Neurons and Their Connections

1. Cognitive Architectures Neurons and Their Connections Based on book Cognition, Brain and Consciousness ed. Bernard J. Baars Janusz A. Starzyk

2. Introduction Neurons did not change much for millions of years The brain can be viewed as a hyper complex surface of neurons. Sensory and motor cortex are viewed as processing hierarchies of neurons.

3. Introduction A bipolar neuron A single neuron may have thousands of inputs (dendrites) and one or more outputs (axons). Neurons grow extending their axons and connecting to other neurons in the interconnected structure

4. Neurons’ Growth This growth can be observed in the lab and under stimuli the network can learn a control function

5. Real and idealized neurons Neurons have been idealized into the classical integrate and fire neuron (right). In this neuron inputs from dendrites are accumulated and if total voltage value exceeds -50 mV it triggers fast traveling action potential in the cell’s axon. Neuron sends its signal by firing spikes from the cell body to terminals synapses.

6. Excitation and Inhibition Classical neurons are connected by excitatory and inhibitory synapses. There are many classes of neurons, neurochemicals, and mechanisms for information processing Many factors determine neuron activity – the sleep-waking cycle, availability of chemicals, and more.

7. Excitation and Inhibition (cont.) Transmission of signals through axons is assisted by wrapping the axons in Myelinating Schwann cells. The cells improve the conduction velocity of signals. At the breaks known as the nodes of Ranvier, the action potentials are regenerated.

8. A Synapse A spike in the presynaptic cell triggers release of neurotransmitter that diffuses across the synaptic gap and changes potential of postsynaptic cell. Efficiency of signal transmission corresponds to synaptic weigh in network of neurons

9. Working Assumptions Neurons add graded voltage inputs until total membrane voltage exceeds -50 mV and then fires. Connections are either excitatory or inhibitory and its strengths is represented by the connection weight. The weight can be normalized between -1 and 1. Artificial neural networks that use simple neuron models can be used for pattern recognition or unknown function approximation. Neurons can form one-way or bidirectional pathways to transfer information from one part of the brain to other. Cortex is a massive 6-layer array of neurons. Arrays of neurons are called maps. Stable collections of neurons form Hebbian cell assemblies

10. A simple reflex circuit • An example of a spinal (knee-jerk) reflex. • Sensory neurons pick up the tap and transmit it to the spinal cord. • An interneuron links the sensory impulses to motor neurons • bypassing higher level brain function and making the leg jump

11. A simple reflex circuit While reflex circuits can be triggered by outside stimuli, they are integrated into voluntary, goal driven activities. Many times this is unconscious and almost automatic. Voluntary goal driven brain mechanisms, are associated with cortex. Sophisticated subcortical activity is also engaged in planning and executing actions. Spinal centers communicate with higher centers while carrying sensorimotor reflexes and return feedback signals to brain.

12. There are several types of receptors, however, they are all similar in structure and function. Sensory nerves have parallel pathways sending sensory information to thalamus and sending back feedback information 90% of neurons go backwards towards the source Most sensory and motor pathways split and cross over the midline of the body Different types of receptors 12

13. Similarities between sensor pathways This image shows the similarities between the different sensory streams. arm vs. leg, high frequency vs. low frequency, and foveal vs. peripheral vision.

14. Sensory Interactions Sensory regions interact with thalamic nuclei (RTN) Notice similarities between cortical input and output layers in all these senses 14

15. Lateral inhibition is used to differentiate between neighboring cells This gives better resolution at various levels of sensory perception In retina it helps to spot a tiny point At higher level it helps to differentiate e.g. between ‘astronomy’ and ‘astrology’ Lateral Interactions 15

16. Visual demonstration of lateral inhibition Notice that lateral inhibition applies to adjacent black squares, color perception, and even perception of direction Lateral Interactions 16

17. Mapping of the brain Visual quadrants map to cortical quadrants Mapping is observed for various senses

18. Neuron organization Neurons organize into layers. The figure above shows a single layer of pyramid neurons at 200 micrometers.

19. Visual Maps Neuron connections form various pathways In V1 the upper pathway is sensitive to location ‘where The lower pathway is sensitive to color, shape contrast and object identity ‘what’

20. Layers have 2-way connections Neuronal layers have both feed-forward and feedback connections between layers/arrays. Lower levels tend to be sensitive to simpler stimuli, while higher levels respond to more complex stimuli.

21. Sensory and motor hierarchies Sensory and motor systems appear to be arranged in hierarchies with information flowing between each level of the sensory and motor hierarchies.

22. Ambiguous stimuli Ambiguous stimuli pose choices for interpretation. It all depends on how the image is perceived and what ever preconceived notions you may have.

23. Hebbian Learning • “Neurons that fire together, wire together” • Long term potentiation (LTP) and long term depression (LTD) • The figure depicts Hebbian learning in cell assemblies. • At t1 input is encoded into connection weights. • Memory is retained at times t2 & t3.

24. A Three Layer Network Hidden layer makes the network more flexible Backpropagation is used to adjust network weights to match the input to a desired output.

25. A pattern recognition network An example of an auto-associative network that matches its output with its input.

26. A self-organizing network Self-organizing networks appear often in biological organisms. A self-organizing network can be used for face recognition.

27. Neural Darwinism Gerald Edelman proposed that brain is a massive selectionist organ where neurons develop and make connections following Darwinian principle of selection of the fittest. In biological evolution, species adapt by reproduction, mutation that leads to diverse forms, and selection. A similar process occurs in the immune system, where millions of immune cells adapt to invading toxins. Thus selectionism leads to flexible adaptation.

28. Symbolic Processing Neural nets can handle both distributed numerical values as well as symbolic expressions. The figure shows proposed by McClelland and Rogers merge between symbolic features and their associations expressed by connections of a neural network Brain uses adaptation and representation to learn the world.

29. Deep neural nets for image processing • The architecture of LeNet5 (Yann LeCun)

30. The brute force approach • LeNet uses knowledge about the invariances to design: • the local connectivity • the weight-sharing • the pooling. • This achieves about 80 errors on MNIST data base. • This can be reduced to about 40 errors by using many different transformations of the input and other tricks (Ranzato 2008) • Ciresan et. al. (2010) inject knowledge of invariances by creating a huge amount of carefully designed extra training data: • For each training image, they produce many new training examples by applying many different transformations. • They can then train a large, deep, dumb net on a GPU without much overfitting. • They achieve about 35 errors.

31. The errors made by the Ciresan et. al. net The top printed digit is the right answer. The bottom two printed digits are the network’s best two guesses. The right answer is almost always in the top 2 guesses. With model averaging they can now get about 25 errors.

32. Multiple layers make sense Your brain works that way

33. Milestones: Digit Recognition LeNet 1989: recognize zip codes, Yann Lecun, Bernhard Boser and others, ran live in US postal service

34. Image Classification Convolutional NNs: AlexNet (2012): trained on 200 GB of ImageNet Data Human performance5.1% error

35. Deep Reinforcement Learning In 2013, Deep Mind’s arcade player bests human expert on six Atari Games. In 2016, Deep Mind’s alphaGodefeated former Goworld champion Lee Sedol. Lee walked out of the game Claiming that no human could make such winning move as alphaGo made.

36. Coordinating Neural Nets • A high density of gamma rhythms has been related to conscious visual perception and understanding of spoken words. • Alpha rhythms are associated with an absence of focused attentional tasks. • Theta rhythms coordinate hippocampal region and the frontal cortex during retrieval of memories. • And delta rhythms signal deep sleep, are believed to group fast neuronal activities to consolidate learned events. Neurons’ activation is coordinated by large-scale rhythms to signify their activities. Epileptic seizures are also caused by slow, intense, regular waves that lead to a loss of consciousness Thus there must be a balance between integration and differentiation.

37. Coordinating Neural Nets • This figure illustrates hypothesis how brain rhythms coordinate large number of neuron cells’ firing. • Neurons that fire in synch with the dominant rhythm are strengthened by feedback from many other neurons, while those that fire out of synch are weakened.

38. Summary • The basic question in cognitive neuroscience is how the nerve cells work together to perform cognitive functions like perception, memory and action. • Models of neurons were developed and used to build functional processing networks. • Artificial neural networks and biologically inspired networks are useful to study cognitive processing. • Sensory and motor systems are complex hierarchies of neurons organized in two or three dimensional arrays. • In vision, touch and motor control, arrays of neurons are topographically arranged as maps of the spatial surroundings. • Hierarchies are bidirectional pathways, that allow signals to travel up, down and laterally. • A major function of downwards pathway is to resolve sensory ambiguities. • Lateral inhibition is used to emphasize differences between inputs.