Striosomes Mediate Value-Based Learning Vulnerable in Age and a Huntington's Disease Model.

Learning valence-based responses to favorable and unfavorable options requires judgments of the relative value of the options, a process necessary for species survival. We found, using engineered mice, that circuit connectivity and function of the striosome compartment of the striatum are critical for this type of learning. Calcium imaging during valence-based learning exhibited a selective correlation between learning and striosomal but not matrix signals. This striosomal activity encoded discrimination learning and was correlated with task engagement, which, in turn, could be regulated by chemogenetic excitation and inhibition. Striosomal function during discrimination learning was disturbed with aging and severely so in a mouse model of Huntington's disease. Anatomical and functional connectivity of parvalbumin-positive, putative fast-spiking interneurons (FSIs) to striatal projection neurons was enhanced in striosomes compared with matrix in mice that learned. Computational modeling of these findings suggests that FSIs can modulate the striosomal signal-to-noise ratio, crucial for discrimination and learning.

Radial Glial Lineage Progression and Differential Intermediate Progenitor Amplification Underlie Striatal Compartments and Circuit Organization.

The circuitry of the striatum is characterized by two organizational plans: the division into striosome and matrix compartments, thought to mediate evaluation and action, and the direct and indirect pathways, thought to promote or suppress behavior. The developmental origins of these organizations and their developmental relationships are unknown, leaving a conceptual gap in understanding the cortico-basal ganglia system. Through genetic fate mapping, we demonstrate that striosome-matrix compartmentalization arises from a lineage program embedded in lateral ganglionic eminence radial glial progenitors mediating neurogenesis through two distinct types of intermediate progenitors (IPs). The early phase of this program produces striosomal spiny projection neurons (SPNs) through fate-restricted apical IPs (aIPSs) with limited capacity; the late phase produces matrix SPNs through fate-restricted basal IPs (bIPMs) with expanded capacity. Notably, direct and indirect pathway SPNs arise within both aIPS and bIPM pools, suggesting that striosome-matrix architecture is the fundamental organizational plan of basal ganglia circuitry.

Chronic Stress Alters Striosome-Circuit Dynamics, Leading to Aberrant Decision-Making.

Effective evaluation of costs and benefits is a core survival capacity that in humans is considered as optimal, "rational" decision-making. This capacity is vulnerable in neuropsychiatric disorders and in the aftermath of chronic stress, in which aberrant choices and high-risk behaviors occur. We report that chronic stress exposure in rodents produces abnormal evaluation of costs and benefits resembling non-optimal decision-making in which choices of high-cost/high-reward options are sharply increased. Concomitantly, alterations in the task-related spike activity of medial prefrontal neurons correspond with increased activity of their striosome-predominant striatal projection neuron targets and with decreased and delayed striatal fast-firing interneuron activity. These effects of chronic stress on prefronto-striatal circuit dynamics could be blocked or be mimicked by selective optogenetic manipulation of these circuits. We suggest that altered excitation-inhibition dynamics of striosome-based circuit function could be an underlying mechanism by which chronic stress contributes to disorders characterized by aberrant decision-making under conflict. VIDEO ABSTRACT.

Analysis of complex neural circuits with nonlinear multidimensional hidden state models.

A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamically interact to bring about thought and action. Granger causality is a powerful tool for identifying linear interactions, but handling nonlinear interactions remains an unmet challenge. We present a nonlinear multidimensional hidden state (NMHS) approach that achieves interaction strength analysis and decoding of networks with nonlinear interactions by including latent state variables for each node in the network. We compare NMHS to Granger causality in analyzing neural circuit recordings and simulations, improvised music, and sociodemographic data. We conclude that NMHS significantly extends the scope of analyses of multidimensional, nonlinear networks, notably in coping with the complexity of the brain.

A Corticostriatal Path Targeting Striosomes Controls Decision-Making under Conflict.

A striking neurochemical form of compartmentalization has been found in the striatum of humans and other species, dividing it into striosomes and matrix. The function of this organization has been unclear, but the anatomical connections of striosomes indicate their relation to emotion-related brain regions, including the medial prefrontal cortex. We capitalized on this fact by combining pathway-specific optogenetics and electrophysiology in behaving rats to search for selective functions of striosomes. We demonstrate that a medial prefronto-striosomal circuit is selectively active in and causally necessary for cost-benefit decision-making under approach-avoidance conflict conditions known to evoke anxiety in humans. We show that this circuit has unique dynamic properties likely reflecting striatal interneuron function. These findings demonstrate that cognitive and emotion-related functions are, like sensory-motor processing, subject to encoding within compartmentally organized representations in the forebrain and suggest that striosome-targeting corticostriatal circuits can underlie neural processing of decisions fundamental for survival.

A multistage mathematical approach to automated clustering of high-dimensional noisy data.

A critical problem faced in many scientific fields is the adequate separation of data derived from individual sources. Often, such datasets require analysis of multiple features in a highly multidimensional space, with overlap of features and sources. The datasets generated by simultaneous recording from hundreds of neurons emitting phasic action potentials have produced the challenge of separating the recorded signals into independent data subsets (clusters) corresponding to individual signal-generating neurons. Mathematical methods have been developed over the past three decades to achieve such spike clustering, but a complete solution with fully automated cluster identification has not been achieved. We propose here a fully automated mathematical approach that identifies clusters in multidimensional space through recursion, which combats the multidimensionality of the data. Recursion is paired with an approach to dimensional evaluation, in which each dimension of a dataset is examined for its informational importance for clustering. The dimensions offering greater informational importance are given added weight during recursive clustering. To combat strong background activity, our algorithm takes an iterative approach of data filtering according to a signal-to-noise ratio metric. The algorithm finds cluster cores, which are thereafter expanded to include complete clusters. This mathematical approach can be extended from its prototype context of spike sorting to other datasets that suffer from high dimensionality and background activity.

Shifting responsibly: the importance of striatal modularity to reinforcement learning in uncertain environments.

We propose here that the modular organization of the striatum reflects a context-sensitive modular learning architecture in which clustered striosome-matrisome domains participate in modular reinforcement learning (RL). Based on anatomical and physiological evidence, it has been suggested that the modular organization of the striatum could represent a learning architecture. There is not, however, a coherent view of how such a learning architecture could relate to the organization of striatal outputs into the direct and indirect pathways of the basal ganglia, nor a clear formulation of how such a modular architecture relates to the RL functions attributed to the striatum. Here, we hypothesize that striosome-matrisome modules not only learn to bias behavior toward specific actions, as in standard RL, but also learn to assess their own relevance to the environmental context and modulate their own learning and activity on this basis. We further hypothesize that the contextual relevance or "responsibility" of modules is determined by errors in predictions of environmental features and that such responsibility is assigned by striosomes and conveyed to matrisomes via local circuit interneurons. To examine these hypotheses and to identify the general requirements for realizing this architecture in the nervous system, we developed a simple modular RL model. We then constructed a network model of basal ganglia circuitry that includes these modules and the direct and indirect pathways. Based on simple assumptions, this model suggests that while the direct pathway may promote actions based on striatal action values, the indirect pathway may act as a gating network that facilitates or suppresses behavioral modules on the basis of striatal responsibility signals. Our modeling functionally unites the modular compartmental organization of the striatum with the direct-indirect pathway divisions of the basal ganglia, a step that we suggest will have important clinical implications.

Brain stem feedback in a computational model of birdsong sequencing.

Uncovering the roles of neural feedback in the brain is an active area of experimental research. In songbirds, the telencephalic premotor nucleus HVC receives neural feedback from both forebrain and brain stem areas. Here we present a computational model of birdsong sequencing that incorporates HVC and associated nuclei and builds on the model of sparse bursting presented in our preceding companion paper. Our model embodies the hypotheses that 1) different networks in HVC control different syllables or notes of birdsong, 2) interneurons in HVC not only participate in sparse bursting but also provide mutual inhibition between networks controlling syllables or notes, and 3) these syllable networks are sequentially excited by neural feedback via the brain stem and the afferent thalamic nucleus Uva, or a similar feedback pathway. We discuss the model's ability to unify physiological, behavioral, and lesion results and we use it to make novel predictions that can be tested experimentally. The model suggests a neural basis for sequence variations, shows that stimulation in the feedback pathway may have different effects depending on the balance of excitation and inhibition at the input to HVC from Uva, and predicts deviations from uniform expansion of syllables and gaps during HVC cooling.

Inhibition and recurrent excitation in a computational model of sparse bursting in song nucleus HVC.

The telencephalic premotor nucleus HVC is situated at a critical point in the pattern-generating premotor circuitry of oscine songbirds. A striking feature of HVC's premotor activity is that its projection neurons burst extremely sparsely. Here we present a computational model of HVC embodying several central hypotheses: 1) sparse bursting is generated in bistable groups of recurrently connected robust nucleus of the arcopallium (RA)-projecting (HVCRA) neurons; 2) inhibitory interneurons terminate bursts in the HVCRA groups; and 3) sparse sequences of bursts are generated by the propagation of waves of bursting activity along networks of HVCRA neurons. Our model of sparse bursting places HVC in the context of central pattern generators and cortical networks using inhibition, recurrent excitation, and bistability. Importantly, the unintuitive result that inhibitory interneurons can precisely terminate the bursts of HVCRA groups while showing relatively sustained activity throughout the song is made possible by a specific constraint on their connectivity. We use the model to make novel predictions that can be tested experimentally.

Synaptic plasticity with discrete state synapses.

Experimental observations on synaptic plasticity at individual glutamatergic synapses from the CA3 Shaffer collateral pathway onto CA1 pyramidal cells in the hippocampus suggest that the transitions in synaptic strength occur among discrete levels at individual synapses [C. C. H. Petersen, Proc. Natl. Acad. Sci. USA 85, 4732 (1998); O'Connor, Wittenberg, and Wang, D. H. O'Connor, Proc. Natl. Acad. Sci. USA (to be published); J. M. Montgomery and D. V. Madison, Trends Neurosci. 27, 744 (2004)]. This happens for both long term potentiation (LTP) and long term depression (LTD) induction protocols. O'Connor, Wittenberg, and Wang have argued that three states would account for their observations on individual synapses in the CA3-CA1 pathway. We develop a quantitative model of this three-state system with transitions among the states determined by a competition between kinases and phosphatases shown by D. H. O'Connor, to be determinant of LTP and LTD, respectively. Specific predictions for various plasticity protocols are given by coupling this description of discrete synaptic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor ligand gated ion channel conductance changes to a model of postsynaptic membrane potential and associated intracellular calcium fluxes to yield the transition rates among the states. We then present various LTP and LTD induction protocols to the model system and report the resulting whole cell changes in AMPA conductance. We also examine the effect of our discrete state synaptic plasticity model on the synchronization of realistic oscillating neurons. We show that one-to-one synchronization is enhanced by the plasticity we discuss here and the presynaptic and postsynaptic oscillations are in phase. Synaptic strength saturates naturally in this model and does not require artificial upper or lower cutoffs, in contrast to earlier models of plasticity.

Dynamical model of birdsong maintenance and control.

The neuroethology of song learning, production, and maintenance in songbirds presents interesting similarities to human speech. We have developed a biophysical model of the manner in which song could be maintained in adult songbirds. This model may inform us about the human counterpart to these processes. In songbirds, signals generated in nucleus High Vocal center (HVc) follow a direct route along a premotor pathway to the robust nucleus of the archistriatum (RA) as well as an indirect route to RA through the anterior forebrain pathway (AFP): the neurons of RA are innervated from both sources. HVc expresses very sparse bursts of spikes having interspike intervals of about 2 ms. The expressions of these bursts arrive at the RA with a time difference DeltaT approximately equal to 50+/-10 ms between the two pathways. The observed combination of AMPA and NMDA receptors at RA projection neurons suggests that long-term potentiation and long-term depression can both be induced by spike timing plasticity through the pairing of the HVc and AFP signals. We present a dynamical model that stabilizes this synaptic plasticity through a feedback from the RA to the AFP using known connections. The stabilization occurs dynamically and is absent when the RA-->AFP connection is removed. This requires a dynamical selection of DeltaT. The model does this, and DeltaT lies within the observed range. Our model represents an illustration of a functional consequence of activity-dependent plasticity directly connected with neuroethological observations. Within the model the parameters of the AFP, and thus the magnitude of DeltaT, can also be tuned to an unstable regime. This means that destabilization might be induced by neuromodulation of the AFP.

Spike timing and synaptic plasticity in the premotor pathway of birdsong.

The neural circuits of birdsong appear to utilize specific time delays in their operation. In particular, the anterior forebrain pathway (AFP) is implicated in an approximately 40- to 50- ms time delay, DeltaT, playing a role in the relative timing of premotor signals from the nucleus HVc to the nucleus robust nucleus of the archistratium (RA) and control/learning signals from the nucleus lateral magnocellular nucleus of the anterior neostratium (lMAN) to RA. Using a biophysical model of synaptic plasticity based on experiments on mammalian hippocampal and neocortical pyramidal neurons, we propose an understanding of this approximately 40- to 50- ms delay. The biophysical model describes the influence of Ca2+ influx into the postsynaptic RA cells through NMDA and AMPA receptors and the induction of LTP and LTD through complex metabolic pathways. The delay, DeltaT, between HVc --> RA premotor signals and lMAN --> RA control/learning signals plays an essential role in determining if synaptic plasticity is induced by signaling from each pathway into RA. If DeltaT is substantially larger than 40 ms, no plasticity is induced. If DeltaT is much less than 40 ms, only potentiation is expected. If DeltaT approximately 40 ms, the sign of synaptic plasticity is sensitive to DeltaT. Our results suggest that changes in DeltaT may influence learning and maintenance of birdsong. We investigate the robustness of this result to noise and to the removal of the Ca2+ contribution from lMAN --> RA NMDA receptors.

Mapping neural architectures onto acoustic features of birdsong.

The motor pathway responsible for the complex vocalizations of songbirds has been extensively characterized, both in terms of intrinsic and synaptic physiology in vitro and in terms of the spatiotemporal patterns of neural activity in vivo. However, the relationship between the neural architecture of the song motor pathway and the acoustic features of birdsong is not well understood. Using a computational model of the song motor pathway and the songbird vocal organ, we investigate the relationship between song production and the neural connectivity of nucleus HVc (used as a proper name) and the robust nucleus of the archistriatum (RA). Drawing on recent experimental observations, our neural model contains a population of sequentially bursting HVc neurons driving the activity of a population of RA neurons. An important focus of our investigations is the contribution of intrinsic circuitry within RA to the acoustic output of the model. We find that the inclusion of inhibitory interneurons in the model can substantially influence the features of song syllables, and we illustrate the potential for subharmonic behavior in RA in response to forcing by HVc neurons. Our results demonstrate the association of specific acoustic features with specific neural connectivities and support the view that intrinsic circuitry within RA may play a critical role in generating the features of birdsong.

Biophysical model of synaptic plasticity dynamics.

We discuss a biophysical model of synaptic plasticity that provides a unified view of the outcomes of synaptic modification protocols, including: (1) prescribed time courses of postsynaptic intracellular Ca(2+) release, (2) postsynaptic voltage clamping with presentation of presynaptic spike trains at various frequencies, (3) direct postsynaptic response to presynaptic spike trains at various frequencies, and (4) LTP/LTD as a response to precisely timed presynaptic and postsynaptic spikes.