Showing papers by "Greg S. Corrado published in 2010"

Stimulus onset quenches neural variability: a widespread cortical phenomenon

Abstract: Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.

Integrate and fire electronic neurons

Abstract: An integrate and fire electronic neuron is disclosed. Upon receiving an external spike signal, a digital membrane potential of the electronic neuron is updated based on the external spike signal. The electric potential of the membrane is decayed based on a leak rate. Upon the electric potential of the membrane exceeding a threshold, a spike signal is generated.

Sparse Spatiotemporal Coding for Activity Recognition

Abstract: We present a new approach to learning sparse, spatiotemporal features and demonstrate the utility of the approach by applying the resulting sparse codes to the problem of activity recognition. Learning features that discriminate among human activities in video is difficult in part because the stable space-time events that reliably characterize the relevant motions are rare. To overcome this problem, we adopt a multi-stage approach to activity recognition. In the initial preprocessing stage, we first whiten and apply local contrast normalization to each frame of the video. We then apply an additional set of filters to identify and extract salient space-time volumes that exhibit smooth periodic motion. We collect a large corpus of these space-time volumes as training data for the unsupervised learning of a sparse, over-complete basis using a variant of the two-phase analysis-synthesis algorithm of Olshausen and Field [1997]. We treat the synthesis phase, which consists of reconstructing the input as a sparse — mostly zero coefficient – linear combination of basis vectors, as an L1-regularized least-squares problem. We found most existing algorithms for solving the L1-regularized least-squares problem to be slow and perform poorly at the task of learning codes that are spatially oriented and temporally diverse in terms of transformations and velocities. To reduce the time required in learning — and most importantly the time required for reconstruction in subsequent production use — we adapted existing algorithms to exploit potential parallelism through the use of readily-available SIMD hardware. To obtain better codes, we developed a new approach to learning sparse, spatiotemporal codes in which the number of basis vectors, their orientations, velocities and the size of their receptive fields change over the duration of unsupervised training. The algorithm starts with a relatively small, initial basis with minimal temporal extent. This initial basis is obtained through conventional sparse coding techniques and is expanded over time by recursively constructing a new basis consisting of basis vectors with larger temporal extent that proportionally conserve regions of previously trained weights. These proportionally conserved weights are combined with the result of adjusting newly added weights to represent a greater range of primitive motion features. The size of the current basis is determined probabilistically by sampling from existing basis vectors according to their activation on the training set. The resulting algorithm produces bases consisting of filters that are bandpass, spatially oriented and temporally diverse in terms of their transformations and velocities. We demonstrate the utility of our approach by using it to recognize human activity in video.