Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection

The external globus pallidus (GPe) is a key nucleus within basal ganglia circuits that are thought to be involved in action selection. A class of computational models assumes that, during action selection, the basal ganglia compute for all actions available in a given context the probabilities that they should be selected. These models suggest that a network of GPe and subthalamic nucleus (STN) neurons computes the normalization term in Bayes’ equation. In order to perform such computation, the GPe needs to send feedback to the STN equal to a particular function of the activity of STN neurons. However, the complex form of this function makes it unlikely that individual GPe neurons, or even a single GPe cell type, could compute it. Here, we demonstrate how this function could be computed within a network containing two types of GABAergic GPe projection neuron, so-called ‘prototypic’ and ‘arkypallidal’ neurons, that have different response properties in vivo and distinct connections. We compare our model predictions with the experimentally-reported connectivity and input-output functions (f-I curves) of the two populations of GPe neurons. We show that, together, these dichotomous cell types fulfil the requirements necessary to compute the function needed for optimal action selection. We conclude that, by virtue of their distinct response properties and connectivities, a network of arkypallidal and prototypic GPe neurons comprises a neural substrate capable of supporting the computation of the posterior probabilities of actions.     

Interplay between Short- and Long-Term Plasticity in Cell-Assembly Formation

Various hippocampal and neocortical synapses of mammalian brain show both short-term plasticity and long-term plasticity, which are considered to underlie learning and memory by the brain. According to Hebb’s postulate, synaptic plasticity encodes memory traces of past experiences into cell assemblies in cortical circuits. However, it remains unclear how the various forms of long-term and short-term synaptic plasticity cooperatively create and reorganize such cell assemblies. Here, we investigate the mechanism in which the three forms of synaptic plasticity known in cortical circuits, i.e., spike-timing-dependent plasticity (STDP), short-term depression (STD) and homeostatic plasticity, cooperatively generate, retain and reorganize cell assemblies in a recurrent neuronal network model. We show that multiple cell assemblies generated by external stimuli can survive noisy spontaneous network activity for an adequate range of the strength of STD. Furthermore, our model predicts that a symmetric temporal window of STDP, such as observed in dopaminergic modulations on hippocampal neurons, is crucial for the retention and integration of multiple cell assemblies. These results may have implications for the understanding of cortical memory processes.     

Model of cellular and network mechanisms for odor-evoked temporal patterning in the locust antennal lobe

Locust antennal lobe (AL) projection neurons (PNs) respond to olfactory stimuli with sequences of depolarizing and hyperpolarizing epochs, each lasting hundreds of milliseconds. A computer simulation of an AL network was used to test the hypothesis that slow inhibitory connections between local neurons (LNs) and PNs are responsible for temporal patterning. Activation of slow inhibitory receptors on PNs by the same GABAergic synapses that underlie fast oscillatory synchronization of PNs was sufficient to shape slow response modulations. This slow stimulus- and neuron-specific patterning of AL activity was resistant to blockade of fast inhibition. Fast and slow inhibitory mechanisms at synapses between LNs and PNs can thus form dynamical PN assemblies whose elements synchronize transiently and oscillate collectively, as observed not only in the locust AL, but also in the vertebrate olfactory bulb.     

Rapid target-specific remodeling of fast-spiking inhibitory circuits after loss of dopamine

In Parkinson's disease (PD), dopamine depletion alters neuronal activity in the direct and indirect pathways and leads to increased synchrony in the basal ganglia network. However, the origins of these?changes remain elusive. Because GABAergic interneurons regulate activity of projection neurons and?promote neuronal synchrony, we recorded from pairs of striatal fast-spiking (FS) interneurons and direct- or indirect-pathway MSNs after dopamine depletion with 6-OHDA. Synaptic properties of?FS-MSN connections remained similar, yet within 3?days of dopamine depletion, individual FS cells doubled their connectivity to indirect-pathway MSNs, whereas connections to direct-pathway MSNs remained unchanged. A model of the striatal microcircuit revealed that such increases in FS innervation were effective at enhancing synchrony within targeted cell populations. These data suggest that after dopamine depletion, rapid target-specific microcircuit organization in the striatum may lead to increased synchrony of indirect-pathway MSNs that contributes to pathological network oscillations and motor symptoms of PD.     

Desynchronization of Neocortical Networks by Asynchronous Release of GABA at Autaptic and Synaptic Contacts from Fast-Spiking Interneurons

Networks of specific inhibitory interneurons regulate principal cell firing in several forms of neocortical activity. Fast-spiking (FS) interneurons are potently self-inhibited by GABAergic autaptic transmission, allowing them to precisely control their own firing dynamics and timing. Here we show that in FS interneurons, high-frequency trains of action potentials can generate a delayed and prolonged GABAergic self-inhibition due to sustained asynchronous release at FS-cell autapses. Asynchronous release of GABA is simultaneously recorded in connected pyramidal (P) neurons. Asynchronous and synchronous autaptic release show differential presynaptic Ca²⁺ sensitivity, suggesting that they rely on different Ca²⁺ sensors and/or involve distinct pools of vesicles. In addition, asynchronous release is modulated by the endogenous Ca²⁺ buffer parvalbumin. Functionally, asynchronous release decreases FS-cell spike reliability and reduces the ability of P neurons to integrate incoming stimuli into precise firing. Since each FS cell contacts many P neurons, asynchronous release from a single interneuron may desynchronize a large portion of the local network and disrupt cortical information processing.     

Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons

System identification techniques—projection pursuit regression models (PPRs) and convolutional neural networks (CNNs)—provide state-of-the-art performance in predicting visual cortical neurons’ responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron’s receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron’s receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.     

Efficient and robust coding in heterogeneous recurrent networks

Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, ‘type 1’ and ‘type 2’ neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that ‘type 2’ neurons are more coherent with the overall network activity than ‘type 1’ neurons.     

Retinoic acid functions as a key GABAergic differentiation signal in the basal ganglia

Although retinoic acid (RA) has been implicated as an extrinsic signal regulating forebrain neurogenesis, the processes regulated by RA signaling remain unclear. Here, analysis of retinaldehyde dehydrogenase mutant mouse embryos lacking RA synthesis demonstrates that RA generated by Raldh3 in the subventricular zone of the basal ganglia is required for GABAergic differentiation, whereas RA generated by Raldh2 in the meninges is unnecessary for development of the adjacent cortex. Neurospheres generated from the lateral ganglionic eminence (LGE), where Raldh3 is highly expressed, produce endogenous RA, which is required for differentiation to GABAergic neurons. In Raldh3?/? embryos, LGE progenitors fail to differentiate into either GABAergic striatal projection neurons or GABAergic interneurons migrating to the olfactory bulb and cortex. We describe conditions for RA treatment of human embryonic stem cells that result in efficient differentiation to a heterogeneous population of GABAergic interneurons without the appearance of GABAergic striatal projection neurons, thus providing an in vitro method for generation of GABAergic interneurons for further study. Our observation that endogenous RA is required for generation of LGE-derived GABAergic neurons in the basal ganglia establishes a key role for RA signaling in development of the forebrain.     

Avalanches in a Stochastic Model of Spiking Neurons

Neuronal avalanches are a form of spontaneous activity widely observed in cortical slices and other types of nervous tissue, both in vivo and in vitro. They are characterized by irregular, isolated population bursts when many neurons fire together, where the number of spikes per burst obeys a power law distribution. We simulate, using the Gillespie algorithm, a model of neuronal avalanches based on stochastic single neurons. The network consists of excitatory and inhibitory neurons, first with all-to-all connectivity and later with random sparse connectivity. Analyzing our model using the system size expansion, we show that the model obeys the standard Wilson-Cowan equations for large network sizes ( neurons). When excitation and inhibition are closely balanced, networks of thousands of neurons exhibit irregular synchronous activity, including the characteristic power law distribution of avalanche size. We show that these avalanches are due to the balanced network having weakly stable functionally feedforward dynamics, which amplifies some small fluctuations into the large population bursts. Balanced networks are thought to underlie a variety of observed network behaviours and have useful computational properties, such as responding quickly to changes in input. Thus, the appearance of avalanches in such functionally feedforward networks indicates that avalanches may be a simple consequence of a widely present network structure, when neuron dynamics are noisy. An important implication is that a network need not be “critical” for the production of avalanches, so experimentally observed power laws in burst size may be a signature of noisy functionally feedforward structure rather than of, for example, self-organized criticality.     

Neuroprotection of striatal neurons against kainate excitotoxicity by neurotrophins and GDNF family members

Neurotrophic factors are regarded as potential therapeutic tools in neurodegenerative disorders. Here, we analysed the protective effects of brain-derived neurotrophic factor, neurotrophin-3, glial cell line-derived neurotrophic factor and neurturin against the excitotoxic damage induced by kainate in striatal neurons in vitro and in vivo. Our results show that the decrease in the number of cultured striatal calbindin-positive neurons induced by kainate was prevented by treatment with any of these factors. To characterize their protective effects in vivo, cell lines overexpressing brain-derived neurotrophic factor, neurotrophin-3, glial cell line-derived neurotrophic factor or neurturin were grafted into the striatum. We found that the numbers of striatal projection neurons (calbindin-positive) and striatal interneurons (parvalbumin- or choline acetyltransferase-positive) were differentially decreased after kainate lesion. These neurotrophic factors prevented the loss of striatal projection neurons and interneurons with differing efficiency: brain-derived neurotrophic factor was the most efficient, whereas neurturin was the least. Our findings show that brain-derived neurotrophic factor, neurotrophin-3, glial cell line-derived neurotrophic factor and neurturin have specific neuroprotective profiles in striatal neurons and indicate that they are specific modulators of the survival of distinct subsets of striatal neurons in pathophysiological conditions.     

Cortical interneuron specification and diversification in the era of big data

Inhibition in the mammalian cerebral cortex is mediated by a small population of highly diverse GABAergic interneurons. These largely local neurons are interspersed among excitatory projection neurons and exert pivotal regulation on the formation and function of cortical circuits. We are beginning to understand the extent of GABAergic neuron diversity and how this is generated and shaped during brain development in mice and humans. In this review, we summarise recent findings and discuss how new technologies are being used to further advance our knowledge. Understanding how inhibitory neurons are generated in the embryo is an essential pre-requisite of stem cell therapy, an evolving area of research, aimed at correcting human disorders that result in inhibitory dysfunction.     

Inferring Synaptic Structure in Presence of Neural Interaction Time Scales

Biological networks display a variety of activity patterns reflecting a web of interactions that is complex both in space and time. Yet inference methods have mainly focused on reconstructing, from the network’s activity, the spatial structure, by assuming equilibrium conditions or, more recently, a probabilistic dynamics with a single arbitrary time-step. Here we show that, under this latter assumption, the inference procedure fails to reconstruct the synaptic matrix of a network of integrate-and-fire neurons when the chosen time scale of interaction does not closely match the synaptic delay or when no single time scale for the interaction can be identified; such failure, moreover, exposes a distinctive bias of the inference method that can lead to infer as inhibitory the excitatory synapses with interaction time scales longer than the model’s time-step. We therefore introduce a new two-step method, that first infers through cross-correlation profiles the delay-structure of the network and then reconstructs the synaptic matrix, and successfully test it on networks with different topologies and in different activity regimes. Although step one is able to accurately recover the delay-structure of the network, thus getting rid of any a priori guess about the time scales of the interaction, the inference method introduces nonetheless an arbitrary time scale, the time-bin dt used to binarize the spike trains. We therefore analytically and numerically study how the choice of dt affects the inference in our network model, finding that the relationship between the inferred couplings and the real synaptic efficacies, albeit being quadratic in both cases, depends critically on dt for the excitatory synapses only, whilst being basically independent of it for the inhibitory ones.     

Regimes and mechanisms of transient amplification in abstract and biological neural networks

Neuronal networks encode information through patterns of activity that define the networks’ function. The neurons’ activity relies on specific connectivity structures, yet the link between structure and function is not fully understood. Here, we tackle this structure-function problem with a new conceptual approach. Instead of manipulating the connectivity directly, we focus on upper triangular matrices, which represent the network dynamics in a given orthonormal basis obtained by the Schur decomposition. This abstraction allows us to independently manipulate the eigenspectrum and feedforward structures of a connectivity matrix. Using this method, we describe a diverse repertoire of non-normal transient amplification, and to complement the analysis of the dynamical regimes, we quantify the geometry of output trajectories through the effective rank of both the eigenvector and the dynamics matrices. Counter-intuitively, we find that shrinking the eigenspectrum’s imaginary distribution leads to highly amplifying regimes in linear and long-lasting dynamics in nonlinear networks. We also find a trade-off between amplification and dimensionality of neuronal dynamics, i.e., trajectories in neuronal state-space. Networks that can amplify a large number of orthogonal initial conditions produce neuronal trajectories that lie in the same subspace of the neuronal state-space. Finally, we examine networks of excitatory and inhibitory neurons. We find that the strength of global inhibition is directly linked with the amplitude of amplification, such that weakening inhibitory weights also decreases amplification, and that the eigenspectrum’s imaginary distribution grows with an increase in the ratio between excitatory-to-inhibitory and excitatory-to-excitatory connectivity strengths. Consequently, the strength of global inhibition reveals itself as a strong signature for amplification and a potential control mechanism to switch dynamical regimes. Our results shed a light on how biological networks, i.e., networks constrained by Dale’s law, may be optimised for specific dynamical regimes.     

A Highlights from MBoC Selection: Peptide TFP5/TP5 derived from Cdk5 activator P35 provides neuroprotection in the MPTP model of Parkinson’s disease

Parkinson’s disease (PD) is a chronic neurodegenerative disorder characterized by the loss of dopamine neurons in the substantia nigra, decreased striatal dopamine levels, and consequent extrapyramidal motor dysfunction. Recent evidence indicates that cyclin-dependent kinase 5 (Cdk5) is inappropriately activated in several neurodegenerative conditions, including PD. To date, strategies to specifically inhibit Cdk5 hyperactivity have not been successful without affecting normal Cdk5 activity. Previously we reported that TFP5 peptide has neuroprotective effects in animal models of Alzheimer’s disease. Here we show that TFP5/TP5 selective inhibition of Cdk5/p25 hyperactivation in vivo and in vitro rescues nigrostriatal dopaminergic neurodegeneration induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP/MPP+) in a mouse model of PD. TP5 peptide treatment also blocked dopamine depletion in the striatum and improved gait dysfunction after MPTP administration. The neuroprotective effect of TFP5/TP5 peptide is also associated with marked reduction in neuroinflammation and apoptosis. Here we show selective inhibition of Cdk5/p25 ­hyperactivation by TFP5/TP5 peptide, which identifies the kinase as a potential therapeutic target to reduce neurodegeneration in Parkinson’s disease.     

Cholinergic Plasticity of Oscillating Neuronal Assemblies in Mouse Hippocampal Slices

The mammalian hippocampus expresses several types of network oscillations which entrain neurons into transiently stable assemblies. These groups of co-active neurons are believed to support the formation, consolidation and recall of context-dependent memories. Formation of new assemblies occurs during theta- and gamma-oscillations under conditions of high cholinergic activity. Memory consolidation is linked to sharp wave-ripple oscillations (SPW-R) during decreased cholinergic tone. We hypothesized that increased cholinergic tone supports plastic changes of assemblies while low cholinergic tone favors their stability. Coherent spatiotemporal network patterns were measured during SPW-R activity in mouse hippocampal slices. We compared neuronal activity within the oscillating assemblies before and after a transient phase of carbachol-induced gamma oscillations. Single units maintained their coupling to SPW-R throughout the experiment and could be re-identified after the transient phase of gamma oscillations. However, the frequency of SPW-R-related unit firing was enhanced after muscarinic stimulation. At the network level, these changes resulted in altered patterns of extracellularly recorded SPW-R waveforms. In contrast, recording of ongoing SPW-R activity without intermittent cholinergic stimulation revealed remarkably stable repetitive activation of assemblies. These results show that activation of cholinergic receptors induces plasticity at the level of oscillating hippocampal assemblies, in line with the different role of gamma- and SPW-R network activity for memory formation and –consolidation, respectively.     

How do glutamatergic and GABAergic cells contribute to synchronization in the medial septum?

The medial septum-diagonal band (MSDB) complex is considered as a pacemaker for the hippocampal theta rhythm. Identification of the different cell types, their electro-physiological properties and their possible function in the generation of a synchronized activity in the MSDB is a hot topic. A recent electro-physiological study showed the presence of two antiphasically firing populations of parvalbumin containing GABAergic neurons in the MSDB. Other papers described a network of cluster-firing glutamatergic neurons, which is able to generate synchronized activity in the MSDB. We propose two different computer models for the generation of synchronized population theta oscillation in the MSDB and compare their properties. In the first model GABAergic neurons are intrinsically theta periodic cluster-firing cells; while in the second model GABAergic cells are fast-firing cells and receive periodic input from local glutamatergic neurons simulated as cluster-firing cells. Using computer simulations we show that the GABAergic neurons in both models are capable of generating antiphasic theta periodic population oscillation relying on local, septal mechanisms. In the first model antiphasic theta synchrony could emerge if GABAergic neurons form two populations preferentially innervate each other. In the second model in-phase synchronization of glutamatergic neurons does not require specific network structure, and the network of these cells are able to act as a theta pacemaker for the local fast-firing GABAergic circuit. Our simulations also suggest that neurons being non-cluster-firing in vitro might exhibit clustering properties when connected into a network in vivo. Action Editor: David Golomb     

High Accuracy Decoding of Dynamical Motion from a Large Retinal Population

Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.