cortical rhythms. One approach to investigate the impact of local circuits on global rhythms is through optogenetic techniques. My first project involves the statistical analysis of electrophysiological recordings of an optogenetically-mediated Parkinsonian phenotype. Empirical studies demonstrate that Parkinsonian motor deficits correlate with the emergence of exaggerated beta frequency (15-30 Hz) oscillations throughout the cortico-basal ganglia-thalamic network. However, the mechanism of these aberrant oscillatory dynamics is not well understood. A previous modeling study predicted that cholinergic neuromodulation of medium spiny neurons in the striatum of the basal ganglia may mediate the pathologic beta rhythm. Here, this hypothesis was tested using selective optogenetic stimulation of striatal cholinergic interneurons in normal mice; stimulation robustly and reversibly amplified beta oscillations and Parkinsonian motor symptoms. The modulation of global rhythms by local networks was further studied using computational modeling in the context of intrathalamic neuromodulation. While intrathalamic vasoactive intestinal peptide (VIP) is known to cause long-lasting excitation in vitro, its in vivo dynamical effects have not been reported. Here, biophysical computational models were used to elucidate the impact of VIP on thalamocortical dynamics during sleep and propofol general anesthesia. The modeling results suggest that VIP can form robust sleep spindle oscillations and control aspects of sleep architecture through a novel homeostatic mechanism. This homeostatic mechanism would be inhibited by general anesthesia, representing a new mechanism contributing to anesthetic-induced loss of consciousness. While the previous two projects differed in their use of empirical versus theoretical methods, a challenge common to both domains is the difficulty in visualizing and analyzing large multi-dimensional datasets. A tool to mitigate these issues is introduced here: GIMBL-Vis is a Graphical Interactive Multi-dimensional extensiBLe Visualization toolbox for Matlab. This toolbox simplifies the process of exploring multi-dimensional data in Matlab by providing a graphical interface for visualization and analysis. Furthermore, it provides an extensible open platform for distributed development by the community