Talk on the four generations of single-neuron models: From the perceptron to the complex adaptive system

Talk on The four generations of single-neuron models: From the perceptron to the complex adaptive system

by Professor Rishikesh Narayanan, Molecular Biophysics Unit, Indian Institute of Science, Bengaluru 560012

Venue: Multimedia Classroom (MMCR), EE Department, IISc

Date & Time: February 17, 2025, 3 PM (Coffee will be served at 2.45 PM)

The key objective of this talk is to foster interdisciplinary AI research by way of understanding the recent advances in Neuroscience and leveraging them for building superior AI models that are closer to natural intelligence.

The first generation of single-neuron models treated neurons as perceptrons or integrate-and-fire devices, involving some form of summation that was followed by a nonlinearity. This class of models originated in the early 1900s with the law of dynamic polarization laying the conceptual foundation. The 1950s introduced the second generation of models with Hodgkin and Huxley’s ground-breaking use of ordinary differential equations to describe action potential dynamics. This second era emphasized the nonlinear dynamical systems framework to capture ionic interactions underlying neuronal functions. The third era, beginning in the early 1990s, incorporated spatial complexity into single-neuron models by acknowledging dendrites as active participants in neural computation. Patch-clamp electrophysiology facilitated discoveries of active conductances in dendrites, leading to models based on coupled partial differential equations spanning entire dendritic structures. By the early 2000s, variability among neurons of the same subtype highlighted the need for models beyond a single archetype. This ushered in the fourth generation of models, where single neurons are recognized as complex adaptive systems. Complex systems are systems where several functionally specialized subsystems interact to yield collective functional outcomes, and are defined by two key attributes. First, the interactions among subsystems of a complex system are neither fully determined nor completely random. This intermediate level of randomness is characterized by network motifs — subnetworks that appear more frequently than expected in random networks. The second defining feature of complex systems is degeneracy, where multiple combinations of distinct subsystems can achieve the same collective function. The complex systems framework unifies earlier models, highlighting dynamic and adaptive interactions among specialized subsystems to explain collective neuronal function.

About Rishi: Rishi earned his Ph.D. from the Department of Electrical Engineering at the Indian Institute of Science, Bangalore (Advisor: Prof. Y. V. Venkatesh). After that, he held two postdoctoral positions, the first at the National Centre for Biological Sciences, Bangalore (Advisor: Prof. Sumantra Chattarji), and the second at the University of Texas at Austin (Advisor: Prof. Daniel Johnston). He returned to the Institute in July 2009. He is currently a Professor at the Molecular Biophysics Unit of the Institute.

Hosts: Chandra Sekhar Seelamantula (EE) & Chiranjib Bhattacharyya (CSA)