Trajectories through similarity space produced by local neocortical circuits (original) (raw)
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Connectivity and dynamics in local cortical networks
Handbook of Brain Connectivity, 2007
Recent experimental work has begun to characterize activity in local cortical networks containing thousands of neurons. There has also been an explosion of work on connectivity in networks of all types. It would seem natural then to explore the influence of connectivity on dynamics at the local network level. In this chapter, we will give an overview of this emerging area. After a brief introduction, we will first review early neural network models and show how they suggested attractor dynamics based on recurrent connectivity. Second, we will review physiological reports of repeating activity patterns that have been influenced by this initial concept of attractors. Third, we will introduce tools from dynamical systems theory that will allow us to precisely quantify neural network dynamics. Fourth, we will apply these tools to simple network models where connectivity can be tuned. We will conclude with a summary and a discussion of future prospects.
Cerebral Cortex Communications
A major goal of neuroscience is to reveal mechanisms supporting collaborative actions of neurons in local and larger-scale networks. However, no clear overall principle of operation has emerged despite decades-long experimental efforts. Here, we used an unbiased method to extract and identify the dynamics of local postsynaptic network states contained in the cortical field potential. Field potentials were recorded by depth electrodes targeting a wide selection of cortical regions during spontaneous activities, and sensory, motor, and cognitive experimental tasks. Despite different architectures and different activities, all local cortical networks generated the same type of dynamic confined to one region only of state space. Surprisingly, within this region, state trajectories expanded and contracted continuously during all brain activities and generated a single expansion followed by a contraction in a single trial. This behavior deviates from known attractors and attractor network...
Toward a quantitative description of large-scale neocortical dynamic function and EEG
Behavioral and Brain Sciences, 2000
A general conceptual framework for large-scale neocortical dynamics based on data from many laboratories is applied to a variety of experimental designs, spatial scales, and brain states. Partly distinct, but interacting local processes (e.g., neural networks) arise from functional segregation. Global processes arise from functional integration and can facilitate (top down) synchronous activity in remote cell groups that function simultaneously at several different spatial scales. Simultaneous local processes may help drive (bottom up) macroscopic global dynamics observed with electroencephalography (EEG) or magnetoencephalography (MEG).A local/global dynamic theory that is consistent with EEG data and the proposed conceptual framework is outlined. This theory is neutral about properties of neural networks embedded in macroscopic fields, but its global component makes several qualitative and semiquantitative predictions about EEG measures of traveling and standing wave phenomena. A ...
Emergence of Spatio-Temporal Patterns in Neuronal Activity
Zeitschrift für Naturforschung C, 1998
Neuronal Activity, Emergence of Spatio-Temporal Patterns This paper explores if dynamic modulation of coherent firing serves cortical functions. We recorded neuronal activity in the frontal cortex of behaving monkeys and found that tempo ral coincidences of spikes firing of different neurons can emerge within a fraction of a second in relation to the animal behavior. The temporal patterns of the correlation could not be predicted from the modulations of the neurons firing rate and finally, the patterns of correla tion depend on the distance between neurons. These findings call for a revision of prevailing models of neural coding that solely rely on firing rates. The findings suggest that modification of neuronal interactions can serve as a mechanism by which neurons associate rapidly into a functional group in order to perform a specific computational task. Increased correlation between members of the groups, and decreased or negative correlation with others, enhance the ability t...
Synchronization, chaos and spike patterns in neocortical computation
IU-Journal of Electrical & Electronics Engineering, 2012
From simulations relying on detailed physiological data, cortical layer IV recurrent network topology and small thalamic input currents, a novel synchronization paradigm emerges. Coarse synchronization, where small temporal variations due to ...
Reliable Recall of Spontaneous Activity Patterns in Cortical Networks
Journal of Neuroscience, 2009
Irregular ongoing activity in cortical networks is often modelled as a result of recurrent connectivity. Yet it remains unclear how its presence corrupts the signal transmission evoked by the sensory drive. Here we show that reproducible responses in a generic recurrent cortical-like network can be obtained if the imposed external drive is consistent with patterns previously seen in the spontaneous, self-sustained activity. A subset of neurons in the network, constrained to replay a spiking pattern previously recorded during spontaneous activity, reliably drives the remaining, free-running neurons to reproduce the rest of the pattern. Comparison with surrogate Poisson patterns indicates that such transmission of input patterns is optimal for inputs with the statistical properties of the spontaneous activity. We propose that the similarity between spontaneous and evoked activity in sensory cortical areas could be the signature of learned efficiency of transmission across cortical networks.
Spontaneous and driven cortical activity: implications for computation
Current Opinion in Neurobiology, 2009
The traditional view of spontaneous neural activity as 'noise' has been challenged by recent findings suggesting that: (a) spontaneous activity in cortical populations is highly structured in both space and time, (b) the spatio-temporal structure of spontaneous activity is linked to the underlying connectivity of the cortical network, (c) spontaneous cortical activity interacts with external stimulation to generate responses to the individual presentations of a stimulus, (d) network connectivity is shaped in part by the statistics of natural signals and (e) ongoing cortical activity represents a continuous top-down prediction/expectation signal that interacts with incoming input to generate an updated representation of the world. These results can be integrated to provide a new framework for the study of cortical computation.
Nonlinear time series analyses have suggested that the human electroencephalogram (EEG) may share statistical and dynamical properties with chaotic systems. During slow-wave sleep or pathological states like epilepsy, correlation dimension measurements display low values, while in awake and attentive subjects, there is not such low dimensionality, and the EEG is more similar to a stochastic variable. We briefly review these results and contrast them with recordings in cat cerebral cortex, as well as with theoretical models. In awake or sleeping cats, recordings with microelectrodes inserted in cortex show that global variables such as local field potentials (local EEG) are similar to the human EEG. However, in both cases, neuronal discharges are highly irregular and exponentially distributed, similar to Poisson stochastic processes. To attempt reconcile these results, we investigate models of randomly-connected networks of integrate-and-fire neurons, and also contrast global (averaged) variables, with neuronal activity. The network displays different states, such as "synchronous regular" (SR) or "asynchronous irregular" (AI) states. In SR states, the global variables display coherent behavior with low dimensionality, while in AI states, the global activity is high-dimensionally chaotic with exponentially distributed neuronal discharges, similar to awake cats. Scale-dependent Lyapunov exponents and ε-entropies show that the seemingly stochastic nature at small scales (neurons) can coexist with more coherent behavior at larger scales (averages). Thus, we suggest that brain activity obeys similar scheme, with seemingly stochastic dynamics at small scales (neurons), while large scales (EEG) display more coherent behavior or highdimensional chaos.
Oscillations and Synchrony in Large-scale Cortical Network Models
Journal of Biological Physics, 2008
Intrinsic neuronal and circuit properties control the responses of large ensembles of neurons by creating spatiotemporal patterns of activity that are used for sensory processing, memory formation, and other cognitive tasks. The modeling of such systems requires computationally efficient single-neuron models capable of displaying realistic response properties. We developed a set of reduced models based on difference equations (map-based models) to simulate the intrinsic dynamics of biological neurons. These phenomenological models were designed to capture the main response properties of specific types of neurons while ensuring realistic model behavior across a sufficient dynamic range of inputs. This approach allows for fast simulations and efficient parameter space analysis of networks containing hundreds of thousands of neurons of different types using a conventional workstation. Drawing on results obtained using large-scale networks of map-based neurons, we discuss spatiotemporal cortical network dynamics as a function of parameters that affect synaptic interactions and intrinsic states of the neurons.