Title: Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making
The notion that a neuron transmits the same set of neurotransmitters at all of its post-synaptic connections, typically known as Dale's law, is well supported throughout the majority of the brain and is assumed in almost all theoretical studies investigating the mechanisms for computation in neuronal networks. Dale's law has numerous functional implications in fundamental sensory processing and decision-making tasks, and it plays a key role in the current understanding of the structure-function relationship in the brain. However, since exceptions to Dale's law have been discovered for certain neurons and because other biological systems with complex network structure incorporate individual units that send both positive and negative feedback signals, we investigate the functional implications of network model dynamics that violate Dale's law by allowing each neuron to send out both excitatory and inhibitory signals to its neighbors. We show how balanced network dynamics, in which large excitatory and inhibitory inputs are dynamically adjusted such that input fluctuations produce irregular firing events, are theoretically preserved for a single population of neurons violating Dale's law. We further leverage this single-population network model in the context of two competing pools of neurons to demonstrate that effective decision-making dynamics are also produced, agreeing with experimental observations from honeybee dynamics in selecting a food source and artificial neural networks trained in optimal selection. Through direct comparison with the classical two-population balanced neuronal network, we argue that the one-population network demonstrates more robust balanced activity for systems with less computational units, such as honeybee colonies, whereas the two-population network exhibits a more rapid response to temporal variations in network inputs, as required by the brain. We expect this study will shed light on the role of neurons violating Dale's law found in experiment as well as shared design principles across biological systems that perform complex computations. more »« less
Understanding the neural code has been one of the central aims of neuroscience research for decades. Spikes are commonly referred to as the units of information transfer, but multi-unit activity (MUA) recordings are routinely analyzed in aggregate forms such as binned spike counts, peri-stimulus time histograms, firing rates, or population codes. Various forms of averaging also occur in the brain, from the spatial averaging of spikes within dendritic trees to their temporal averaging through synaptic dynamics. However, how these forms of averaging are related to each other or to the spatial and temporal units of information representation within the neural code has remained poorly understood.
Materials and methods
In this work we developed NeuroPixelHD, a symbolic hyperdimensional model of MUA, and used it to decode the spatial location and identity of static images shown ton= 9 mice in the Allen Institute Visual Coding—NeuroPixels dataset from large-scale MUA recordings. We parametrically varied the spatial and temporal resolutions of the MUA data provided to the model, and compared its resulting decoding accuracy.
Results
For almost all subjects, we found 125ms temporal resolution to maximize decoding accuracy for both the spatial location of Gabor patches (81 classes for patches presented over a 9×9 grid) as well as the identity of natural images (118 classes corresponding to 118 images) across the whole brain. This optimal temporal resolution nevertheless varied greatly between different regions, followed a sensory-associate hierarchy, and was significantly modulated by the central frequency of theta-band oscillations across different regions. Spatially, the optimal resolution was at either of two mesoscale levels for almost all mice: the area level, where the spiking activity of all neurons within each brain area are combined, and the population level, where neuronal spikes within each area are combined across fast spiking (putatively inhibitory) and regular spiking (putatively excitatory) neurons, respectively. We also observed an expected interplay between optimal spatial and temporal resolutions, whereby increasing the amount of averaging across one dimension (space or time) decreases the amount of averaging that is optimal across the other dimension, and vice versa.
Discussion
Our findings corroborate existing empirical practices of spatiotemporal binning and averaging in MUA data analysis, and provide a rigorous computational framework for optimizing the level of such aggregations. Our findings can also synthesize these empirical practices with existing knowledge of the various sources of biological averaging in the brain into a new theory of neural information processing in which theunit of informationvaries dynamically based on neuronal signal and noise correlations across space and time.
INTRODUCTION The analysis of the human brain is a central goal of neuroscience, but for methodological reasons, research has focused on model organisms, the mouse in particular. Because substantial homology was found at the level of ion channels, transcriptional programs, and basic neuronal types, a strong similarity of neuronal circuits across species has also been assumed. However, a rigorous test of the configuration of local neuronal circuitry in mouse versus human—in particular, in the gray matter of the cerebral cortex—is missing. The about 1000-fold increase in number of neurons is the most obvious evolutionary change of neuronal network properties from mouse to human. Whether the structure of the local cortical circuitry has changed as well is, however, unclear. Recent data from transcriptomic analyses has indicated an increase in the proportion of inhibitory interneurons from mouse to human. But what the effect of such a change is on the circuit configurations found in the human cerebral cortex is not known. This is, however, of particular interest also to the study of neuropsychiatric disorders because in these, the alteration of inhibitory-to-excitatory synaptic balance has been identified as one possible mechanistic underpinning. RATIONALE We used recent methodological improvements in connectomics to acquire data from one macaque and two human individuals, using biopsies of the temporal, parietal, and frontal cortex. Human tissue was obtained from neurosurgical interventions related to tumor removal, in which access path tissue was harvested that was not primarily affected by the underlying disease. A key concern in the analysis of human patient tissue has been the relation to epilepsy surgery, when the underlying disease has required often year-long treatment with pharmaceuticals, plausibly altering synaptic connectivity. Therefore, the analysis of nonepileptic surgery tissue seemed of particular importance. We also included data from one macaque individual, who was not known to have any brain-related pathology. RESULTS We acquired three-dimensional electron microscopy data from temporal and frontal cortex of human and temporal and parietal cortex of macaque. From these, we obtained connectomic reconstructions and compared these with five connectomes from mouse cortex. On the basis of these data, we were able to determine the effect of the about 2.5-fold expansion of the interneuron pool in macaque and human cortex compared with that of mouse. Contrary to expectation, the inhibitory-to-excitatory synaptic balance on pyramidal neurons in macaque and human cortex was not substantially altered. Rather, the interneuron pool was selectively expanded for bipolar-type interneurons, which prefer the innervation of other interneurons, and which further increased their preference for interneuron innervation from mouse to human. These changes were each multifold, yielding in effect an about 10-fold expanded interneuron-to-interneuron network in the human cortex that is only sparsely present in mouse. The total amount of synaptic input to pyramidal neurons, however, did not change according to the threefold thickening of the cortex; rather, a modest increase from about 12,000 synaptic inputs in mouse to about 15,000 in human was found. CONCLUSION The principal cells of the cerebral cortex, pyramidal neurons, maintain almost constant inhibitory-to-excitatory input balance and total synaptic input across 100 million years of evolutionary divergence, which is particularly noteworthy with the concomitant 1000-fold expansion of the neuronal network size and the 2.5-fold increase of inhibitory interneurons from mouse to human. Rather, the key network change from mouse to human is an expansion of almost an order of magnitude of an interneuron-to-interneuron network that is virtually absent in mouse but constitutes a substantial part of the human cortical network. Whether this new network is primarily created through the expansion of existing neuronal types, or is related to the creation of new interneuron subtypes, requires further study. The discovery of this network component in human cortex encourages detailed analysis of its function in health and disease. Connectomic screening across mammalian species: Comparison of five mouse, two macaque, and two human connectomic datasets from the cerebral cortex. ( A ) Automated reconstructions of all neurons with their cell bodies in the volume shown, using random colors. The analyzed connectomes comprised a total of ~1.6 million synapses. Arrows indicate evolutionary divergence: the last common ancestor between human and mouse, approximately 100 million years ago, and the last common ancestor between human and macaque, about 20 million years ago. ( B ) Illustration of the about 10-fold expansion of the interneuron-to-interneuron network from mouse to human.
Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.
Zheng, Wenrui; Li, Peng(
, 2021 International Joint Conference on Neural Networks)
null
(Ed.)
In biological brains, recurrent connections play a crucial role in cortical computation, modulation of network dynamics, and communication. However, in recurrent spiking neural networks (SNNs), recurrence is mostly constructed by random connections. How excitatory and inhibitory recurrent connections affect network responses and what kinds of connectivity benefit learning performance is still obscure. In this work, we propose a novel recurrent structure called the Laterally-Inhibited Self-Recurrent Unit (LISR), which consists of one excitatory neuron with a self-recurrent connection wired together with an inhibitory neuron through excitatory and inhibitory synapses. The self-recurrent connection of the excitatory neuron mitigates the information loss caused by the firing-and-resetting mechanism and maintains the long-term neuronal memory. The lateral inhibition from the inhibitory neuron to the corresponding excitatory neuron, on the one hand, adjusts the firing activity of the latter. On the other hand, it plays as a forget gate to clear the memory of the excitatory neuron. Based on speech and image datasets commonly used in neuromorphic computing, RSNNs based on the proposed LISR improve performance significantly by up to 9.26% over feedforward SNNs trained by a state-of-the-art backpropagation method with similar computational costs.
Paredes, Mercedes F.; Mora, Cristina; Flores-Ramirez, Quetzal; Cebrian-Silla, Arantxa; Del Dosso, Ashley; Larimer, Phil; Chen, Jiapei; Kang, Gugene; Gonzalez Granero, Susana; Garcia, Eric; et al(
, Science)
INTRODUCTION Balance between excitatory and inhibitory neuron (interneuron) populations in the cortex promotes normal brain function. Interneurons are primarily generated in the medial, caudal, and lateral ganglionic eminences (MGE, CGE, and LGE) of the ventral embryonic forebrain; these subregions give rise to distinct interneuron subpopulations. In rodents, the MGE generates cortical interneurons, the parvalbumin + (PV + ) and somatostatin + (SST + ) subtypes that connect with excitatory neurons to regulate their activity. Defects in interneuron production have been implicated in neurodevelopmental and psychiatric disorders including autism, epilepsy, and schizophrenia. RATIONALE How does the human MGE (hMGE) produce the number of interneurons required to populate the forebrain? The hMGE contains progenitor clusters distinct from what has been observed in the rodent MGE and other germinal zones of the human brain. This cytoarchitecture could be the key to understanding interneuron neurogenesis. We investigated the cellular and molecular properties of different compartments within the developing hMGE, from 14 gestational weeks (GW) to 39 GW (term), to study their contribution to the production of inhibitory interneurons. We developed a xenotransplantation assay to follow the migration and maturation of the human interneurons derived from this germinal region. RESULTS Within the hMGE, densely packed aggregates (nests) of doublecortin + (DCX + ) and LHX6 + cells were surrounded by nestin + progenitor cells and their processes. These DCX + cell–enriched nests (DENs) were observed in the hMGE but not in the adjacent LGE. We found that cells within DENs expressed molecular markers associated with young neurons, such as DCX, and polysialylated neural cell adhesion molecule (PSA-NCAM). A subpopulation also expressed Ki-67, a marker of proliferation; therefore, we refer to these cells as neuroblasts. A fraction of DCX + cells inside DENs expressed SOX2 and E2F1, transcription factors associated with progenitor and proliferative properties. More than 20% of DCX + cells in the hMGE were dividing, specifically within DENs. Proliferating neuroblasts in DENs persisted in the hMGE throughout prenatal human brain development. The division of DCX + cells was confirmed by transmission electron microscopy and time-lapse microscopy. Electron microscopy revealed adhesion contacts between cells within DENs, providing multiple sites to anchor DEN cells together. Neuroblasts within DENs express PCDH19, and nestin + progenitors surrounding DENs express PCDH10; these findings suggest a role for differential cell adhesion in DEN formation and maintenance. When transplanted into the neonatal mouse brain, dissociated hMGE cells reformed DENs containing proliferative DCX + cells, similar to DENs observed in the prenatal human brain. This suggests that DENs are generated by cell-autonomous mechanisms. In addition to forming DENs, transplanted hMGE-derived neuroblasts generated young neurons that migrated extensively into cortical and subcortical regions in the host mouse brain. One year after transplantation, these neuroblasts had differentiated into distinct γ-aminobutyric acid–expressing (GABAergic) interneuron subtypes, including SST + and PV + cells, that showed morphological and functional maturation. CONCLUSION The hMGE harbors DENs, where cells expressing early neuronal markers continue to divide and produce GABAergic interneurons. This MGE-specific arrangement of neuroblasts in the human brain is present until birth, supporting expanded neurogenesis for inhibitory neurons. Given the robust neurogenic output from this region, knowledge of the mechanisms underlying cortical interneuron production in the hMGE will provide insights into the cell types and developmental periods that are most vulnerable to genetic or environmental insults. Nests of DCX + cells in the ventral prenatal brain. Schematic of a coronal view of the embryonic human forebrain showing the medial ganglionic eminence (MGE, green), with nests of DCX + cells (DENs, green). Nestin + progenitor cells (blue) are present within the VZ and iSVZ and are intercalated in the oSVZ (where DENs reside). The initial segment of the oSVZ contains palisades of nestin + progenitors referred to as type I clusters (light blue cells) around DENs. In the outer part of the oSVZ, DENs transition to chains of migrating DCX + cells; surrounding nestin + progenitors are arranged into groups of cells referred to as type II clusters (white cells). In addition to proliferation of nestin + progenitors, cell division is present among DCX + cells within DENs, suggesting multiple progenitor states for the generation of MGE-derived interneurons in the human forebrain. ILLUSTRATION: NOEL SIRIVANSANTI
Barranca, Victor J., Bhuiyan, Asha, Sundgren, Max, and Xing, Fangzhou. Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making. Retrieved from https://par.nsf.gov/biblio/10321311. Frontiers in Neuroscience 16. Web. doi:10.3389/fnins.2022.801847.
Barranca, Victor J., Bhuiyan, Asha, Sundgren, Max, & Xing, Fangzhou. Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making. Frontiers in Neuroscience, 16 (). Retrieved from https://par.nsf.gov/biblio/10321311. https://doi.org/10.3389/fnins.2022.801847
Barranca, Victor J., Bhuiyan, Asha, Sundgren, Max, and Xing, Fangzhou.
"Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making". Frontiers in Neuroscience 16 (). Country unknown/Code not available. https://doi.org/10.3389/fnins.2022.801847.https://par.nsf.gov/biblio/10321311.
@article{osti_10321311,
place = {Country unknown/Code not available},
title = {Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making},
url = {https://par.nsf.gov/biblio/10321311},
DOI = {10.3389/fnins.2022.801847},
abstractNote = {The notion that a neuron transmits the same set of neurotransmitters at all of its post-synaptic connections, typically known as Dale's law, is well supported throughout the majority of the brain and is assumed in almost all theoretical studies investigating the mechanisms for computation in neuronal networks. Dale's law has numerous functional implications in fundamental sensory processing and decision-making tasks, and it plays a key role in the current understanding of the structure-function relationship in the brain. However, since exceptions to Dale's law have been discovered for certain neurons and because other biological systems with complex network structure incorporate individual units that send both positive and negative feedback signals, we investigate the functional implications of network model dynamics that violate Dale's law by allowing each neuron to send out both excitatory and inhibitory signals to its neighbors. We show how balanced network dynamics, in which large excitatory and inhibitory inputs are dynamically adjusted such that input fluctuations produce irregular firing events, are theoretically preserved for a single population of neurons violating Dale's law. We further leverage this single-population network model in the context of two competing pools of neurons to demonstrate that effective decision-making dynamics are also produced, agreeing with experimental observations from honeybee dynamics in selecting a food source and artificial neural networks trained in optimal selection. Through direct comparison with the classical two-population balanced neuronal network, we argue that the one-population network demonstrates more robust balanced activity for systems with less computational units, such as honeybee colonies, whereas the two-population network exhibits a more rapid response to temporal variations in network inputs, as required by the brain. We expect this study will shed light on the role of neurons violating Dale's law found in experiment as well as shared design principles across biological systems that perform complex computations.},
journal = {Frontiers in Neuroscience},
volume = {16},
author = {Barranca, Victor J. and Bhuiyan, Asha and Sundgren, Max and Xing, Fangzhou},
}
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