\n\n\u0003\n\u0003\n\n\u001f\n\n\b\n\n\u0012\n\u0016\n\n\u001f\n\u0012\n\u0016\n\u0003\n\u0003\n\u0003\n\fi\n\nr\ne\nw\no\np\n \ne\nv\ni\nt\nc\nd\ne\nr\np\n \nD\nR\nD\nS\nA\n \nd\ne\nz\n\n/\n\ni\nl\n\na\nm\nr\no\nn\n\n0.6\n\n0.4\n\n0.2\n\n0\n\n0.08\n\n0.06\n\n0.04\n\n0.02\n\n0\n\n\u22120.02\n\n0.08\n\n0.06\n\n0.04\n\n0.02\n\n0\n\n\u22120.02\n\n0\n\n0.6\nnormalized ASD predictive power\n\n0.4\n\n0.2\n\n\u22120.04\n\n0\n\n50\nnormalized noise power\n\n25\n\n\u22120.04\n\n10\n\n0\n20\nno. of recordings\n\ne\nc\nn\ne\nr\ne\nf\nf\ni\nd\n \nn\no\ni\nt\nc\nd\ne\nr\np\n \nd\ne\nz\n\ni\n\ni\nl\n\na\nm\nr\no\nn\n\n)\n\n \n\n \n\nD\nS\nA\n\u2212\nD\nR\nD\nS\nA\n\n/\n\n(\n\nFigure 4: Comparison of ARD in the ASD basis and simple ASD\n\nin this basis will be formed by a superposition of Gaussian components, each of which\nindividually matches the ASD prior on its covariance.\n\nThe results of this procedure (labelled ASD/RD) on our example recording are shown in\nthe rightmost panel of \ufb01gure 1. The combined prior shows a similar degree of smoothing\nto the ASD-optimized prior alone; in addition, like the ARD prior, it suppresses the appar-\nent background estimation noise at higher frequencies and longer time lags. Predictions\nmade with this estimate are yet more accurate, capturing 30% of the signal power. This\nimprovement over estimates derived from ASD alone is borne out in the whole population\n(\ufb01gure 4), although the gain is smaller than in the previous cases.\n\n7 Conclusions\n\nWe have demonstrated a succession of evidence-optimization techniques which appear to\nimprove the accuracy of STRF estimates from noisy data. The mean improvement in pre-\ndiction of the ASD/RD method over the Wiener kernel is 40% of the stimulus-related signal\npower. Considering that the best linear predictor would on average capture no more than\n40% of the signal power in these data even in the absence of noise (Sahani and Linden,\n\u201cHow Linear are Auditory Cortical Responses?\u201d, this volume), this is a dramatic improve-\nment. These results apply to the case of linear models; our current work is directed toward\nextensions to non-linear SRFs within an augmented linear regression framework.\n\nReferences\n\n[1] Marmarelis, P. Z & Marmarelis, V. Z. (1978) Analysis of Physiological Systems. (Plenum Press,\n\nNew York).\n\n[2] Lewicki, M. S. (1994) Neural Comp 6, 1005\u20131030.\n[3] Sahani, M. (1999) Ph.D. thesis (California Institute of Technology, Pasadena, CA).\n[4] deCharms, R. C, Blake, D. T, & Merzenich, M. M. (1998) Science 280, 1439\u20131443.\n[5] MacKay, D. J. C. (1994) ASHRAE Transactions 100, 1053\u20131062.\n[6] Tipping, M. E. (2001) J Machine Learning Res 1, 211\u2013244.\n\n\f", "award": [], "sourceid": 2294, "authors": [{"given_name": "Maneesh", "family_name": "Sahani", "institution": null}, {"given_name": "Jennifer", "family_name": "Linden", "institution": null}]}