Orbán G., Lengyel M. & Fiser J. (2011) Statistically optimal effects of uncertainty in scene segmentation on human learning. COSYNE 2011, Nature Precedings (2011) [Abstract]
High-throughput neuroscience presents unique challenges for exploratory data analysis. Clustering often helps experimenters make sense of data, but model-based clustering techniques, including Dirichlet-process mixture models, have difficulty when differing subsets of dimensions are best explained by differing clusterings. As a result, they can be misled by irrelevant dimensions, they easily miss structure that dimensionality reduction […]
Fiser J., Orbán G. & Lengyel M. (2008) Linking implicit chunk learning and the capacity of working memory. VSS 2008, Journal of Vision 8 (6), 213-213 [Abstract]
Classical studies of the capacity of working memory have posited a fix limit for the maximum number of items human can store temporarily in their memory, such as 7-2 or 4-1. More recent results showed that when the stored items are viewed as complex multi-dimensional objects capacity can be increased and conversely, when distinctiveness of […]
Zhao J., Szirtes G., Eisele M., Fiser J., Chiu C., Weliky M. & Miller KD. (2006) Analysis of spontaneous and sensory-driven activity in ferret V1. SFN 2006, Atlanta, GA [Abstract]
We analyze multiunit recordings from linear arrays of 16 electrodes spanning 3 or 9 mm in awake ferret V1, as in Fiser et al. Nature 431:573 (2004). Recordings were made at ages ranging from 29 to 168 days postnatal. Fiser et al. 2004 found that activity from P30 to P90 was dominated by similar activity […]
Haefner RM., Berkes P. & Fiser J. (2012) The relation of decision-making and endogenous covert attention to sampling-based neural representations. VSS 2012, Journal of Vision 12 (9), 159-159 [Abstract]
Empirical evidence suggests that the brain during perception and decision-making has access to both point estimates of any external stimulus and to the certainty about this estimate. This requires a neural representation of entire probability distributions in the brain. Two alternatives for neural codes supporting such representations are probabilistic population codes (PPC) and sampling-based representations […]
Berkes P., White BL. & Fiser J. (2010) Sparseness is not actively optimized in V1. COSYNE 2010, Salt Lake City, UT [Abstract]
Sparse coding is a powerful idea in computational neuroscience referring to the general principle that the cortex exploits the benefits of representing every stimulus by a small subset of neurons. Advantages of sparse coding include reduced dependencies, improved detection of co-activation of neurons, and a more efficient encoding of visual information. Computational models based on […]
Berkes P., Wood F. & Pillow J. (2008) Modeling neural dependencies with Poisson copulas. Bernstein Symposium 2008, Munich, Germany [Abstract]
The coding of information by neural populations depends critically on the statistical dependencies between neuronal responses. At the moment, however, we lack of a simple model that can simultaneously account for (1) marginal distributions over single-neuron spike counts that are typically close to Poisson; and (2) joint distributions over the responses of multiple neurons that […]
Fiser J., Bourjaily M., Chiu C. & Weliky M. (2006) Distinct states of firing patterns in the primary visual cortex of awake ferrets. Sloan-Swartz Meeting of Theoretical Neurobiology 2006, Columbia University, USA [Abstract]
PRESENTATION
Bernacchia A., Fiser J., Hennequin G. & Lengyel M. (2019) Adaptive erasure of spurious sequences in cortical circuits. COSYNE 2019, Lisbon, Portugal [Abstract]
The sequential activation of neurons, reflecting a previously experienced temporal sequence of stimuli, is be- lieved to be a hallmark of learning across cortical areas1, including the primary visual cortex2,3 (V1). While circuit mechanisms of sequence learning have been studied extensively4,5, the converse problem, that is equally important for robust performance, has so far received […]
Bernacchia A., Fiser J., Hennequin G. & Lengyel M. (2017) Dale’s principle preserves sequentiality in neural circuits. COSYNE 2017, Salt Lake City, UT [Abstract]
Cortical circuits obey Dale’s principle: each neuron either excites or inhibits all its postsynaptic targets. There is no known principled justification for why this must be so; in fact, Dale’s principle is considered – if at all – a mere constraint in neural network models. Here we provide a novel rationale for Dale’s principle: networks […]
Christensen JH., Bex PJ. & Fiser J. (2015) Prior implicit knowledge shapes human threshold for orientation noise. VSS 2015, Journal of vision 15 (9), 24-24 [Abstract]
Although orientation coding in the human visual system has been researched with simple stimuli, little is known about how orientation information is represented while viewing complex images. We show that, similar to findings with simple Gabor textures, the visual system involuntarily discounts orientation noise in a wide range of natural images, and that this discounting […]