Introduction

An introduction is provided to neurons; computation by biologically plausible networks of neurons; the representation of information in the brain; the functions of different brain areas; and the structure and connectivity of the cerebral neocortex.

Cerebellar cortex

The cerebellar cortex appears to be involved in predictive feedforward control to generate smooth movements. There is a beautiful network architecture which suggests that the granule cells perform expansion recoding of the inputs; that these connect to the Purkinje cells via an architecture that ensures regular sampling; and that each Purkinje cell has a single teacher, the climbing fibre, which produces associative long-term synaptic depression as part of perceptron-like learning.

Computations by different types of brain, and by artificial neural systems

In this Chapter a comparison is made between computations in the brain and computations performed in computers. This is intended to be helpful to those engineers, computer scientists, AI specialists et al interested in designing new computers that emulate aspects of brain function. In fact, the whole of this book is intended to be useful for this aim, by setting out what is computed by different brain systems, and what we know about how it is computed. It is essential to know this if an emulation of brain function is to be performed, and this is important to enable this group of scientists to bring their expertise to help understand brain function more. The Chapter also considers the levels of investigation, which include the computational, necessary to understand brain function; and some applications of this understanding, to for example how our developing understanding is relevant to understanding disorders, including for example of food intake control leading to obesity. Finally, Section 19.10 makes it clear why the focus of this book is on computations in primate (and that very much includes human) brains, rather than on rodent (rat and mice) brains. It is because the systems-level organization of primate including human brains is quite different from that in rodents, in many fundamental ways that are described.

The ventral visual system

The brain processes involved in visual object recognition are described. Evidence is presented that what is computed are sparse distributed representations of objects that are invariant with respect to transforms including position, size, and even view in the ventral stream towards the inferior temporal visual cortex. Then biologically plausible unsupervised learning mechanisms that can perform this computation are described that use a synaptic modification rule what utilises a memory trace. These are compared with deep learning and other machine learning approaches that require supervision.

The temporal cortex

The inferior and middle temporal gyri are involved visual object recognition, with the more dorsal areas involved in face expression, gesture, and motion representation that is useful in social behaviour. The superior temporal cortex is involved in auditory processing. The anterior temporal lobe is involved in semantic representations, for example information about objects, people, and places. Network mechanisms involved in semantic representations are described. The output of this system reaches the inferior frontal gyrus, which on the left is Broca’s area, involved in language production. The concept that the semantics for language are computed in the anterior temporal lobe, and communicates with Broca’s area for speech production, is introduced.

The hippocampus, memory, and spatial function

The hippocampal system provides a beautiful example of how different classes of neuronal network in the brain work together as a system to implement episodic memory, the memory for particular recent events. The hippocampus contains spatial view neurons in primates including humans, which provide a representation of locations in viewed space. These representations can be combined with object and temporal representations to provide an episodic memory about what happened where and when. A key part of the system is the CA3 system with its recurrent collateral connections that provide a single attractor network for these associations to be learned. The computational generation of time, encoded by time cells in the hippocampus, is described, and this leads to a theory of hippocampal replay and reverse replay. The computational operation of a key part of the architecture, the recall of memories to the neocortex, is described.

The parietal cortex, spatial functions, and navigation

The parietal areas that are involved in the dorsal visual stream are described in Chapter 3. This Chapter builds on that, and considers the functions of spatial representations in the parietal cortex and areas to which it projects the retrosplenial and posterior cingulate cortex, which in turn project to the hippocampus, in navigation. It is hypothesized that human navigation is likely to often depend on spatial view neurons, which with a list of landmarks provides a common method of navigation. This may be complemented by the use of allocentric bearing to a landmark cells, which provide a basis for navigation that is not based on approach to landmarks, but instead on bearings to landmarks. Models for both types of navigation are provided with Matlab code. Idiothetic (self-motion) update of hippocampal representations is likely to be performed by the operations of the coordinate transform systems in the dorsal visual system described in Chapter 3, which provides inputs to the hippocampus.

The motor cortical areas

Premotor cortical areas have outputs to the motor cortex, and receive inputs from the parietal cortex to implement actions such as reaching into space and grasping objects. Neurons in some premotor areas respond not only to movements being performed, but also to the sight of movements being performed, and are termed ‘mirror neurons’.

Cortical attractor dynamics and connectivity, stochasticity, psychiatric disorders, and aging

In this chapter we consider how the operation of attractor networks in the brain is influenced by noise in the brain produced by the random firing times of neurons for a given mean firing rate; how this can in fact be beneficial to the operation of the brain; and how the stability of these systems and how they are influenced by noise in the brain is relevant to understanding a number of mental disorders. The concept of noise in attractor networks is important to understanding decision-making, short-term memory, and depression and schizophrenia, and this is described in this Chapter. It is a key aim of this book to increase understanding of the brain that is relevant not only to its operation in health, but also in disease, and how it may be possible to ameliorate some of the effects found in these mental and other disorders.

The taste and flavour system

Information is represented in taste regions up to and including the insular primary taste system of what the taste is independent of its reward value and pleasantness with a sparse distributed representation of sweet, salt, bitter, sour and umami inputs. The texture of food in the mouth, including fat texture, is also represented in these areas. The insular taste cortex then projects to the orbitofrontal cortex, in which the reward value and pleasantness of the taste and flavour are represented, with olfactory components included.