There are numerous experience-driven changes in cortical circuitry that correlate with improved performance. Improved motor performance on a reach-to-grasp task in rodents is associated with changes ...in long-term potentiation (LTP), synaptogenesis, and movement representations in primary motor cortex (M1) by training days 3, 7, and 10, respectively. We recorded single-cell activity patterns in M1 during reach-to-grasp training to test how neural-spiking properties change with respect to LTP, synaptogenesis, and motor map changes. We also tested how neural-spiking changes relate directly to improved performance by monitoring muscle activity patterns. We found that signal-to-noise ratios (SNRs) of M1 spiking were significantly improved with practice but only after 7-12 d. Three sources of noise were assessed: signal-dependent noise exemplified by the slope of the relationship between mean spike count and count variance per burst, signal-independent noise exemplified by the offset of this relationship, and background firing rates before and after bursts. Signal-independent noise and pre-burst firing rates were reduced with practice. Early performance gains (days 1-6) were dissociated from SNR improvements, whereas later performance gains (day 7-12) were related directly to the magnitude of improvement in both muscle recruitment reliability and success rates. With training, an increased number of cells exhibited firing rates that were correlated with muscle recruitment patterns, with lags suggesting a primary direction of influence from M1 to muscles. These results suggest a functional linkage from local synaptogenesis in M1 to improved spiking reliability of M1 cells to more reliable recruitment of muscles and finally to improved behavioral performance.
Animals adapt action-selection policies when the relationship between possible actions and associated outcomes changes. Prefrontal cortical neurons vary their discharge patterns depending on action ...choice and rewards received and undoubtedly play a pivotal role in maintaining and adapting action policies. Here, we recorded neurons from the medial precentral subregion of mouse prefrontal cortex to examine neural substrates of goal-directed behavior. Discharge patterns were recorded after animals developed stable action-selection policies, wherein four possible action sequences were invariably related to different reward magnitudes and during adaptation to changes in the action-reward contingencies. During the adaptation period, when the same action sequence resulted in different reward magnitudes, many neurons (38%) exhibited significantly different discharge patterns for identical action sequences, well before reaching the reward site. In addition, trial-to-trial reliability of ensemble pattern production leading up to reward was found to vary both positively and negatively with increases and decreases in reward magnitude, respectively. Pairwise analyses of simultaneously recorded neurons revealed that decreased reliability in part reflected fluctuations between different ensemble activity patterns as opposed to within-pattern variability. Increases in reliability were related to an increased probability of both selecting highly rewarding actions and completing such actions without pause or reversal, whereas decreases in reliability were associated with the opposite pattern. Thus, we suggest that both the spatiotemporal pattern and fidelity of prefrontal cortical discharge are impacted by action-outcome relationships and that each of these features serve to adapt action choices and maintain behaviors leading to reward.
Cholinergic, GABAergic and glutamatergic projection neurons of the basal forebrain (BF) innervate widespread regions of the neocortex and are thought to modulate learning and attentional processes. ...Although it is known that neuronal cell types in the BF exhibit oscillatory firing patterns, whether the BF as a whole shows oscillatory field potential activity, and whether such neuronal patterns relate to components of cognitive tasks, has yet to be determined. To this end, local field potentials (LFPs) were recorded from the BF of rats performing an associative learning task wherein neutral objects were paired with differently valued reinforcers (pellets). Over time, rats developed preferences for the different objects based on pellet‐value, indicating that the pairings had been well learned. LFPs from all rats revealed robust, short‐lived bursts of beta‐frequency oscillations (∼25 Hz) around the time of object encounter. Beta‐frequency LFP events were found to be learning‐dependent, with beta‐frequency peak amplitudes significantly greater on the first day of the task when object–reinforcement pairings were novel than on the last day when pairings were well learned. The findings indicate that oscillatory bursting field potential activity occurs in the BF in freely behaving animals. Furthermore, the temporal distribution of these bursts suggests that they are probably relevant to associative learning.
Attention is a complex neurobiological process that involves rapidly and flexibly balancing sensory input and goal-directed predictions in response to environmental changes. The cholinergic and ...noradrenergic systems, which have been proposed to respond to expected and unexpected environmental uncertainty, respectively, play an important role in attention by differentially modulating activity in a multitude of cortical targets. Here we develop a model of an attention task that involves expected and unexpected uncertainty. The cholinergic and noradrenergic systems track this uncertainty and, in turn, influence cortical processing in five different, experimentally verified ways: (1) nicotinic enhancement of thalamocortical input, (2) muscarinic regulation of corticocortical feedback, (3) noradrenergic mediation of a network reset, (4) locus coeruleus (LC) activation of the basal forebrain (BF), and (5) cholinergic and noradrenergic balance between sensory input and frontal cortex predictions. Our results shed light on how the noradrenergic and cholinergic systems interact with each other and a distributed set of neural areas, and how this could lead to behavioral adaptation in the face of uncertainty.
Analyzing neural dynamics underlying complex behavior is a major challenge in systems neurobiology. To meet this challenge through computational neuroscience, we have constructed a brain-based device ...(Darwin X) that interacts with a real environment, and whose behavior is guided by a simulated nervous system incorporating detailed aspects of the anatomy and physiology of the hippocampus and its surrounding regions. Darwin X integrates cues from its environment to solve a spatial memory task. Place-specific units, similar to place cells in the rodent, emerged by integrating visual and self-movement cues during exploration without prior assumptions in the model about environmental inputs. Because synthetic neural modeling using brain-based devices allows recording from all elements of the simulated nervous system during behavior, we were able to identify different functional hippocampal pathways. We did this by tracing back from reference neuronal units in the CA1 region of the simulated hippocampus to all of the synaptically connected units that were coactive during a particular exploratory behavior. Our analysis identified a number of different functional pathways within the simulated hippocampus that incorporate either the perforant path or the trisynaptic loop. Place fields, which were activated by the trisynaptic circuit, tended to be more selective and informative. However, place units that were activated by the perforant path were prevalent in the model and were crucial for generating appropriate exploratory behavior. Thus, in the model, different functional pathways influence place field activity and, hence, behavior during navigation.
Neuronal inter-spike intervals (ISIs) have previously been described as Poisson, Gamma, inverse Gaussian or other unimodal distributions. We analyzed ISIs of rhythmic and arrhythmic neuronal spike ...trains in cerebellum recorded from freely behaving rats, and found that their distributions can be described as the summation or integration of multiple Gaussian distributions. The ISIs of rhythmic cerebellar Purkinje cells have a main Gaussian peak at a basic firing interval and exponentially reduced peaks at multiples of this firing period. ISIs of arrhythmic Purkinje cells can be modeled as the integration of multiple Gaussian distributions centered at continuous intervals with exponentially reduced peak amplitudes. The sources of variability are directly related to the relative timing of action potentials between neighboring cells since we show that irregularities of discharge in one cell are associated with the previous history of its discharge in time relative to another cell. Through relative phase analyses, we demonstrate that the shape and the mathematical form of the ISI distributions in cerebellum are direct result of dynamic interactions in the nearby neuronal network, in addition to intrinsic firing properties. The analysis in this paper provides a unified description of cerebellar inter-spike interval distributions which deviate from the usual Poisson assumptions. Our results suggest the existence of an intrinsic rhythmicity in cells exhibiting arrhythmic spike trains in cerebellum, and may identify an important source of variability in neuronal firing patterns that is relevant to the mechanism of neural computation in cerebellum.
Actions and their associated consequences, such as reward attainment, are often temporally distant. Animals nevertheless learn such associations thereby solving the 'distal reward' problem. We sought ...to determine whether dopamine signaling plays a role in such learning. Wild-type and dopamine type I receptor knockout mice executed three left/right choices leading to one of eight differentially rewarded goal sites. Compared with wild-type mice, knockouts exhibited selective impairments in decision making at choice points distal, but not proximal, to goal sites. We conclude that dopamine's role in reinforcement learning depends on the temporal relationship of actions to reward and that dopamine signaling through D1 receptors constitutes a component of those brain mechanisms responsible for solving the distal reward problem.
We describe Darwin X, a physical device that interacts with a real environment, whose behavior is guided by a simulated nervous system incorporating aspects of the detailed anatomy and physiology of ...the hippocampus and its surrounding regions. This brain-based device integrates cues from its environment and solves a spatial memory task. The responses of simulated neuronal units in the hippocampal areas during its exploratory behavior are comparable to place cells in the rodent hippocampus and emerged by associating sensory cues during exploration. To identify different functional hippocampal pathways and their influence on behavior, we employed a time series analysis that distinguishes causal interactions within and between simulated hippocampal and neocortical regions while the device is engaged in a spatial memory task. Our analysis identified different functional pathways within the neural simulation and prompts novel predictions about the influence of the perforant path, the trisynaptic loop and hippocampal-cortical interactions on place cell activity and behavior during navigation. Moreover, this causal time series analysis may be useful in analyzing networks in general.
In rodent navigational studies, spatial responses have been identified in both the hippocampal subregion CA1 and the subiculum (SUB), but these two brain regions appear to encode spatial features ...differently. Place fields of SUB place cells are larger and less specific than CA1. Additionally, SUB neurons exhibit stronger directional modulation for heading and axes of travel. Based on neural and behavioral data recorded as rats perform a navigational task on a "triple-T" maze, we present a spiking neural network modeling framework to replicate response properties observed in the CA1 and SUB. The parameters of Spike Timing Dependent Plasticity and homeostatic scaling (STDP-H) were evolved such that the response of the two different SNNs resembled recordings from CA1 and SUB when rats traversed the triple-T maze. Our results suggest that positional input may be more influential in forming CA1 place cells, while the SUB appears to integrate both allocentric positional information and self-motion cues to encode "kinds of places". Furthermore, our results predict that the different spatial responses in these regions may be due in part to different STDP-H learning parameters. The framework presented here could be used as an automated parameter tuning system for replicating responses in other brain regions.
We propose a new method of studying the correlation between neuronal spike trains. This technique is based on the analysis of relative phase between two point processes. Relative phase here is ...defined as the relative timing difference between two spike trains normalized by the associated interspike interval of one cell. This phase measurement is intended to reveal the relative timing relationship between spike trains at different firing rates. We apply this method to a numerical example and an example from two cerebellar neuronal spike trains of a behaving rat. The results are compared with classical cross-correlation analysis. We show that the technique can avoid some of the limitations of cross-correlation methods, reveal certain statistical dependencies that cannot be shown by cross correlation, and provide information as to the direction of influence between two spike trains.
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