- Poster presentation
- Open Access
Sparse coding and dictionary learning for spike trains to find spatio-temporal patterns
BMC Neuroscience volume 16, Article number: P255 (2015)
In biological neural networks, it is widely accepted that the spikes are the fundamental building blocks of information representation . In contrast, whether such building blocks exist at a higher level in terms of time and in a population of neurons is a topic of ongoing debate. One approach for finding candidates for such building blocks is to seek for frequently appearing spike patterns in a population. These sequences are often called spatio-temporal patterns, cell assemblies, or unitary events [2–4]. They could metaphorically be considered as an ``alphabet'' of neural information processing [5, 6]. Some patterns have already been found and are related to functional roles such as memory consolidation and gating of sensory inputs [7, 8].
One difficulty in finding spatio-temporal patterns arises from observed spike trains being a superposition of multiple patterns. In signal processing, one commonly used method for decomposing the signal into patterns is dictionary learning for sparse coding [9–11]. Sparse coding expresses the input signal as a linear combination of a few template vectors taken from a matrix called a dictionary or codebook. In terms of linear algebra, sparse coding corresponds to finding a sparse vector x, which fulfills y = Dx, where y is the observed signal vector and D is a dictionary. When the dimension of × is much larger than that of y, it is possible to find sparse x. Each column of D is called an atom, which represents a template vector. A good dictionary decomposes the most of the observed signals into a small set of template vectors. In other words, D must sparsify not just one input vector y but many others as well. This is represented by using matrix Y whose column vectors are observed signals. In this case, sparse coding is represented by equation Y = DX. The goal is to find sparse matrix × given Y and D. Whether input matrix Y can be transformed into sparse × or not depends on dictionary D. The goodness of D depends on Y. The task of finding optimal D given Y is called dictionary learning. In this work sparse coding and dictionary learning were applied for finding spatio-temporal patterns from multivariate spike trains. Spike trains were transformed to vectors using binning, that is, converted to vectors of short-time firing rates. The methods were tested using different bin sizes. The results obtained for biological data showed possible candidates of spatio-temporal patterns in neural activity.
Gerstner W, Kistler WM, Naud R, Paninski L: Neuronal Dynamics. 2014, Cambridge: Cambridge University Press
Villa AEP: Empirical evidence about temporal structure in multi-unit recordings. Conceptual Advances in Brain Research. 2000, 3: 1-51.
Buzsaki G: Neural syntax: cell assemblies, synapsembles, and readers. Neuron. 2010, 68 (3): 362-385.
Grun S, Diesmann M, Aertsen A: Unitary events in multiple single-neuron spiking activity. I. Detection and significance. Neural Computation. 2002, 14: 43-80.
Baram Y: Global attractor alphabet of neural firing modes. Journal of Neurophysiology. 2013, 110: 907-915.
Eyherabide HG, Samengo L: The information transmitted by spike patterns in single neurons. Journal of Physiology Paris. 2010, 104: 147-155.
Nadasdy Z, Hirase H, Czurko A, Csicsvari J, Buzsaki G: Replay and time compression of recurring spike sequences in the hippocampus. Journal of Neuroscience. 1999, 19 (21): 9497-9507.
Luczak A, Bartho P, Harris KD: Gating of sensory input by spontaneous cortical activity. Journal of Neuroscience. 2013, 33 (4): 1684-1695.
Hoyer PO: Non-negative matrix factorization with sparseness constraints. Journal of Machine Learning Research. 2004, 5: 1457-1469.
Bruckstein A, Donoho DL, Elad M: From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Review. 2009, 51 (1): 34-81.
Elad M: Sparse and Redundant Representations. 2010, Berlin: Springer
This work was supported in part by JSPS KAKENHI Grant Numbers 21700121, 25280110, and 25540159.
About this article
Cite this article
Tezuka, T. Sparse coding and dictionary learning for spike trains to find spatio-temporal patterns. BMC Neurosci 16, P255 (2015). https://0-doi-org.brum.beds.ac.uk/10.1186/1471-2202-16-S1-P255
- Building Block
- Firing Rate
- Spike Train
- Sparse Matrix
- Memory Consolidation