Attention in a Bayesian Framework

The behavioral phenomena of sensory attention are thought to reflect the allocation of a limited processing resource, but there is little consensus on the nature of the resource or why it should be limited. Here we argue that a fundamental bottleneck emerges naturally within Bayesian models of perception, and use this observation to frame a new computational account of the need for, and action of, attention – unifying diverse attentional phenomena in a way that goes beyond previous inferential, probabilistic and Bayesian models. Attentional effects are most evident in cluttered environments, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental settings, where cues shape expectations about a small number of upcoming stimuli and thus convey “prior” information about clearly defined objects. While operationally consistent with the experiments it seeks to describe, this view of attention as prior seems to miss many essential elements of both its selective and integrative roles, and thus cannot be easily extended to complex environments. We suggest that the resource bottleneck stems from the computational intractability of exact perceptual inference in complex settings, and that attention reflects an evolved mechanism for approximate inference which can be shaped to refine the local accuracy of perception. We show that this approach extends the simple picture of attention as prior, so as to provide a unified and computationally driven account of both selective and integrative attentional phenomena

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Spike-based local synaptic plasticity: A survey of computational models and neuromorphic circuits

Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if and how they could be mapped onto neuromorphic hardware, and to compare their features and ease of implementation. To this end, in this survey, we provide a comprehensive overview of representative brain-inspired synaptic plasticity models and mixed-signal CMOS neuromorphic circuits within a unified framework. We review historical, bottom-up, and top-down approaches to modeling synaptic plasticity, and we identify computational primitives that can support low-latency and low-power hardware implementations of spike-based learning rules. We provide a common definition of a locality principle based on pre- and post-synaptic neuron information, which we propose as a fundamental requirement for physical implementations of synaptic plasticity. Based on this principle, we compare the properties of these models within the same framework, and describe the mixed-signal electronic circuits that implement their computing primitives, pointing out how these building blocks enable efficient on-chip and online learning in neuromorphic processing systems

Neocortical Layer 4 to Layer 2/3 Sensory Information Processing Investigated with Digital-Light-Projection Neuronal Photostimulation

The mammalian brain forms neuronal networks and microcircuits with cell-type- and anatomical-specific synaptic connections. Despite great advances in elucidating the cellular physiology of the nervous system, little is known about the computational processes occurring at the level of neuronal microcircuits. Much success has been reported in describing the synaptic input patterns of many brain regions and cell types using photostimulation systems; however, these systems are severely limited in their ability to study the integration of synaptic input from multiple synchronous or temporally correlated presynaptic locations. Here we describe a system that allows the generation of arbitrary 2-D stimulus patterns with thousands of independently controlled sites to manipulate the activity of populations of neurons with high spatial and temporal precision. The PC-controlled Digital-Light-Processing (DLP) based system updates the 780,000 parallel photostimulation beams, or pixels, at a maximum rate of 13 kHz. With the currently used projection objective, the pixel sizes at the plane of focus are 7.3 µm2 . The high-power UV laser source used in this system provides a light flux density sufficient for bins of 8x8 pixels (21.6 µm x 21.6 µm) with dwell times as low 3 ms to reliably induce action potentials in 2.5 mM MNI-caged glutamate. At these settings the effective diameter of a glutamate uncaging site is \u3c 86 µm, which is equivalent to most other UV photostimulation rigs. With DLP photostimulation, sub-threshold responses and action potentials can be synchronously induced at thousands of sites over a 2.76 mm x 2.07 mm area, a capability unmatched by any other current system. This DLP-based system has the unique capability to investigate normal and diseased circuit properties by investigating neuronal responses to spatiotemporally complex activity patterns. This technique was used to investigate the temporal integration of synaptic input in the whisker barrel cortex of mice. The neocortex is organized into layers, with neuronal networks and circuits formed by layer-specific connections. While the anatomical organization of these circuits has been well characterized, the information processing and coding performed by these ensembles is poorly understood. A key component of this investigation concerns the transmission and transformation of the neuronal representation from one neuronal pool to the next. In the rodent somatosensory barrel cortex, histologically-distinguishable “barrels” in layer 4 (L4) receive principal input from a single whisker. L4 projects to layer II/III (L2/3), where the circuit diverges to multiple postsynaptic targets. Using the DLP-photostimulation system, we modulated the synchronicity of action potentials in L4 cells while recording from L2/3 in an acute slice preparation. This data shows that synchronous activity in L4 neurons is highly effective at eliciting strong spiking responses in L2/3 pyramidal cells, while asynchronous L4 activity fails to drive L2/3 to action-potential threshold. Pharmacological manipulation of the slice-bathing solution has suggested that this phenomenon is AMPA-receptor dependent and modulated by NMDA receptor activity. Intracellular pharmacological manipulations suggest that postsynaptic conductances also play a role in the nonlinear L2/3 synaptic integration of L4 activity