https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.

/pdf/eigenfaces-for-recognition-1vpm5kz4jh.pdf

Eigenfaces for recognition

We review evidence for partially segregated networks of brain areas that carry out different attentional functions. One system, which includes parts of the intraparietal cortex and superior frontal cortex, is involved in preparing and applying goal-directed (top-down) selection for stimuli and responses. This system is also modulated by the detection of stimuli. The other system, which includes the temporoparietal cortex and inferior frontal cortex, and is largely lateralized to the right hemisphere, is not involved in top-down selection. Instead, this system is specialized for the detection of behaviourally relevant stimuli, particularly when they are salient or unexpected. This ventral frontoparietal network works as a 'circuit breaker' for the dorsal system, directing attention to salient events. Both attentional systems interact during normal vision, and both are disrupted in unilateral spatial neglect.

Control of goal-directed and stimulus-driven attention in the brain

▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

/pdf/an-integrative-theory-of-prefrontal-cortex-function-2j9unprn5f.pdf

An integrative theory of prefrontal cortex function

Publisher Summary This chapter focuses on the modern notion of short-term memory, called working memory. Working memory refers to the temporary maintenance of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be maintained for longer periods of time through active rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behavior. Working memory is a system that is critically important in cognition and seems necessary in the course of performing many other cognitive functions, such as reasoning, language comprehension, planning, and spatial processing. This chapter demonstrates the functional importance of dopamine to working memory function in several ways. Elucidation of the cognitive and neural mechanisms underlying human working memory is an important focus of cognitive neuroscience and neurology for much of the past decade. One conclusion that arises from research is that working memory, a faculty that enables temporary storage and manipulation of information in the service of behavioral goals, can be viewed as neither a unitary, nor a dedicated system. Data from numerous neuropsychological and neurophysiological studies in animals and humans demonstrates that a network of brain regions, including the prefrontal cortex, is critical for the active maintenance of internal representations.

Chapter 11 Working memory

1. An oculomotor delayed-response task was used to examine the spatial memory functions of neurons in primate prefrontal cortex. Monkeys were trained to fixate a central spot during a brief presentation (0.5 s) of a peripheral cue and throughout a subsequent delay period (1-6 s), and then, upon the extinction of the fixation target, to make a saccadic eye movement to where the cue had been presented. Cues were usually presented in one of eight different locations separated by 45 degrees. This task thus requires monkeys to direct their gaze to the location of a remembered visual cue, controls the retinal coordinates of the visual cues, controls the monkey's oculomotor behavior during the delay period, and also allows precise measurement of the timing and direction of the relevant behavioral responses. 2. Recordings were obtained from 288 neurons in the prefrontal cortex within and surrounding the principal sulcus (PS) while monkeys performed this task. An additional 31 neurons in the frontal eye fields (FEF) region within and near the anterior bank of the arcuate sulcus were also studied. 3. Of the 288 PS neurons, 170 exhibited task-related activity during at least one phase of this task and, of these, 87 showed significant excitation or inhibition of activity during the delay period relative to activity during the intertrial interval. 4. Delay period activity was classified as directional for 79% of these 87 neurons in that significant responses only occurred following cues located over a certain range of visual field directions and were weak or absent for other cue directions. The remaining 21% were omnidirectional, i.e., showed comparable delay period activity for all visual field locations tested. Directional preferences, or lack thereof, were maintained across different delay intervals (1-6 s). 5. For 50 of the 87 PS neurons, activity during the delay period was significantly elevated above the neuron's spontaneous rate for at least one cue location; for the remaining 37 neurons only inhibitory delay period activity was seen. Nearly all (92%) neurons with excitatory delay period activity were directional and few (8%) were omnidirectional. Most (62%) neurons with purely inhibitory delay period activity were directional, but a substantial minority (38%) was omnidirectional. 6. Fifteen of the neurons with excitatory directional delay period activity also had significant inhibitory delay period activity for other cue directions. These inhibitory responses were usually strongest for, or centered about, cue directions roughly opposite those optimal for excitatory responses.(ABSTRACT TRUNCATED AT 400 WORDS)

Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex

We studied the activity of single neurons in the frontal eye fields of awake macaque monkeys trained to perform several oculomotor tasks. Fifty-four percent of neurons discharged before visually gu...

Primate frontal eye fields. I. Single neurons discharging before saccades

Previous studies have reported that some neurons in the inferior temporal (IT) cortex respond selectively to highly specific complex objects. In the present study, we conducted the first systematic survey of the responses of IT neurons to both simple stimuli, such as edges and bars, and highly complex stimuli, such as models of flowers, snakes, hands, and faces. If a neuron responded to any of these stimuli, we attempted to isolate the critical stimulus features underlying the response. We found that many of the responsive neurons responded well to virtually every stimulus tested. The remaining, stimulus-selective cells were often selective along the dimensions of shape, color, or texture of a stimulus, and this selectivity was maintained throughout a large receptive field. Although most IT neurons do not appear to be “detectors” for complex objects, we did find a separate population of cells that responded selectively to faces. The responses of these cells were dependent on the configuration of specific face features, and their selectivity was maintained over changes in stimulus size and position. A particularly high incidence of such cells was found deep in the superior temporal sulcus. These results indicate that there may be specialized mechanisms for the analysis of faces in IT cortex.

https://www.jneurosci.org/content/jneuro/4/8/2051.full.pdf

Stimulus-selective properties of inferior temporal neurons in the macaque

1. We recorded from single neurons in the dorsal bank and fundus of the anterior portion of the superior temporal sulcus, an area we term the superior temporal polysensory area (STP). Five macaques were studied under anesthesia ( N20) and immobilization in repeated recording sessions. 2. Almost all of the neurons were visually responsive, and over half responded to more than one sensory modality; 21% responded to visual and auditory stimuli, 17% responded to visual and somesthetic stimuli, 17% were trimodal, and 41% were exclusively visual. 3. Almost all the visual receptive fields extended into both visual half-fields, and the majority approached the size of the visual field of the monkey, including both monocular crescents. Somesthetic receptive fields were also bilateral and usually included most of the body surface. 4. Virtually all neurons responded better to moving visual stimuli than to stationary visual stimuli, and almost half were sensitive to the direction of movement. Several classes of directional neurons were found, including a) neurons selective for a single direction of movement throughout their receptive field, b) neurons selective for directions of movement radially symmetric about the center of gaze, and c) neurons selective for movement in depth. 5. The majority of neurons (70%) had little or no preference for stimulus size, shape, orientation, or contrast. The minority (30%) responded best to particular stimuli. Some of these appeared to be selective for faces. 6. The properties of most STP neurons, such as large receptive fields, sensitivity to movement, insensitivity to form, and polymodal responsiveness, suggest that STP is more involved in orientation and spatial functions than in pattern recognition.

Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque.

We studied single neurons in the frontal eye fields of awake macaque monkeys and compared their activity with the saccadic eye movements elicited by microstimulation at the sites of these neurons. ...

Charles J. Bruce

Papers

Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex

Primate frontal eye fields. I. Single neurons discharging before saccades

Stimulus-selective properties of inferior temporal neurons in the macaque

Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque.

Primate frontal eye fields. II. Physiological and anatomical correlates of electrically evoked eye movements.