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Network Models (2)

LECTURE 7. Network Models (2). Introduction − Basic concepts of neural networks Realistic neural networks − Homogeneous excitatory and inhibitory populations − The olfactory bulb − Persistent neural activity − Perceptual decision

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Network Models (2)

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  1. LECTURE 7 Network Models (2)

  2. Introduction • − Basic concepts of neural networks • Realistic neural networks • − Homogeneous excitatory and • inhibitory populations • − The olfactory bulb • − Persistent neural activity • −Perceptual decision • −Discrete versus continuous • attractor

  3. Random-dot motion direction discrimination experiment

  4. The decision process occursand guides saccades Dorsal stream (Where pathway ): V1  V2  MT / MST  posterior parietal cortex (LIP )  …… Encoding the motion stimulus (Shadlen and Newsome 2001)

  5. Network model A or B group: 0.15NE with NE=1600 and NI=400 Below threshold, W =1.7 (in A or B) = 0.88 (excitatory weights) = 1.0 (others) (Wang 2002, Neuron 36: 955)

  6. Poisson input • All neurons receive a large amount of background Poisson inputs and fire spontaneously at a few hertz • During stimulation, both neural groups receive stochastic Poisson inputs at rates µAand µB

  7. Model reproduces salient characteristics of decision-correlated neural activity in LIP Input presentation (1 s) is followed by a delay period (2 s) (Wang 2002, Neuron 36: 955)

  8. (Wang 2002, Neuron 36: 955) Decision dynamics with inputs of zero coherence

  9. Optimal decision-making performance requires sufficiently strong and slow synaptic reverberations When W =1.7 (in A or B) 1.4, attractor dynamics can no longer be sustained by intrinsic network excitation. (Wang 2002, Neuron 36: 955)

  10. − With stronger excitatory reverberations, persistent activity level is doubled (from 20 Hz in control to 40 Hz), and the integration time of stimulus is shortened by a half −If there are only fast AMPA receptors ( ) at recurrent synapses, the network can still show attractor dynamics. However, the network cannot integrate stimuli for more than tens of milliseconds, and it “latches onto” oneof the two attractors immediately after the stimulus onset

  11. Introduction • − Basic concepts of neural networks • Realistic neural networks • − Homogeneous excitatory and • inhibitory populations • − The olfactory bulb • − Persistent neural activity • −Perceptual decision • −Discrete versus continuous • attractor

  12. Kinds of attractors in network models: • fixed points, line (or ring) attractors,limit cycles,and chaos • 2. The network for persistent neural activityhas a continuous (vs. discrete) line attractor embedded in the phase space.Continuous attractor neural networks are emerging as promising models for describing the encoding of continuous stimuli in neural systems

  13. A line attractor A point attractor A cyclic attractor A chaotic attractor

  14. Discrete versus continuous attractor This neutral stability allows the system to change status smoothly, following a fixed path. This property (not shared by discrete attractors) is crucial for the system to seamlessly track the smooth change of stimulus

  15. Robustness of line attractors to noise (Brody et al. 2003) L: Imagine that there is a function of the state of the system and that the dynamics of the system are such that L always tends to fall in value as time progresses The neutral stability implies the system is sensitive to fluctuations along the attractor space

  16. Cellular bistability at single neuron level as a candidate mechanism for robust working memory performance In order to perform a working memory, the PFC network should be able to be “switched on” into a persistent firing pattern by transientcue stimuli and be “turned off” back to its resting state of spontaneous activity by a suitable “go” signal f 100 Hz Physiological Range I

  17. Cellular bistability (or multistable states) Two stable membrane states correspond to different activation conditions of various voltage-dependent ionic currents. Typically, neuromodulatory inhibition of certain potassium currents (and/or modulation of calcium and other currents) is required to unmask the plateau potential and enable the bistability behavior. (Activation function of medium spiny neurons (MSNs) in the striatum of the BG)

  18. Homework • 1. Develop a neural network including the minimalbiophysical mechanisms necessary to reproduce persistent activity in neural systems. And how about the stability of network attractors? • The possible computational mechanism underlying perceptual choice • The differences in dynamics between feedforward and recurrent neural networks

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