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Cross-Correlation Analysis of Interneuronal Connectivity in cat visual cortex
Abstract
Neuronal interactions were studied in the cat's cortex (area 17) by analyzing the cross-correlation of impulse discharges in two simultaneously recorded cells. In order to obtain a reliable correlogram in a short period of time, impulse discharges in cortical cells were enhanced by electrophoretic ejection of glutamate ions or by stimulating their receptive fields with a stationary or moving light bar. The cross-correlation study of 208 neuronal pairs sampled from 17 cats revealed three types of interneuronal interactions. The first type of interaction was a conjoint excitation of two cortical cells. A positive correlation occurred with practically no delay: the maximum positivity was located ± 0.3 ms around zero time and the positivity extended only for ±0.6 ms. The second type of interaction was a delayed excitation of one cell following the excitation of the other. The third type of interaction was a delayed inhibition of one cell following the excitation of the other. A negative correlation started with a monosynaptic delay and declined relatively slowly (total duration more than 80 ms). Conjoint excitation occurred in about half of the neuronal pairs studied (105/208) and delayed excitation and inhibition in about 1/10 of them (24 and 26/208). Conjoint excitation of cortical cells may be ascribed to common excitatory inputs from the lateral geniculate cells (common excitation). Delayed excitation and inhibition may represent, respectively, excitatory and inhibitory interactions through intracortical connections (intracortical excitation and intracortical inhibition).
... Besides inferring synaptic connectivity from intracellular recordings, there has been a surge in studies that used the statistical relationship of the activity among neurons as an indirect measure of neuronal coupling [30]. Spike train cross-correlograms (CCGs), for example, have been applied to estimate spike transmission or effective connectivity between defined neurons and/or specific brain regions [31][32][33][34][35][36]. To improve the performance of CCG-based circuit inference, several modifications have been suggested-such as, to take into account co-modulating background dynamics [37][38][39], to apply model-based timescale separation techniques [40,41] or, more recently, to apply deep learning methods [42]. ...
... Historically, many studies have applied cross-correlograms (CCGs) to estimate putative mono-synaptic connections between neurons [31][32][33][35][36][37]. In the present study, we considered three CCG-based connectivity inference methods: First, the coincidence index (CI), which integrates the CCG over a small synaptic window [56] and compares it to values obtained from surrogate data (i.e., jittered spike trains for which the short-latency synaptic relationships have been destroyed). ...
Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.
... Cross-correlations between pairs of neurons have been observed and described on multiple timescales, and previous work has aimed to distinguish between common input, stimulus-driven, and emergent synchrony (Ostojic et al., 2009;. Sensory-driven and spontaneous activity in sensory cortex can also occur with nearsynchronous spiking (Bair et al., 2001;Kohn and Smith, 2005;Swadlow et al., 1998;Toyama et al., 1981), and similar fast correlations have been observed between hippocampal (Diba et al., 2014) and thalamic neurons (Alonso et al., 1996). In most cases, synchronous common input does not have a clear directionality and is centered at zero latency (see examples from ABI-715093703 in Fig 4D and S1), but short duration cross-correlations with non-zero latency can still have ambiguous origins (e.g. ...
Whether, when, and how causal interactions between neurons can be meaningfully studied from observations of neural activity alone are vital questions in neural data analysis. Here we aim to better outline the concept of functional connectivity for the specific situation where systems neuroscientists aim to study synapses using spike train recordings. In some cases, cross-correlations between the spikes of two neurons are such that, although we may not be able to say that a relationship is causal without experimental manipulations, models based on synaptic connections provide precise explanations of the data. Additionally, there is often strong circumstantial evidence that pairs of neurons are monosynaptically connected. Here we illustrate how circumstantial evidence for or against synapses can be systematically assessed and show how models of synaptic effects can provide testable predictions for pair-wise spike statistics. We use case studies from large-scale multi-electrode spike recordings to illustrate key points and to demonstrate how modeling synaptic effects using large-scale spike recordings opens a wide range of data analytic questions.
... Cross-correlations between pairs of neurons have been observed and described on multiple timescales, and previous work has aimed to distinguish between common input, stimulus-driven, and emergent synchrony Ostojic et al., 2009). Sensory-driven and spontaneous activity in sensory cortex can also occur with near-synchronous spiking (Toyama et al., 1981;Swadlow et al., 1998;Bair et al., 2001;Kohn and Smith, 2005), and similar fast correlations have been observed between hippocampal (Diba et al., 2014) and thalamic neurons (Alonso et al., 1996). In most cases, synchronous common input does not have a clear directionality and is centered at zero latency (see examples from ABI-715093703 in Fig 4D and S1), but short duration cross-correlations with non-zero latency can still have ambiguous origins (e.g. ...
Whether, when, and how causal interactions between neurons can be meaningfully studied from observations of neural activity alone are vital questions in neural data analysis. Here we aim to better outline the concept of functional connectivity for the specific situation where systems neuroscientists aim to study synapses using spike train recordings. In some cases, cross-correlations between the spikes of two neurons are such that, although we may not be able to say that a relationship is causal without experimental manipulations, models based on synaptic connections provide precise explanations of the data. Additionally, there is often strong circumstantial evidence that pairs of neurons are monosynaptically connected. Here we illustrate how circumstantial evidence for or against synapses can be systematically assessed and show how models of synaptic effects can provide testable predictions for pair-wise spike statistics. We use case studies from large-scale multi-electrode spike recordings to illustrate key points and to demonstrate how modeling synaptic effects using large-scale spike recordings opens a wide range of data analytic questions.
... To date, many studies have leveraged the covariation in spiking activity between simultaneously recorded neurons to elucidate underlying neural mechanisms in the primate brain with some success, particularly within the visual system (Briggs et al., 2013;Chu et al., 2014;Hansen et al., 2012;Hembrook-Short et al., 2019;Jia et al., 2013;Kohn and Smith, 2005;Koren et al., 2020;Krüger and Aiple, 1988;Maldonado et al., 2000;Smith and Kohn, 2008;Zandvakili and Kohn, 2015). In particular, temporally precise correlations in spiking activity have provided a unique means of assessing interactions among neurons in both local and distributed networks (Aertsen and Gerstein, 1985;Aertsen et al., 1989;Diba et al., 2014;Moore et al., 1970;Nelson et al., 1992;Nowak et al., 1999;Perkel et al., 1967;Siegle et al., 2021), and identification of such interactions has played an important part in understanding neural circuits in the mammalian visual system (Alonso and Martinez, 1998;Alonso et al., 1996;Alonso et al., 2001;Baker and Bair, 2012;Cohen and Kohn, 2011;Das and Gilbert, 1999;Denman and Contreras, 2014;Michalski et al., 1983;Nelson et al., 1992;Reid and Alonso, 1995;Schwarz and Bolz, 1991;Senzai et al., 2019;Siegle et al., 2021;Toyama et al., 1981a;Ts'o et al., 1986;Usrey et al., 1998;Usrey et al., 1999). However, the extent of circuit-level details addressable with crosscorrelation is greatly limited by the low incidences of simultaneous recordings from connected neurons when using conventional extracellular recording techniques (e.g. ...
Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.
... The proposed CCM algorithm for brain-connectivity analysis is compared here with 5 well-known techniques of functional connectivity analysis. They are Crosscorrelation technique [38], probabilistic relative correlation adjacency matrix (PRCAM) [17], Granger Causality [13], standard CCM [4] and Transfer entropy based analysis [39]. The relative performance of the proposed algorithm with the existing algorithms, has been evaluated on the basis of three performance metrics of classifier: classifier accuracy, sensitivity and specificity. ...
Brain-connectivity refers to a pattern of functional or effective connectivity of distinct modules of human brain due to interactions between them. In this paper, the authors have attempted to conduct a brain connectivity based analysis to study the brain circuitry in subjects from their electroencephalographic (EEG) data, while they are engaged in playing a horror video game. The main motive of our work is to understand the differences in the effective connectivity among phasmophobic and non-phasmophobic subjects. In the present analysis, we propose a modified version of the causality test, named as Convergent Cross Mapping (CCM) to perform the analysis. The proposed CCM improves the performance of the standard CCM with an added feature of finding the possible direction of causation in terms of conditional entropy or maximum information transfer among the brain signal-sources. Experimental results and statistical analysis show that the proposed method shows superior efficacy in estimating the directed brain-connectivity as compared to the very well-known classical Granger Causality, classical CCM and other off-the-shelf brain-connectivity algorithms.
Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic and empirical ground-truth data sets, obtained from simulations and parallel single-cell patch-clamp and high-density microelectrode array (HD-MEA) recordings in vitro. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike train data. We find gross differences between different algorithms, and many algorithms have difficulties in detecting inhibitory connections. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms, and show how it improves network reconstruction accuracy and robustness. Overall, the eANN was robust across different dynamical regimes, with shorter recording time, and ameliorated the identification of synaptic connections, in particular inhibitory ones. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.
Author summary
This study introduces an ensemble artificial neural network (eANN) to infer neuronal connectivity from multi-unit spike time recordings. We compare the eANN to previous algorithms and validate it using simulations and HD-MEA/patch-clamp datasets. The latter is obtained from three single-cell patch-clamp recordings and high-density microelectrode array (HD-MEA) measurements, in parallel. Our results demonstrate that the eANN outperforms all other algorithms across different dynamical regimes and provides a more accurate description of the underlying topological organization of the studied networks. We also provide a SHAP analysis of the trained eANN to understand which input features of the eANN contribute most to this superior performance. The eANN is a promising approach to improve connectivity inference from spike-train data.
Cerebellar climbing fibers convey diverse signals, but how they are organized in the compartmental structure of the cerebellar cortex during learning remains largely unclear. We analyzed a large amount of coordinate-localized two-photon imaging data from cerebellar Crus II in mice undergoing 'Go/No-go' reinforcement learning. Tensor component analysis revealed that a majority of climbing fiber inputs to Purkinje cells were reduced to only four functional components, corresponding to accurate timing control of motor initiation related to a Go cue, cognitive error-based learning, reward processing, and inhibition of erroneous behaviors after a No-go cue. Changes in neural activities during learning of the first two components were correlated with corresponding changes in timing control and error learning across animals, indirectly suggesting causal relationships. Spatial distribution of these components coincided well with boundaries of Aldolase-C/zebrin II expression in Purkinje cells, whereas several components are mixed in single neurons. Synchronization within individual components was bidirectionally regulated according to specific task contexts and learning stages. These findings suggest that, in close collaborations with other brain regions including the inferior olive nucleus, the cerebellum, based on anatomical compartments, reduces dimensions of the learning space by dynamically organizing multiple functional components, a feature that may inspire new-generation AI designs.
The prefrontal cortex plays important roles in a variety of higher cognitive functions, such as thinking, reasoning, planning, and decision-making (Goldman-Rakic 1987; Miller 2000; Miller and Cohen 2001; Fuster 2008; Funahashi 2015). Neural mechanisms for the prefrontal cortex to operate these functions have been extensively examined using human subjects and animals. Especially, studies using nonhuman primates have contributed significantly to understanding prefrontal functions and their neural mechanisms. Since Jacobsen (1936) first reported that monkeys having bilateral lesions in the prefrontal cortex exhibited severe and long-lasting impairments of delayed-response performances, the delayed-response task has been one of the important behavioral tasks to examine prefrontal functions using animal models. Since the monkey is required to retain the baited position during the delay period to correctly perform the delayed-response task, delayed-response deficits had been thought to be caused by an impairment of short-term storage of spatial information. Subsequently, not only the tasks requiring short-term storage of spatial information but also behavioral tasks requiring the storage of non-spatial information were shown to be impaired by bilateral lesions of the prefrontal cortex. Therefore, an important function of the prefrontal cortex was thought to be the short-term storage of various information. Although behavioral studies repeatedly exhibited delayed-response deficits in monkeys having prefrontal lesions, no agreement regarding the cause of delayed-response deficits had been reached among researchers. Some researchers concluded that delayed-response deficits were caused by an impairment of short-term memory. However, other researchers concluded that these deficits were caused by an increase in susceptibility to interference stimuli or difficulty to use kinesthetic information. In addition, the disagreement regarding behavioral deficits after prefrontal lesions was also present between animal studies and human clinical studies. Especially, human patients having damages in the prefrontal cortex did not exhibit delayed-response deficits and exhibit any impairment in memory functions.
Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional connections between neurons thereby providing an unprecedented view of local circuit interactions. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally connected neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the strength and synchrony of functional connections across the cortical column. Despite neurons residing within the same column, both measures of functional connectivity depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of connected pairs to categorize functional connections between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional connections within the full population. These classes of functional connections were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.
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