A comment on "A fast L_p spike alignment metric" by A. J. Dubbs, B. A. Seiler and M. O. Magnasco [arXiv:0907.3137]

Measuring the transmitted information in metric-based clustering has become something of a standard test for the performance of a spike train metric. In this comment, the recently proposed L_p Victor-Purpura metric is used to cluster spiking responses to zebra finch songs, recorded from field L of anesthetized zebra finch. It is found that for these data the L_p metrics with p>1 modestly outperform the standard, p=1, Victor-Purpura metric. It is argued that this is because for larger values of p, the metric comes closer to performing windowed coincidence detection.Comment: 9 pages, 3 figures included as late

Which spike train distance is most suitable for distinguishing rate and temporal coding?

Background: It is commonly assumed in neuronal coding that repeated presentations of a stimulus to a coding neuron elicit similar responses. One common way to assess similarity are spike train distances. These can be divided into spike-resolved, such as the Victor-Purpura and the van Rossum distance, and time-resolved, e.g. the ISI-, the SPIKE- and the RI-SPIKE-distance. New Method: We use independent steady-rate Poisson processes as surrogates for spike trains with fixed rate and no timing information to address two basic questions: How does the sensitivity of the different spike train distances to temporal coding depend on the rates of the two processes and how do the distances deal with very low rates? Results: Spike-resolved distances always contain rate information even for parameters indicating time coding. This is an issue for reasonably high rates but beneficial for very low rates. In contrast, the operational range for detecting time coding of time-resolved distances is superior at normal rates, but these measures produce artefacts at very low rates. The RI-SPIKE-distance is the only measure that is sensitive to timing information only. Comparison with Existing Methods: While our results on rate-dependent expectation values for the spike-resolved distances agree with \citet{Chicharro11}, we here go one step further and specifically investigate applicability for very low rates. Conclusions: The most appropriate measure depends on the rates of the data being analysed. Accordingly, we summarize our results in one table that allows an easy selection of the preferred measure for any kind of data.Comment: 14 pages, 6 Figures, 1 Tabl

Neural mechanisms for sparse, informative and background-invariant coding of vocalizations

To efficiently process natural environments, many species have sensory systems that selectively encode behaviorally relevant information. Vocal communicators such as humans and songbirds rely on their auditory systems to recognize vocalizations and to extract vocalizations from complex auditory scenes. Yet many of the neural correlates of these perceptual abilities remain poorly understood. In this dissertation, I describe neural mechanisms by which the songbird auditory system produces sparse, informative and background-invariant neural representations of vocalizations. First, I show that auditory midbrain neurons encode vocalizations differently than other complex sounds, and that subthreshold excitation and inhibition may facilitate stimulus-dependent encoding of vocalizations. Second, I show that the responses of individual midbrain neurons can be unreliable, and that pooling the responses of correlated and similarly tuned neurons facilitates the neural discrimination of vocalizations. Third, I show that sparse coding neurons in the songbird forebrain extract individual vocalizations from auditory scenes at signal-to-noise ratios that match behavior. Lastly, I show that a simple neural circuit of delayed inhibition transforms a dense and background-sensitive neural representation into a sparse and background-invariant representation, in as little as one synapse. Together, these findings illuminate previously unknown mechanisms for selective vocalization coding, suggest a behaviorally relevant role for the ubiquitous phenomenon of sparse neural coding, and provide a neural correlate for the perceptual extraction of vocalizations from complex auditory scenes

Learning spatio-temporal spike train encodings with ReSuMe, DelReSuMe, and Reward-modulated Spike-timing Dependent Plasticity in Spiking Neural Networks

SNNs are referred to as the third generation of ANNs. Inspired from biological observations and recent advances in neuroscience, proposed methods increase the power of SNNs. Today, the main challenge is to discover efficient plasticity rules for SNNs. Our research aims are to explore/extend computational models of plasticity. We make various achievements using ReSuMe, DelReSuMe, and R-STDP based on the fundamental plasticity of STDP. The information in SNNs is encoded in the patterns of firing activities. For biological plausibility, it is necessary to use multi-spike learning instead of single-spike. Therefore, we focus on encoding inputs/outputs using multiple spikes. ReSuMe is capable of generating desired patterns with multiple spikes. The trained neuron in ReSuMe can fire at desired times in response to spatio-temporal inputs. We propose alternative architecture for ReSuMe dealing with heterogeneous synapses. It is demonstrated that the proposed topology exactly mimic the ReSuMe. A novel extension of ReSuMe, called DelReSuMe, has better accuracy using less iteration by using multi-delay plasticity in addition to weight learning under noiseless and noisy conditions. The proposed heterogeneous topology is also used for DelReSuMe. Another plasticity extension based on STDP takes into account reward to modulate synaptic strength named R-STDP. We use dopamine-inspired STDP in SNNs to demonstrate improvements in mapping spatio-temporal patterns of spike trains with the multi-delay mechanism versus single connection. From the viewpoint of Machine Learning, Reinforcement Learning is outlined through a maze task in order to investigate the mechanisms of reward and eligibility trace which are the fundamental in R-STDP. To develop the approach we implement Temporal-Difference learning and novel knowledge-based RL techniques on the maze task. We develop rule extractions which are combined with RL and wall follower algorithms. We demonstrate the improvements on the exploration efficiency of TD learning for maze navigation tasks

Correlations and functional connections in a population of grid cells

We study the statistics of spike trains of simultaneously recorded grid cells in freely behaving rats. We evaluate pairwise correlations between these cells and, using a generalized linear model (kinetic Ising model), study their functional connectivity. Even when we account for the covariations in firing rates due to overlapping fields, both the pairwise correlations and functional connections decay as a function of the shortest distance between the vertices of the spatial firing pattern of pairs of grid cells, i.e. their phase difference. The functional connectivity takes positive values between cells with nearby phases and approaches zero or negative values for larger phase differences. We also find similar results when, in addition to correlations due to overlapping fields, we account for correlations due to theta oscillations and head directional inputs. The inferred connections between neurons can be both negative and positive regardless of whether the cells share common spatial firing characteristics, that is, whether they belong to the same modules, or not. The mean strength of these inferred connections is close to zero, but the strongest inferred connections are found between cells of the same module. Taken together, our results suggest that grid cells in the same module do indeed form a local network of interconnected neurons with a functional connectivity that supports a role for attractor dynamics in the generation of the grid pattern.Comment: Accepted for publication in PLoS Computational Biolog

Balancing Feed-Forward Excitation and Inhibition via Hebbian Inhibitory Synaptic Plasticity

It has been suggested that excitatory and inhibitory inputs to cortical cells are balanced, and that this balance is important for the highly irregular firing observed in the cortex. There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates ‘negative feedback’ that balances excitation and inhibition, which contrasts with the ‘positive feedback’ of excitatory Hebbian synaptic plasticity. As a result, this balance may increase the sensitivity of the learning dynamics to the correlation structure of the excitatory inputs