Nonmonotonic spatial structure of interneuronal correlations in prefrontal microcircuits

Safavi, S; Dwarakanath, A; Kapoor, Vishal; Werner, Joachim; Hatsopoulos, Nicholas G.; Logothetis, Nikos K.; Panagiotaropoulos, T

doi:10.1073/pnas.1802356115

Cited by 22 publications

(29 citation statements)

References 78 publications

(132 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In canonical cortical microcircuits, horizontal connections allow correlated neural activity. In a region of PFC where visual neurons tuned to object identity (Wilson et al, 1993;Asaad et al, 1998) coexist with genuine hand movement-related cells (Bruni et al, 2015;Simone et al, 2015), the correlated fluctuation of single-neuron activities occurs over long-range distances (Safavi et al, 2018) ( Fig. 2A) compared with the limited-range correlational structure typical of sensory areas, such as V1 (Smith and Sommer, 2013) (Fig.…”

Section: Relevance Of Corticocortical Connections For the Functional mentioning

confidence: 99%

See 1 more Smart Citation

Corticocortical Systems Underlying High-Order Motor Control

Battaglia-Mayer

Caminiti

2019

J. Neurosci.

View full text Add to dashboard Cite

Cortical networks are characterized by the origin, destination, and reciprocity of their connections, as well as by the diameter, conduction velocity, and synaptic efficacy of their axons. The network formed by parietal and frontal areas lies at the core of cognitive-motor control because the outflow of parietofrontal signaling is conveyed to the subcortical centers and spinal cord through different parallel pathways, whose orchestration determines, not only when and how movements will be generated, but also the nature of forthcoming actions. Despite intensive studies over the last 50 years, the role of corticocortical connections in motor control and the principles whereby selected cortical networks are recruited by different task demands remain elusive. Furthermore, the synaptic integration of different cortical signals, their modulation by transthalamic loops, and the effects of conduction delays remain challenging questions that must be tackled to understand the dynamical aspects of parietofrontal operations. In this article, we evaluate results from nonhuman primate and selected rodent experiments to offer a viewpoint on how corticocortical systems contribute to learning and producing skilled actions. Addressing this subject is not only of scientific interest but also essential for interpreting the devastating consequences for motor control of lesions at different nodes of this integrated circuit. In humans, the study of corticocortical motor networks is currently based on MRI-related methods, such as resting-state connectivity and diffusion tract-tracing, which both need to be contrasted with histological studies in nonhuman primates.

show abstract

Section: Relevance Of Corticocortical Connections For the Functional mentioning

confidence: 99%

“…Notice how, at variance from V1 and V4, distal interactions are as strong as proximal interactions during visual stimulation of functionally similar prefrontal neurons. Modified with permission fromSmith and Kohn (2008),Smith and Sommer (2013), andSafavi et al (2018).…”

mentioning

confidence: 99%

Corticocortical Systems Underlying High-Order Motor Control

Battaglia-Mayer

Caminiti

2019

J. Neurosci.

View full text Add to dashboard Cite

show abstract

“…In the cortex, they drop significantly over distances of 200-400 μm [10,38]. Recent experimental studies have reported correlations varying nonmonotonically with distance [39] or correlations which are positive for closeby neurons but negative for neurons farther apart [40]. Correlation coefficients also depend on the functional properties of the neurons.…”

Section: Introductionmentioning

confidence: 99%

Strength of Correlations in Strongly Recurrent Neuronal Networks

2018

View full text Add to dashboard Cite

Spatiotemporal correlations in brain activity are functionally important and have been implicated in perception, learning and plasticity, exploratory behavior, and various aspects of cognition. Neurons in the cerebral cortex are strongly interacting. Their activity is temporally irregular and can exhibit substantial correlations. However, how the collective dynamics of highly recurrent and strongly interacting neurons can evolve into a state in which the activity of individual cells is highly irregular yet macroscopically correlated is an open question. Here, we develop a general theory that relates the strength of pairwise correlations to the anatomical features of networks of strongly coupled neurons. To this end, we investigate networks of binary units. When interactions are strong, the activity is irregular in a large region of parameter space. We find that despite the strong interactions, the correlations are generally very weak. Nevertheless, we identify architectural features, which if present, give rise to strong correlations without destroying the irregularity of the activity. For networks with such features, we determine how correlations scale with the network size and the number of connections. Our work shows the mechanism by which strong correlations can be consistent with highly irregular activity, two hallmarks of neuronal dynamics in the central nervous system.

show abstract

“…(36) and Eq. (38), it is clear that the transformation fromĈ (1) to C (1) mixes elements which are 0 and O( √ K/N ), with those which are O(K/N ). Thus, while inĈ (1) only the elementĈ (1) 12 is O(K/N ), all the elements of the correlation matrix C (1) are O(K/N ).…”

Section: Vi1 Two Population Networkmentioning

confidence: 99%

“…In cortex, they drop significantly over distances of 200 − 400 µm [35,37]. Recent works have reported correlations varying non-monotonically with distance [38] or correlations which are positive for close-by neurons but negative for neurons farther apart [39]. Correlation coefficients also depend on functional properties of the neurons.…”

Section: Introductionmentioning

confidence: 99%

How strong are correlations in strongly recurrent neuronal networks?

Darshan

Vreeswijk

Hansel

2018

Preprint

View full text Add to dashboard Cite

Cross-correlations in the activity in neural networks are commonly used to characterize their dynamical states and their anatomical and functional organizations. Yet, how these latter network features affect the spatiotemporal structure of the correlations in recurrent networks is not fully understood. Here, we develop a general theory for the emergence of correlated neuronal activity from the dynamics in strongly recurrent networks consisting of several populations of binary neurons. We apply this theory to the case in which the connectivity depends on the anatomical or functional distance between the neurons. We establish the architectural conditions under which the system settles into a dynamical state where correlations are strong, highly robust and spatially modulated. We show that such strong correlations arise if the network exhibits an effective feedforward structure. We establish how this feedforward structure determines the way correlations scale with the network size and the degree of the connectivity. In networks lacking an effective feedforward structure correlations are extremely small and only weakly depend on the number of connections per neuron. Our work shows how strong correlations can be consistent with highly irregular activity in recurrent networks, two key features of neuronal dynamics in the central nervous system.

show abstract

Nonmonotonic spatial structure of interneuronal correlations in prefrontal microcircuits

Cited by 22 publications

References 78 publications

Corticocortical Systems Underlying High-Order Motor Control

Corticocortical Systems Underlying High-Order Motor Control

Strength of Correlations in Strongly Recurrent Neuronal Networks

How strong are correlations in strongly recurrent neuronal networks?

Contact Info

Product

Resources

About