Frontiers in Neural Circuits

Although the cerebellar microcircuit is among the most well-characterized systems for studying neural computation, recent connectomic analyses highlight deviations from canonical models, raising fundamental questions about connectivity, function, and learning in the system. A key feature of the circuit is the convergence of a vast number of parallel fibers (PFs) onto Purkinje cells (PCs). Prevailing models of cerebellar computation assume all-to-all connectivity at this intersection, whereby a single PC "samples" from all PFs. However, experimental evidence suggests that each PC is innervated by only a subset of accessible PFs. This partial sampling creates differential connectivity that may reflect an organizational principle of cerebellar learning, but its implications are poorly understood. Based on electron microscopy (EM) reconstructions, we show that PF innervation of PCs is largely consistent with a Bernoulli model where connections are randomly and independently distributed within anatomical constraints. In further support of a random model, we observe that connections of the ascending branches of granule cells are not predictive of connections of their PFs, nor is connectivity correlated across separate spatial encounters of a PF with the same PC. In a model of the cerebellar circuit, we then address the possibility that partial connectivity is a substrate of learning, i.e., a fixed, random mask that enforces diversity between PCs. We find that when considering "ensembles" of PCs, random partial connectivity can indeed outperform all-to-all connectivity. Our results provide a theoretical framework for understanding the role of partial connectivity between cerebellar PFs and PCs and may have implications for cerebellum-like systems and beyond.

Substantial progress in glial electrophysiology has revealed that astrocytes, which account for half of the cells in the human brain, exhibit membrane potentials that often reflect changes in the extracellular environment. Such responses are mediated by a variety of biochemicals, including potassium and neurotransmitters. Recent advances in voltage imaging have provided new insights into voltage activity in astrocyte peripheries, revealing highly localized depolarization that depends on local presynaptic activity. However, the electrophysiological properties of these isolated peripherals have not been explored due to limitations of spatial and temporal resolution. In this study, we aimed to explore differences in the electrophysiological response between whole-cell stimulation and isolated stimuli at different locations in the cell. Therefore, we constructed an empirical conductance-based NEURON model using a realistic morphology to simultaneously capture both astrocyte processes and soma electrophysiological dynamics. Our results predict a breakdown of the Nernstian behavior of astrocytes when potassium stimuli are localized. Instead, local responses are governed by their conductance ratios. Furthermore, we observe strong capabilities for isolating neurotransmitter responses to specific synaptic inputs, with minimal effect on the astrocyte soma. Our study highlights asymmetrical responses of astrocytic electrophysiology that depend on the spatial scale of stimulation.

The intestinal mesentery has been recently classified as a 'new' organ and contains various cell types including adipocytes, preadipocytes, endothelial cells, and immune cells. In addition, neuronal cell bodies are found in the small intestinal mesentery and are situated either individually or clustered together with glial cells in small ganglion structures close to the gut wall. However, little is known about the origin or function of these extra-intestinal mesenteric neurons. The aim of this study was to better these characterize mesenteric neurons and to examine their connectivity with the ENS using calcium imaging in adult mouse ileum with the mesentery attached. Here we show that neurons in the mesentery express typical ENS neurochemical markers, respond to 5-HT, ATP and the nicotinic agonist DMPP, and receive nicotinic synaptic inputs. Furthermore, using labeling with the neuronal tracer DiI, some mesenteric neurons were found to project into the gut wall and can provide functional excitatory inputs to myenteric neurons. By contrast, we did not find evidence for mesenteric neurons providing inputs to other extrinsic neuronal targets, suggesting that they preferentially interact with the ENS. We also demonstrate that mesenteric neurons can be activated by intestinal distension and that the mesentery provides a source of inhibition to the myenteric plexus. Taken together, we show that the ENS not only interacts with vagal and spinal afferents, and sympathetic and parasympathetic nerves, but also neurons situated in the mesentery. Finally, our data suggest that these neurons may provide a form of negative feedback to the myenteric plexus such as in the event of intestinal distension. These findings have important implications for the regulation of intestinal motility in physiological and pathophysiological conditions.

We investigated the dimensions of output zones affecting specific combinations of forelimb muscles in the precentral "motor" cortex of macaque monkeys. Single-pulse intracortical microstimulation (S-ICMS) was used to evoke subthreshold effects in multiple wrist and finger muscles. Results indicate that each motor cortex site represents a different combination of muscles. The effects evoked from cortical sites separated by several hundred microns invariably involved different profiles of muscle activity. The muscle fields of remote CM cells were rarely identical, while the fields of neighboring CM cells were often similar. Given the number of unrecorded muscles, we conclude that primate motor cortex is a mosaic of output sites representing forelimb muscles in different combinations.

Visual signals from the two eyes merge gradually as they pass through the primary visual cortex (V1). Here we use a computational model of Macaque V1 to study the first stage of this integration along the magnocellular pathway, in layer 4C, aiming to infer neuroanatomical origins of binocular response. It is known that neurons in layer 4C are predominantly monocular, though some do exhibit varying degrees of binocularity. We find (1) the emergence of narrow binocular strips along borders of ocular dominance columns (ODC), a finding that aligns with experiments; (2) most consistent with data is when 10 - 30% of interactions near ODC boundaries are cross-columnar; and (3) feedback from layer 6 is largely monocular. These results were obtained through systematic hypothesis testing using a multiscale model that is orders of magnitude faster than its biologically-detailed predecessors. We propose that multiscale modeling can be an effective tool for bridging anatomy and function.

Optical recording from large numbers of neurons is an indispensable technique for studying neuronal ensembles. We use optical sectioning through pinhole illumination to reduce the background fluorescence (F0) and increase the optical signal ({Delta}F/F0) in ex vivo brain slices densely labeled with GCaMP6f, allowing an ordinary fluorescence microscope to capture calcium transients from over 300 individual CA1 neurons - a marked increase compared to ordinary wide field fluorescence illumination. Multiple layers of overlapping neurons can be identified by their locations and the shape in space of their {Delta}F/F0 images. A single pinhole mask was placed at the field stop of a wide field illuminator, and the image of the pinhole was projected onto the tissue by a 20X NA 0.95 water immersion objective (Olympus). This created an illuminated disk with a diameter of [~]200 m and optical sections of hippocampal CA1 pyramidal layer tissue [~]100 m thick. This illumination blocked a large fraction of the F0, which in turn increased the {Delta}F/F0 5-10-fold compared to that of wide field illumination. When putative pyramidal neurons fire sparsely in the brain slice, up to 300 partially superimposed neurons can be identified by their shape and spatial location in the thick ([~]480 m) ex vivo slice in the CA1 area surrounding the pinhole image. The signal-to-noise ratio was adequate even at a low excitation light level of [~]20k photoelectrons per pixel well on the camera, allowing for 3,000 seconds of total recording time without significant bleaching. This pinhole "half confocal" method has created a useful way to sample calcium transient signals in thick tissue with a large population of neurons densely labeled with GCaMP-6f.

A critical step in visual cortical maturation is refinement of receptive field (RF) size, producing higher acuity vision. This was previously studied using species with well-developed vision (e.g., carnivores, primates), in which visual experience was necessary for refinement but not maintenance of RFs in visual cortex. In contrast, in Syrian hamsters, a crepuscular species with low visual acuity, dark rearing had no effect on RF refinement in juveniles, but RFs re-enlarged in adulthood, resulting in reduced acuity. These inter-species differences raise the question of whether the need for visual experience is primarily related to the phylogenetic position of the species or to its ecological niche. Here we report that dark rearing had no effect on development or maintenance of RF properties of visual cortical neurons in nocturnal mice. Mice with lifelong visual deprivation refined and maintained their RF size over time. Furthermore, the development of stimulus direction tuning was unaffected by dark rearing. In contrast, surround suppression, orientation tuning and the sharpness of direction tuning were abnormal in dark reared mice. These and our previous results from hamsters show that species living in an ecological niche with minimal daylight exposure require little to no visual experience to develop and maintain refined RFs. This study is an important step in developing a better understanding of the role of visual experience in the development of visual processing circuitry and suggests that diurnal mammals may be a better model for human visual cortical development than mice.

Cortico-thalamo-cortical circuits entail extensive trans-thalamic connectivity between cortical areas, yet their structural organization and function remain poorly understood. Here, the thalamocortical projections of several higher-order thalamic nuclei were characterized by retrograde tracing from two cortical areas, the primary somatosensory (S1) and motor (M1) cortices. Cholera toxin B conjugated with different fluorophores allowed for simultaneous detection of projection neurons targeting S1 and M1. A cell detection pipeline based on neural networks was developed to allow semi-automated analysis of large thalamic imaging volumes to quantitatively infer the spatial distribution of projection neurons in the posterior complex (PO) and the adjacent ethmoid nucleus (Eth), nucleus centrolateralis (CL), nucleus paracentralis (PCN), and the nucleus parafascicularis (PF). The arrangement of neurons projecting to both, primary somatosensory and motor cortices, occurs at different connection strengths and was topographically organized in all nuclei studied. Co-injections into both cortical areas revealed projection neurons with axons branching into both S1 and M1 cortices. Our work introduces a pipeline for semi-automated quantitative analysis of thalamic projection patterns that could be useful for connectivity analyses in general. This approach revealed repetitive anatomical patterns in different thalamic nuclei with regard to projection strength, spatial organization and fraction of projection neurons targeting two cortical areas simultaneously.

The reliable execution of learned motor sequences poses a challenge: they require reproducible neuron activity patterns for behavioral consistency yet must retain flexibility for modulation that depend on internal states and adaptation to external contingencies. The zebra finch, a songbird, provides an ideal system to examine this problem because their vocalizations are highly stereotyped and are associated with precisely time-locked bursts in HVC, a key nucleus involved in song production. However, their songs are not immutable /totally fixed, as they can be modulated during social interactions and can recover after brain lesions. Here, we used calcium imaging of HVC projection neurons during freely produced songs to track activity across repeated renditions of the same song from seconds to weeks. We first confirmed that song-locked calcium-event timing was stable across days. Surprisingly, not every neuron showed a calcium event on every rendition of the song. We referred to the presence of such an event as participation. This participation was a stable, neuron-specific property that varied with the past and future renditions of the songs within an utterance and depended on social context. Song recovery after brain lesion and refinement during learning further revealed that neural participation could change while sequence timing remained stable. These results suggest that stable motor sequences need not rely on rigid repeated activation of the same cellular ensemble but can preserve temporal order while flexibly selecting which neurons contribute to each expression of the behavior. This provides a cellular solution to the reliability-flexibility tradeoff in learned motor control.

Temperature influences neuronal and circuit output and extreme temperatures can disrupt neuronal performance. Acclimation invokes a form of neuronal plasticity that we call robustness tuning, that preserves nervous system performance during seasonal alterations in environmental conditions. The stomatogastric nervous system (STNS) of the American lobster, Homarus americanus, produces stereotyped rhythmic motor patterns that are maintained over a range of acute temperature changes, but lost under more extreme conditions. In the wild, H. americanus experience water temperatures from [~]2{degrees}C to 25{degrees}C during the course of a year. We acclimated lobsters to 18{degrees}C versus 4{degrees}C for [~]3 weeks, and found that the pyloric rhythm from warm-acclimated animals maintained its characteristic properties over an extended temperature range, when compared to those recorded from cold-acclimated animals. There were acclimation and temperature dependent differences in the responses of pyloric neurons to the neuropeptide, Crustacean Cardioactive Peptide (CCAP). Computational models suggest that pyloric neuron morphology and neuromodulator conductance distribution play a role in robustness tuning, the reversible changes that allow animals to repeatedly adapt to seasonal change.

Motor planning and control is executed by different motor areas within the neocortex. Despite their distinct functions these areas are built by the same archetypes of neurons as the rest of the cortex, with the pyramidal neurons (PNs) as their principal building blocks. Recent results suggest that the synapses of the PNs are modeled and adapted to their required functions in an area specific manner. PN synapses in a cortical area engaged in higher order functions, the prefrontal cortex (PFC), were found to operate with loose microdomain calcium-influx-to-release coupling and showed short-term facilitation, whereas synapses processing sensory information in a lower order cortical area, the primary somatosensory cortex (S1), featured tight nanodomain coupling and showed short-term depression. In the present study, we asked for the functional coupling configuration of an intermediate processing area. We focused on PN synapses in the premotor cortex M2 and compared their properties to those of PN synapses in the primary motor cortex M1. In both areas we found tight nanodomain coupling and high release probability, but a significant difference in short-term plasticity. Synapses in M1 showed paired-pulse depression similar to S1. In contrast, synapses in M2 exhibited paired-pulse facilitation. Our data suggest that this facilitation results from an accelerated recruitment of synaptic vesicles to the readily releasable pool from an enlarged replenishment pool. Thus, PN synapses in M2 appear to have properties intermediate between those in PFC and M1.

Capturing naturalistic behavioral dynamics is essential for understanding social interaction in ecologically valid settings. Existing investigations of naturalistic social interaction rely on time-aggregated analysis methods better suited for task-based experiments, which lose the complex, moment-to-moment dynamics exhibited in naturalistic settings. The emerging field of topological data analysis (TDA) provides new tools to characterize fine-grained dynamics in time-series data that cannot be captured by time-averaged methods. The present work utilizes Temporal Mapper, a recently developed TDA specifically tailored to analyzing dynamical systems. Temporal Mapper characterizes complex temporal dynamics as transition networks, where nodes are stable states and edges are transitions between states. Originally designed for human neural time series analysis, here we demonstrate the utility of Temporal Mapper to capture rich animal postural dynamics during naturalistic social interaction. We utilized an existing dataset with 12 video recording sessions of two domestic ferrets (Mustela putorius furo) during naturalistic interaction and tracked the postures of animals during social interaction. Ferrets were chosen due to their strong social-cognitive skills and rich postural dynamics for investigating social behavior via posture estimation. Temporal Mapper was then used to represent the postural dynamics as transition networks for each recording session. Here, we found that posture states are significantly smaller and more widespread during active social interaction compared to non-social activities. Additionally, the number of sequential postural states before transitioning to new behaviors is more consistent during active social interaction than non-social activities. Together, our findings suggest that social activity has a broad range of unstable postural states arranged in consistent sequences. Our method, Temporal Mapper, allows for network structure analysis of complex naturalistic data, applicable for characterizing rich dynamics in different species, scales, and paradigms.

Ampullary electroreceptors are widespread across aquatic vertebrates. The purpose of sensing exogeneous electric fields is conserved across species but the implementations differ and the encoding mechanisms remain incompletely understood. We compared baseline and stimulus-driven response properties of ampullary electroreceptor afferents in the weakly electric fish Apteronotus leptorhynchus and Eigenmannia virescens. We find that their activity is well captured by an extended leaky integrate-and-fire model that generalizes across both species. The model shares similarities to a previous model of the tuberous electroreceptor afferents but further incorporates a low-pass pre-filtering and additional noise sources to reproduce the observed spectral response characteristics. The low-pass is essential to shape stimulus encoding in the high-frequency range. Accurate prediction of low-frequency stimulus encoding further requires two distinct noise sources: stimulus-independent white current noise and activity-dependent noise in the adaptation current, which is shaped by the adaptation time constant to yield effective pink noise dynamics. Using simulation-based inference, we trained a neural network to map model parameters to neuronal response features. This approach enables the generation of heterogeneous, biologically plausible model populations that may serve as a realistic input layer for studying neuronal processing on the next level. With this, we provide a unified and mechanistic model of ampullary electroreceptor encoding in these species and possibly beyond.

The hippocampus is characterized by a stereotypical macroscopic structure, where the nuclei are densely and heterogeneously packed among different subregions of the hippocampus. Despite the fact that tissue-specific cellular organization has been implicated in neural function, it has been technically challenging to quantitatively analyze mesoscopic cellular organization in the hippocampus due to its high cellular density. To overcome this technical hurdle, we developed Computational Biophysical Histomorphometry Software (CBHS), an automated image-analysis pipeline, aimed at quantifying nuclear shape and the order of the cellular ensemble in high-density areas. When applied to the subfields of hippocampus, we found that denser regions, most notably the dentate gyrus, were the most positionally, but least orientationally ordered. Nuclear shape exhibited a dependence on the local environment in a packing-dependent manner. This association was cell-type specific, with neurons, but not astrocytes displaying nuclear shape that varied with neighbour proximity, although astrocytes demonstrated greater intrinsic shape variance. The results reveal the presence of reproducible mesoscale cell packing order in hippocampal tissue, and are consistent with a nucleus-driven mechanical coupling between neighbouring cells. The present study provides a quantitative framework with which to understand mesoscopic tissue organization, thus enabling the formulation of testable hypotheses for future investigation.

Developmental experience can produce lasting changes in neural function and behavior. Zebra finch offers a powerful model for identifying the molecular mechanisms underlying this process. Both juvenile male and female zebra finches perform developmental sensory song learning that influences their adult behaviors: in males, the structure of the song they sing and in female, the song preferences they exhibit (females cannot sing). The auditory forebrain, a region distinct from but connected to nodes of the male singing circuitry, is required for male sensory song learning. Song experience induces epigenetic, genomic, molecular, cellular and systems-level alterations in the auditory forebrain of males. Much less evidence is available for females. Although epigenetic and molecular data implicate the auditory forebrain in female sensory song learning, there has been no causal test of its role. Further, molecular evidence indicates the potential for distinct mechanisms for male and female sensory song learning, even though they learn during a largely overlapping developmental period. We used pharmacological manipulations of the ERK and mTOR cascades in the auditory forebrain of juvenile females during controlled tutoring, and an operant assay for adult song preference, to test the causal role of the auditory forebrain and the two cascades known to be required for male sensory song learning. We demonstrate that the auditory forebrain is required for female sensory song learning, and that while ERK signaling is necessary for both sexes, that of mTOR is sex specific. Results raise implications for alternative molecular cascade cross-talks and protein synthesis processes that successfully support the developmental learning at the same age and brain region.

The electrophysiological process of auditory transduction in insects remains largely conjecture due to the unknown role of ion channels localised to the cilia, but experimental evidence supports either NompC or Nan-Iav as the auditory mechanotransduction ion channel. Here, we knocked down two key genes that code for the two candidate sound-activated ion channels using dsRNA-mediated RNA interference. We measured sound-evoked activity of the auditory nerve and intracellular electrical currents from the ciliated ending of individual auditory receptors. We found that the sound-evoked nerve activity was reduced in nompC, nan and ift88 knockdown. Using whole-cell patch clamp recordings we found that nompC and nan knockdown resulted in reduced sound-evoked transduction current. Stochastic depolarisations hypothesised to be mediated from one of the candidate mechanotransduction ion channels, either NompC or Nan-Iav, where not affected by knockdown of either channel. The discrete depolarisations are therefore mediated through another unidentified ion channel. We test the hypothesis that discrete depolarisations are graded action potentials that travel toward the soma through noise analysis of the transduction current and analysis of discrete depolarisations to voltage-steps. As a positive control we also knocked down ift88, a protein essential for transporting proteins, including ion channels, along the cilium and found both the transduction current and the discrete depolarisations decreased. Key pointsO_LIInjection of dsRNA decreased RNA of nompC and nan C_LIO_LISound-evoked nerve activity is reduced for RNAi-mediated knockdown of nompC and nan C_LIO_LINompC and Nan both contribute to the transduction current C_LIO_LIThe stochastic discrete depolarisations are not due to NompC or Nan-Iav ion channel but to a third unidentified ion channel. C_LIO_LINoise analysis of the transduction current and the discrete depolarisations suggests they are graded action potentials that travel in the direction of the soma. C_LIO_LIKnockdown of ift88 reduced both the transduction current and discrete depolarisations. C_LI Significance StatementInsects are important to understand, economically, agriculturally and medically. However, we still do not understand fundamental aspects of how insects detect their own body movements, vibrations and sound. These senses are detected by insect chordotonal organs, specialised miniaturised mechanoreceptors that convert movements into electrical signals through specialised ion channels. Previous experimental work has advocated either NompC or Nan-Iav as the mechanosensitive ion channel. Here, for the first time, we reduced the expression of both nompC and nan and measured the sound-evoked transduction current directly from neurons in a specialised auditory chordotonal organ. In contradiction to previous studies, we show that both ion channels contribute to the transduction current and find that electrical signals termed "discrete depolarisations" travel toward the soma.

Although muscle spindle sensory signals have been extensively studied, little is known about how and why muscle spindle firing is modulated by the central nervous system during movement. Specialized motor neurons to the muscle spindle, i.e. gamma motor neurons, can profoundly alter spindle firing during behavior, but technological limitations hinder our ability to record gamma motor and muscle spindle sensory signals during most behaviors. We used a biophysical model of a muscle spindle within a muscle-tendon unit to simulate how gamma drive may modulate muscle spindle Ia firing during locomotion. Based on a few available recordings from decerebrate animals, we demonstrate that our model, tuned to passive stretch conditions, can reproduce profound changes in muscle spindle firing in response to identical joint motions in locomotor vs. relaxed stretch conditions. Our model can discover phasic patterns of two types of gamma motor neuron drive based on recorded muscle spindle Ia firing and joint motion. By simulating perturbations, we conclude that: 1) sinusoidal activation of static gamma motor neurons during locomotion, encoding intended movement, modulates muscle spindle signals such that they act as sensorimotor feedback signals based on errors from the intended muscle fascicle length; 2) phasic on/off activation of dynamic gamma motor neurons during locomotion acts as an event detector, heightening muscle spindle Ia responses to discrete perturbations. As such, their muscle-within-muscle structure allows the muscle spindle to act as a highly tunable physical internal model of muscle state to guide movement. Our model supports proposed but as-yet-untested theories of muscle spindle function and offers a framework for extending the testing of muscle spindle function to active, behavioral conditions.

Understanding how network connectivity shapes neural representations is central to systems neuroscience. While dimensionality reduction methods uncover low-dimensional manifold structure in population recordings, a rigorous framework connecting manifold geometry to network mechanisms and information encoding remains lacking. We develop a differential geometric approach for analyzing neural manifolds in rate-based recurrent networks receiving tuned feedforward inputs. We derive expressions for the pullback metric of neural manifolds, showing how input tuning curves, feedforward and recurrent synaptic connectivity shape manifold geometry. Critically, we establish that the Fisher information matrix at steady states also has the structure of a pullback metric, directly linking intrinsic manifold geometry to stimulus discriminability and information encoding. For noise with slow temporal correlations propagated through the network, we show that recurrent effects on information geometry cancel: Fisher information depends only on the feedforward connectivity. Thus, feedforward connectivity critically determines representational geometry. As an example, we demonstrate that the representation of space by a module of hexagonal grid cells is approximately isometric for random distribution of grid phases. Moreover, a linear feedforward transformation can map spatially random input tuning curves into a population of hexagonal grid cells, forming a toroidal manifold. Thus, feedforward connectivity alone can generate structured spatial representations without requiring carefully tuned recurrent connectivity or continuous attractor dynamics. Recurrent connectivity, however, is shown to improve stimulus encoding under fast noise, thereby implementing a selective noise reduction.

Primate brain function relies on distributed cortical networks. These networks are commonly identified through fMRI functional connectivity, defined as the spatial correlation of hemodynamic fluctuations measured at rest. To assess the contribution of distinct neuronal populations to fMRI functional connectivity, we obtained concurrent fMRI and dense single-unit recordings at rest in the macaque. Then, using standard waveform-based classification of action potential shape, we compared the activity of different neural subtypes to the local and brain-wide patterns of fMRI activity. Putative excitatory neurons were functionally intermixed, with approximately half having positive and half negative correlation with the local fMRI signal. By contrast, all putative inhibitory interneurons were positively correlated, with one subclass exhibiting brain-wide correlation that closely matched conventional seed-based functional connectivity. These findings indicate that, although excitatory projection neurons may underpin long-range network communication interneuron activity most closely matches the fMRI fluctuations at the heart of resting functional connectivity.

Background / Introduction: Biologically inspired spiking neural networks can model adaptive behavior, but learning multiple goals is difficult because synaptic updates for different targets can interfere. We tested whether multi-timescale plasticity and context-specific credit assignment could improve continual multi-goal learning in a spiking navigation system inspired by entorhinal-hippocampal circuitry. Methods: We developed a closed-loop spiking model containing grid-like, place-like, target-related, association, and motor-output populations. An agent navigated in a two-dimensional environment with randomized starting locations and learned through reward-modulated spike-timing dependent plasticity (STDP/RL) and a novel evidence-gated plasticity (EGP) framework. EGP accumulates candidate synaptic modifications, evaluates them using reward evidence, and consolidates only changes that improve performance. A target-context variant maintained separate proposal stores and reward evaluation for each target. Results: STDP/RL learned and retained a single-target navigation policy, but multi-target training produced substantial interference, including attraction to incorrect targets after learning. Across 10 connectivity seeds, target-context EGP achieved higher late-stage reward than global EGP, improved weakest-target performance, and increased the fraction of targets achieving positive reward. In a longer continual-learning simulation, reward increased for all targets, TEST-phase performance increasingly exceeded TRAIN-phase performance, and proposal magnitudes grew over learning. Dwell-time confusion analyses showed that target-context EGP reduced wrong-target attraction and improved target selectivity relative to multi-target STDP/RL. Conclusions: These results demonstrate that spiking navigation circuits can learn goal-directed behavior using local plasticity, but robust multi-goal learning benefits from context-specific evidence-based consolidation. Target-context EGP provides a biologically motivated mechanism for reducing interference during continual reinforcement learning in spiking neural networks.