Synaptic Self-Organization of Spatio-Temporal Pattern Selectivity

Spiking model neurons can be set up to respond selectively to specific spatio-temporal spike patterns by optimization of their input weights. It is unknown, however, if existing synaptic plasticity mechanisms can achieve this temporal mode of neuronal coding and computation. Here it is shown that changes of synaptic efficacies which tend to balance excitatory and inhibitory synaptic inputs can make neurons sensitive to particular input spike patterns. Simulations demonstrate that a combination of Hebbian mechanisms, hetero-synaptic plasticity and synaptic scaling is sufficient for self-organizing sensitivity for spatio-temporal spike patterns that repeat in the input. In networks inclusion of hetero-synaptic plasticity leads to specialization and faithful representation of pattern sequences by a group of target neurons. Pattern detection is found to be robust against a range of distortions and noise. Furthermore, the resulting balance of excitatory and inhibitory inputs protects the memory for a specific pattern from being overwritten during ongoing learning when the pattern is not present. These results not only provide an explanation for experimental observations of balanced excitation and inhibition in cortex but also promote the plausibility of precise temporal coding in the brain.

Generating surrogates for significance estimation of spatio-temporal spike patterns

Spatio-temporal spike patterns were suggested as indications of active cell assemblies. We developed the SPADE method to detect significant spatio-temporal patterns (STPs) with ms accuracy. STPs are defined as identically repeating spike patterns across neurons with temporal delays between the spikes. The significance of STPs is derived by comparison to the null-hypothesis of independence implemented by surrogate data. SPADE binarizes the spike trains and examines the data for STPs by counting repeated patterns using frequent itemset mining. The significance of STPs is evaluated by comparison to pattern counts derived from surrogate data, i.e., modifications of the original data with destroyed potential spike correlations but under conservation of the firing rate profiles. To avoid erroneous results, surrogate data are required to retain the statistical properties of the original data as much as possible. A classically chosen surrogate technique is Uniform Dithering (UD), which displaces each spike independently according to a uniform distribution. We find that binarized UD surrogates of our experimental data (motor cortex) contain fewer spikes than the binarized original data. As a consequence, false positives occur. Here, we identify the reason for the spike reduction, which is the lack of conservation of short ISIs. To overcome this problem, we study five alternative surrogate techniques and examine their statistical properties such as spike loss, ISI characteristics, effective movement of spikes, and arising false positives when applied to different ground truth data sets: first, on stationary point process models, and then on non-stationary point processes mimicking statistical properties of experimental data. We conclude that trial-shifting best preserves the features of the original data and has a low expected false-positive rate. Finally, the analysis of the experimental data provides consistent STPs across the alternative surrogates.

Neural burst codes disguised as rate codes

The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

Cell assembly formation and structure in a piriform cortex model

The piriform cortex is rich in recurrent excitatory synaptic connections between pyramidal neurons. We asked how such connections could shape cortical responses to olfactory lateral olfactory tract (LOT) inputs. For this, we constructed a computational network model of anterior piriform cortex with 2000 multicompartment, multiconductance neurons (500 semilunar, 1000 layer 2 and 500 layer 3 pyramids; 200 superficial interneurons of two types; 500 deep interneurons of three types; 500 LOT afferents), incorporating published and unpublished data. With a given distribution of LOT firing patterns, and increasing the strength of recurrent excitation, a small number of firing patterns were observed in pyramidal cell networks: first, sparse firings; then temporally and spatially concentrated epochs of action potentials, wherein each neuron fires one or two spikes; then more synchronized events, associated with bursts of action potentials in some pyramidal neurons. We suggest that one function of anterior piriform cortex is to transform ongoing streams of input spikes into temporally focused spike patterns, called here “cell assemblies”, that are salient for downstream projection areas.

Mapping circuit dynamics during function and dysfunction

Neural circuits can generate many spike patterns, but only some are functional. The study of how circuits generate and maintain functional dynamics is hindered by a poverty of description of circuit dynamics across functional and dysfunctional states. For example, although the regular oscillation of a central pattern generator is well characterized by its frequency and the phase relationships between its neurons, these metrics are ineffective descriptors of the irregular and aperiodic dynamics that circuits can generate under perturbation or in disease states. By recording the circuit dynamics of the well-studied pyloric circuit in C. borealis, we used statistical features of spike times from neurons in the circuit to visualize the spike patterns generated by this circuit under a variety of conditions. This unsupervised approach captures both the variability of functional rhythms and the diversity of atypical dynamics in a single map. Clusters in the map identify qualitatively different spike patterns hinting at different dynamical states in the circuit. State probability and the statistics of the transitions between states varied with environmental perturbations, removal of descending neuromodulation, and the addition of exogenous neuromodulators. This analysis reveals strong mechanistically interpretable links between complex changes in the collective behavior of a neural circuit and specific experimental manipulations, and can constrain hypotheses of how circuits generate functional dynamics despite variability in circuit architecture and environmental perturbations.

Inferring spikes from calcium imaging in dopamine neurons

Calcium imaging has led to discoveries about neural correlates of behavior in subcortical neurons, including dopamine (DA) neurons. However, spike inference methods have not been tested in most populations of subcortical neurons. To address this gap, we simultaneously performed calcium imaging and electrophysiology in DA neurons in brain slices and applied a recently developed spike inference algorithm to the GCaMP fluorescence. This revealed that individual spikes can be inferred accurately in this population. Next, we inferred spikes in vivo from calcium imaging from these neurons during Pavlovian conditioning, as well as during navigation in virtual reality. In both cases, we quantitatively recapitulated previous in vivo electrophysiological observations. Our work provides a validated approach to infer spikes from calcium imaging in DA neurons and implies that aspects of both tonic and phasic spike patterns can be recovered.

Temporal stimulus segmentation by reinforcement learning in populations of spiking neurons

Learning to detect, identify or select stimuli is an essential requirement of many behavioral tasks. In real life situations, relevant and non-relevant stimuli are often embedded in a continuous sensory stream, presumably represented by different segments of neural activity. Here, we introduce a neural circuit model that can learn to identify action-relevant stimuli embedded in a spatio-temporal stream of spike trains, while learning to ignore stimuli that are not behaviorally relevant. The model uses a biologically plausible plasticity rule and learns from the reinforcement of correct decisions taken at the right time. Learning is fully online; it is successful for a wide spectrum of stimulus-encoding strategies; it scales well with population size; and can segment cortical spike patterns recorded from behaving animals. Altogether, these results provide a biologically plausible theory of reinforcement learning in the absence of prior information on the relevance and timing of input stimuli.